ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2016

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology 1422

Thin films for indoor airmonitoring

Measurements of Volatile Organic Compounds

UMUT CINDEMIR

ISSN 1651-6214ISBN 978-91-554-9683-8urn:nbn:se:uu:diva-302558

Dissertation presented at Uppsala University to be publicly examined in Room Å2001,Ångströmlaboratoriet, Lägerhyddsv 1, Friday, 21 October 2016 at 13:15 for the degree ofDoctor of Philosophy. The examination will be conducted in English. Faculty examiner:Professor emeritus Magnus Willander (Linköping University).

AbstractCindemir, U. 2016. Thin films for indoor air monitoring. Measurements of VolatileOrganic Compounds. Digital Comprehensive Summaries of Uppsala Dissertations from theFaculty of Science and Technology 1422. 78 pp. Uppsala: Acta Universitatis Upsaliensis.ISBN 978-91-554-9683-8.

Volatile organic compounds (VOCs) in the indoor air have adverse effects on the dwellersresiding in a building or a vehicle. One of these effects is called sick building syndrome (SBS).SBS refers to situations in which the users of a building develop acute health effects anddiscomfort depending on the time they spend inside some buildings without having any specificillness. Furthermore, monitoring volatile organic compounds could lead to early diagnosis ofspecific illnesses through breath analysis. Among those VOCs formaldehyde, acetaldehyde canbe listed.

In this thesis, VOC detecting thin film sensors have been investigated. Such sensors havebeen manufactured using semiconducting metal oxides, ligand activated gold nanoparticles andGraphene/TiO2 mixtures. Advanced gas deposition unit, have been used to produce NiO thinfilms and Au nanoparticles. DC magnetron sputtering has been used to produce InSnO and VO2

thin film sensors. Graphene/TiO2 sensors have been manufactured using doctor-blading.While presenting the results, first, material characterization details are presented for each

sensor, then, gas sensing results are presented. Morphologies, crystalline structures and chemicalproperties have been analyzed using scanning electron microscopy, X-ray diffraction and X-ray photo electron spectroscopy. Furthermore, more detailed analyses have been performedon NiO samples using extended X-ray absorption fine structure method and N2 adsorptionmeasurements. Gas sensing measurements were focused on monitoring formaldehyde andacetaldehyde. However, responses ethanol and methane were measured in some cases to monitorselectivity. Graphene/TiO2 samples were used to monitor NO2 and NH3. For NiO thin filmsensors and Au nano particles, fluctuation enhanced gas sensing is also presented in additionto conductometric measurements.

Keywords: gas sensor, thin film, adcanced gas depostion, sputter deposition, nickel oxide,gold nanoparticles, indium tin oxide, acetaldehyde, formaldehyde

Umut Cindemir, Department of Engineering Sciences, Solid State Physics, Box 534, UppsalaUniversity, SE-751 21 Uppsala, Sweden.

© Umut Cindemir 2016

ISSN 1651-6214ISBN 978-91-554-9683-8urn:nbn:se:uu:diva-302558 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-302558)

Our true mentor in life is science. (Hayatta en hakiki mürşit ilimdir.) Mustafa Kemal Atatürk

to Ulaş and Buse

List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

I Cindemir, U., Topalian, Z., Österlund, L., Niklasson, G.A.,

Granqvist, C.G. (2014) Porous Nickel Oxide Film Sensor for Formaldehyde. J. Phys. Conf. Ser. 559 p:012012. doi:10.1088/1742-6596/559/1/012012.

II Cindemir, U., Österlund, L., Niklasson, G.A., Granqvist, C.G., Trawka, M., Smulko, J. (2015) Nickel oxide thin film sensor for fluctuation-enhanced gas sensing of formaldehyde, 2015 IEEE Sensors, IEEE, Busan 2015: pp. 1–4. doi:10.1109/ICSENS.2015.7370408.

III Cindemir, U., Topalian, Z., Granqvist, C.G., Österlund, L., Ni-klasson, G.A., Characterization of porous Nickel Oxide Films produced with Advanced Reactive Gas Deposition, in manu-script.

IV Cindemir, U., Trawka, M., Smulko, J., Granqvist, C.G., Öster-lund, L., Niklasson, G.A., Fluctuation-enhanced and conducto-metric gas sensing with nanocrystalline NiO thin films: A com-parison, submitted to Sensors & Actuators: B. Chemical

V Cindemir, U., Lansåker, P., Österlund, L., Niklasson, G.A, Granqvist, C.G. (2016) Sputter-Deposited Indium–Tin Oxide Thin Films for Acetaldehyde Gas Sensing, Coatings. 6:19. doi: 10.3390/coatings6020019.

VI Ionescu, R., Cindemir, U., Welearegay, T.G., Calavia, R., Had-di, Z., Topalian, Z., Granqvist, C.G., Llobet, E. (2016) Fabrica-tion of ultra-pure gold nanoparticles capped with dodecanethiol for Schottky-diode chemical gas sensing devices. Sensors & Ac-tuators: B. Chemical 239, 455-461. doi: 10.1016/j.snb.2016.07.182

VII Lentka, Ł., Kotarski, M., Smulko, J., Cindemir, U., Topalian, Z., Granqvist, C. G., Calavia, R., Ionescu, R. (2016) Fluctua-tion-Enhanced Sensing with Organically Functionalized Gold Nanoparticle Gas Sensors Targeting Biomedical Applications. Talanta 160, 9–14. doi:10.1016/j.talanta.2016.06.063.

VIII Smulko, J., Trawka, M., Cindemir, U., Granqvist, C. G., Durán, C. (2016) Resistive gas sensors – Perspectives on selectivity and sensitivity improvement. submitted to NANOfIM 2016 (peer reviewed)

Reprints were made with permission from the respective publishers.

My contributions to the appended papers

I Sample preparation and material characterization, gas sensing experiments and most of the writing

II Sample preparation and material characterization, gas sensing experiments and most of the writing

III Sample preparation, material characterization and most of the writing

IV Sample preparation and characterization, gas sensing experi-ments and most of the writing

V Some parts of material characterization, all of gas sensing ex-periments and most of the writing

VI Some parts of sample preparation, part of material characteri-zation and part of the writing

VII Some parts of sample preparation, part of material characteri-zation and part of the writing

VIII Some parts of sample preparation, all of material characteriza-tion and part of the writing

Papers not included in the thesis

I Sarioglu, B., Tumer, M., Cindemir, U., Camli, B., Dundar, G., Ozturk, C., Yalcinkaya, A. D. (2015). An optically powered CMOS tracking system for 3 T magnetic resonance environ-ment. IEEE transactions on biomedical circuits and systems, 9(1), 12-20. doi: 10.1109/TBCAS.2014.2311474

Contents

1. Introduction ............................................................................................... 11

2. Gas Sensors ............................................................................................... 13 2.1. Metal oxide gas sensors ..................................................................... 14 2.2. Gold nanoparticles with thiol ligands ................................................ 19 2.3. Graphene based gas sensors .............................................................. 20

3. Material Preparation and Characterization Techniques ............................ 22 3.1. Film preparation ................................................................................ 22

3.1.1. Advanced Gas Deposition (AGD) ............................................. 22 3.1.2. Reactive DC Magnetron Sputtering ........................................... 24 3.1.3. Doctor – Blading Method .......................................................... 26 3.1.4. Functionalization of Nanoparticles ............................................ 27 3.1.5. Heat Treatment .......................................................................... 28

3.2. Material Characterization Techniques ............................................... 28 3.2.1. Thickness Measurements ........................................................... 28 3.2.2. X-ray Photoelectron Spectroscopy (XPS) ................................. 29 3.2.3 X-ray Diffraction (XRD) ............................................................ 30 3.2.4 Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) .................................................................. 32 3.2.5 Extended X-ray Absorption Fine Structure (EXAFS) ................ 33 3.2.6 Absorption and Desorption Measurements ................................. 37

3.3 Gas Sensing Measurements ................................................................ 38 3.3.1 Resistance measurements ........................................................... 38 3.3.2 Noise measurements ................................................................... 39

4. Results and Discussion ............................................................................. 41 4.1 NiO Sensors ........................................................................................ 41

4.1.1 Material Properties ...................................................................... 41 4.1.2 Gas Sensing Results .................................................................... 45

4.2 InSnO sensors ..................................................................................... 49 4.2.1 Material Properties ...................................................................... 49 4.2.2 Gas Sensing Results .................................................................... 50

4.3 Au NP sensors .................................................................................... 52 4.3.1 Material Properties ...................................................................... 52 4.3.2 Gas Sensing Results .................................................................... 55

4.4 VO2 Sensors ....................................................................................... 59 4.4.1 Material Properties ...................................................................... 59 4.4.2 Gas Sensing Results .................................................................... 60

4.5 TiO2/Graphene Sensors ...................................................................... 62 4.5.1 Material Properties ...................................................................... 62 4.5.2 Gas Sensing Results .................................................................... 63

5. Summary and Conclusions ....................................................................... 66

6. Swedish Summary .................................................................................... 68

Acknowledgements ....................................................................................... 70

References ..................................................................................................... 72

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1. Introduction

People in the industrialized countries spend as much as 80% to 90% of their time inside buildings or vehicles[1]. There has been an increase in public concern about the adverse effects of indoor air quality since the 1970s, with reports from occupants of residences and commercial and industrial build-ings having problems associated with buildings they reside in [2]. Mostly reported complaints from dwellers are eye and upper respiratory tract irrita-tion, headache, fatigue and lethargy, and breathing difficulties or asthma [3]. As a result, it is obvious that the indoor air quality is important for well-being and health as well as productivity [4,5]. For the cases where the air quality is not good enough, the term “sick building syndrome” (SBS) refers to situations in which the users of a building develop acute health problems and discomfort, depending on the time they spend inside some buildings, without having any specific illness[6]. The major causes of SBS are listed as follows: Inadequate ventilation can result in SBS due to lack of oxygen. This

suggests that efforts to improve energy efficiency by decreasing ventila-tion may result in worse indoor air quality and can cause health prob-lems.

Biological contaminants, which covers bacteria, pollen, moulds and viruses, can also cause SBS and reduce the indoor air quality with the risk of further diseases. Biological contaminants generally arise from the stagnant water in drains, humidifiers or through water leaks.

Chemical contaminants are one of the major causes for SBS and they can arise from indoor sources such as furniture, construction materials, painting and cleaning agents. All the aforementioned sources can emit agents known as “volatile organic compounds” (VOCs), such as formal-dehyde and acetaldehyde. Some of the VOCs are also known carcino-gens. In addition to the mentioned sources of VOCs, tobacco smoke produces high amounts. Furthermore, stoves and fire places can give rise to amounts of combustion products like nitrogen dioxide and carbon monoxide.

Among the mentioned causes of SBS one of them can be present by itself or they can exist in combination, thus worsening the effects. In addition, around 30% of new and renovated buildings are subjected to complaints regarding lack of indoor air quality [7]. Poor indoor air quality has its im-pacts on pupils, resulting in reduced school attendance and decreased per-

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formance [3]. Furthermore, causality between asthma and similar respiratory symptoms from some VOCs, such as formaldehyde, is documented [8]. Some of the VOCs, among which aldehydes are prominent, are given as follows: Formaldehyde is a colorless gas at ambient temperature with a suffocat-

ing odor which has irritating effects on eyes and skin. Some major sources of formaldehyde are exhaust from incomplete burning of com-bustion fuels, tobacco smoke, plywood furniture, insulation materials, gas fires and stoves and sterilizing agents. Formaldehyde is reported as a source of asthma [8], bronchial hyper responsiveness [9], increased pul-monary function variability and decreased pulmonary function [10] as well as atopy [11].

Acetaldehyde is another gas belonging to the aldehyde group, having a rotten fruity smell which has irritating effects on eyes and skin. The hu-man perception limit of acetaldehyde is as low as 70 ppb [12]. Potential sources of acetaldehyde can be listed as various combustion processes, such as burning of wood, wastes, fossil fuels and tobacco [13]. Further-more, acetaldehyde can be emitted by polymeric building materials and emulsion paints[12] and it can be intermediate in the respiration of plants [14,15].The threshold limit value for adverse health effects is 25 ppm where the maximum allowed workplace concentration is 50 ppm [12]. Acetaldehyde concentrations above 50 ppm are extremely irritating and possibly carcinogenic [14].

Ethanol is also considered as a VOC due its low boiling point. It is also referred to alcohol spirit, spirit of wine or grain alcohol, due its use in al-coholic beverages. It can be a product of fermentation of sugar by yeast or can be produced by other means such as hydration of ethylene [16]. It can be used for medical purposes due to its antiseptic properties and in cosmetic products as a solvent. It is also used as an engine fuel. Gas phase ethanol has irritating effects on the eye [17] but not on the skin [18].

It is obviously important to detect VOCs in order to monitor indoor air quality. Furthermore, some of the VOCs can be markers of diseases, which increases the importance of monitoring them [19]. Thus, these tasks require good sensors which are efficient and inexpensive to manufacture. In the next section, a survey of gas sensors is presented with the purpose of monitoring VOCs.

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2. Gas Sensors

The dictionary meaning of the word sensor is given roughly as “a device which is used to record the presence of something or changes in something” [20]. Even though this definition seems enough to define the concept, a sen-sor can also be defined as a device or a system which converts the existence or changes in existence of a stimulus (heat, pressure, gas, light etc.) into a form of energy or a signal that can be decoded by an end-reader or user.

Gas sensors are made for detecting the existence and changes in the amount of gas phase materials. There has been a great interest in gas sensor research with large demands on gas sensor applications where there is a need for air quality and safety improvements. One of the oldest examples of a gas sensor is the use of canaries in mines since they are very sensitive to carbon monoxide. However, this method is not reproducible since the response is the death of the bird. More recent examples of gas sensors can be found in industrial applications where hazardous or/and flammable gases are moni-tored in order to ensure the safety of employees at an early stage. Lastly, a daily example for the use of gas sensors is smoke detectors in buildings, which are also compulsory to have in houses and office buildings. Further-more, for an ideal gas sensor there are further criteria such as selectivity, reproducibility, sensitivity and fast detection.

Figure 1: Important parameters for gas sensors which depend on application.

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Some of the important parameters for gas sensors are shown in figure 1. A desired gas sensor is one that gives a fast response to concentration values below which any hazardous effect occur. Secondly, it should be sensitive to the changes of the concentration of the detected gas. Thirdly, it should be as selective as possible. A selective gas sensor gives high response to a target gas where other agents have little effect on the response. Repeatability is also a key issue, so that similar responses are obtained from similar inputs, i.e. gas concentration level, temperature etc. The operating temperature plays an important role for the energy consumption of the device. Detection mech-anisms can be altered depending on the application and the read out part of the system. For example, smoke detectors use the ionization of small parti-cles, whereas a CO2 detector can be designed by using the optical properties such as absorption of infrared light at a specific wavelength. Lastly, the manufacturing method is a key parameter for the gas sensor, not only that it should be cheap to produce but also it should allow structural engineering of the sensor.

2.1. Metal oxide gas sensors Metal oxides have been used to monitor VOCs for more than 50 years [21] due to their semiconducting properties. Semiconducting metal oxides (SMOXs) have been attractive for gas sensing applications since they are cheap, flexible to apply to different manufacturing methods and easy to use [22]. A simple SMOX gas sensor is formed of a polycrystalline metal oxide layer/film connected by two metal electrodes.

One historic and popular example of an SMOX gas sensor is the Taguchi gas sensor [21]. Taguchi manufactured and patented the first chemoresistive metal oxide gas sensor in the beginning of the 1970s. He used tin dioxide (SnO2) as the sensing material in his sensor after trying other metal oxides such as zinc oxide (ZnO). The advantages of SnO2 over other metal oxides were higher sensitivity, a lower operating temperature and thermal stability. To produce the sensing SMOX layer the process is as follows: a mixture of tin chloride (SnCl4) and stearic acid (1g: 8g) is mixed and painted over the ceramic substrate and fired/baked in 700 °C in air. The baking process burns and evaporates the organics and leaves a porous film of SnO2. The sensor operation is also explained as follows: the sensor element is heated to some extent in the oxygen-containing environment. After heating, the oxygen ad-sorption and desorption goes into equilibrium and sensor resistance is stabi-lized. During the operation the output is the two point resistance value of the sensor element. In the presence of a sample gas (VOCs for example) mole-cules are adsorbed on the sensor surface. The gas molecules adsorbed on the sensor surface react with the oxygen species which were already adsorbed on the sensor surface. As a result of these surface chemical reactions, new reac-

15

tion products emerge, with an exchange of electrons to the SMOX sensing layer. After the electrons emerge and remain in the SMOX layer, they con-tribute as charge carriers and the resistance of the SMOX (SnO2) film de-creases. The gas concentration is related to the rate of change in the re-sistance. The schematic structure and a picture of a commercially available Taguchi gas sensor is shown in figure 2 [23].

Figure 2:A schematic representation of a Taguchi sensor and a picture of a sensor unit [23].

Since the development of the SnO2 sensor by Taguchi, there has been in-creasing demand for SMOX sensors with a high performance need. Other metal oxides have been extensively studied in order to make gas sensors, since metal oxides are abundant, diverse, and cheap and also their physical and chemical properties allow such functionalization. Most common metal oxides used for producing gas sensors are binary oxides. However, more complex metal oxides and doped oxides have used as well [24]. A search study by Lee et. al. [25] shows that mostly n-type metal oxides, where SnO2 is leading, have been studied. The summary of the materials and the ratio of the studies on them are given in figure 3, showing the domination of n-type SMOXs over p-type ones.

Figure 3: Studies on metal oxide gas sensors (percentage of published papers) [25].

Among metal oxides SnO2 is undoubtedly the most extensively studied ma-terial and it has been applied to many commercial devices on the market. SnO2 is a very sensitive material to gaseous species. It has a wide band gap

16

of 3.6 eV, as well as interesting electrical properties [26]. However, it does suffer from lack of selectivity to different gases, which is a major drawback for most metal oxide gas sensors. Despite its drawbacks, different synthesis and post treatment conditions, addition of dopants and other structural engi-neering methods have been applied to SnO2 sensors in order to enhance their gas sensing performance [27,28]

Zinc oxide (ZnO) is another n-type SMOX with a band gap of 3.37 eV, which has gained attention due to dominant effects arising from oxygen va-cancies [29]. ZnO has also been attractive for studies on gas sensing applica-tions not only for its chemical and physical properties, but also for its non-toxicity and low-cost [29,30]. Structural engineering methods, such as the addition of dopants, grain size and geometry control, have been applied on ZnO in order to improve its gas sensing properties [30].

Titanium dioxide (TiO2), which is mostly used for its photo catalytic properties [31], has also been used for gas sensing applications. The main advantage of TiO2 gas sensors compared to other metal oxides is that TiO2 has much lower cross-sensitivity to humidity [32]. TiO2 sensor performance can be enhanced with light due its photocatalytic properties [33].

In addition to n-type SMOXs, p-type metal oxide films have gained popu-larity in gas-sensing applications [25]. Highly sensitive p-type SMOX gas sensors can be designed by using the means of structural engineering which allows the control of the size of particles, porosity of the sensing layer/film and control the charge carrier concentration by doping[25,30]. Among p-type metal oxides nickel oxide (NiO) is an attractive material not only for its gas sensing properties, but also for its applications on catalysis [34] and elec-trochromic properties [35,36]. NiO, a structural model shown in figure 4, is a wide band gap material, in which the band gap varies between 3.6 eV and 4.0 eV. It shows substantial conductance changes as a result of surface chemical reactions. There can be several methods, such as chemical, and physical evaporation can be applied to produce NiO nanoparticles and thin films.

The working principle of metal oxide sensors relies on electrochemical reactions on the surface. SMOX sensors generally make use of ceramic, or any other heat conducting but electrically insulating substrates, in order to keep the heating power low. Then a SMOX layer is coated on the contact electrodes. The structure of a porous SMOX sensor can be as shown in fig-ure 5. In the case of a porous metal oxide thin film, the total sum of grain-to-grain band bending (eVig) becomes more dominant than the grain-electrode band bending (eVge). In the end, oxidation, or the reduction of the SMOX, ion exchange, adsorption on the surface, and surface reactions with the ad-sorbed species determine the response of the gas sensor [37].

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Figure 4: Schematic illustration of the arrangement of atoms in nickel oxide is shown with polyhedrons. Red dots show the oxygen atoms where the grey dots point the nickel atoms. NiO has NaCl structure built around a face centered cubic lattice.

Figure 5: The physical structure of an oxide based gas sensor and electronic band structures are illustrated to show grain-to-grain band bending (eVig) and grain-electrode band bending (eVge).

In the case of porous metal oxide sensors, both n-type and p-type oxides form electrical core-shell layers with the preadsorbed oxygen species. How-ever, they exhibit different conduction behavior depending on either n-type or p-type oxides. The oxygen is adsorbed on the metal oxide grains and the adsorbed species take electrons from the metal atoms within the grain. For the n-type case, adsorbed oxygen species reduce the number of electrons, which are the majority charge carriers for the n-type oxides. The reduction of the electrons results in a decrease of conduction in the shell region formed by the depletion layer. Thus, in the n-type case the shell has higher resistance

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than the core after exposure and adsorption of oxygen species. However, for the p-type case, the majority charge carriers are holes. After oxygen atoms are adsorbed, they take electrons from the metal atoms. Since the majority charge carriers are holes, holes accumulate in the outer layer and the number of charge carriers increases. As a result the core of the grain has higher re-sistance compared to a low resistance depletion layer or an accumulation layer of holes. The adsorption of oxygen on the grains is illustrated in figure 6, which shows the formation of electronic core-shell structures.

Figure 6:Core shell structure formation after oxygen adsorption for n-type and p-type metal oxide grains[25].

In the steady state conditions, at a certain operating temperature, the oxygen adsorption and desorption are at the same level. The oxygen adsorption can be described as follows[22,25,38]:

𝑂𝑂2(𝑔𝑔) → 𝑂𝑂2(𝑎𝑎𝑎𝑎𝑎𝑎) (1) 𝑒𝑒− + 𝑂𝑂2(𝑎𝑎𝑎𝑎𝑎𝑎) → 𝑂𝑂2−(𝑎𝑎𝑎𝑎𝑎𝑎) (2) 𝑒𝑒− + 𝑂𝑂2−(𝑎𝑎𝑎𝑎𝑎𝑎) → 2𝑂𝑂−(𝑎𝑎𝑎𝑎𝑎𝑎) (3)

where adsorbed oxygen takes electrons from the sensing layer. The reaction of gas to be detected with the adsorbed surface oxygen species can be sum-marized as follows:

𝑅𝑅 + 𝑂𝑂2−(𝑎𝑎𝑎𝑎𝑎𝑎) → 𝑅𝑅𝑂𝑂2(𝑔𝑔) + 𝑒𝑒− (4) 𝑅𝑅 + 𝑂𝑂−(𝑎𝑎𝑎𝑎𝑎𝑎) → 𝑅𝑅𝑂𝑂(𝑔𝑔) + 𝑒𝑒− (5)

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where R denotes the reactant gas to be detected and at the end of the reaction the electrons donated back to the SMOX sensing layer.

In this study, NiO (p-type), InSnO (n-type) semiconducting oxides were examined for their material properties and gas sensing responses to various VOCs such as formaldehyde and acetaldehyde.

2.2. Gold nanoparticles with thiol ligands Gold (Au) has attracted many people as a commodity, as a tool for trade and valuing items and for its aesthetic use in jewelry and other accessories. Au is experiencing another renaissance in scientific studies due to its use in nano-science and nanotechnology, with its applications as nanoparticles and self-assembled structures and monolayers [39]. However, the use of gold nano-particles or colloids also dates back to ancient times, with the famous exam-ple of Lycurgus Cup which dates back to the Roman era in the 5th to 4th cen-turies B.C. [40]. The cup has a ruby red color in transmitted light (when the light source is in the cup) and it has a green color in reflected light (when the light source in outside the cup). One other use of Au nanoparticles or col-loids in historic times is for its curative powers for various diseases, which were documented by philosopher and doctor Francisci Antonii in 1618 [41] and by chemist Johann Kunckels in 1676 [42]. One major diagnostic use of gold colloids in medicine was for the detection of syphilis, which continued until the 20th century, although it was not a completely reliable test [43,44].

Nanoparticles within the range from 1 nm to 10 nm are expected to dis-play electronic structures due to electronic-band structures of the nanoparti-cles which are governed by quantum-mechanical rules [45]. The physical properties of these gold nanoparticles (Au NPs) strongly depend on the par-ticle size and shape, the distance between particles and the covering organic shell around them [46].

Synthesis and functionalization of Au NPs has been achieved by using various chemical and physical methods. One of the oldest conventional syn-thesis methods is citrate reduction of HAuCl4 in water [47]. Another method, which gave more control of particle size and allowed facile synthesis of thermally and air stable Au NPs covered with organic ligands [48], which is called the Brust-Schiffrin method, which allows repeatable isolation and dissolution of Au NPs in organic solvents without aggregation or decompo-sition. Furthermore, the Au NPs can be easily functionalized with organic and molecular compounds. This method uses organic thiol ligands which bind to the Au NPs due to strong interaction between Au and S [46,48,49]. In addition to chemical methods, various physical methods such as photo-chemistry and radiolysis are combined within the chemical processes [39]. For example the near infrared (IR) laser irradiation is used to have larger size Au NPs with thiol ligands combining photocatalysis with the sol-gel method

20

[50]. Conventional and ion assisted evaporation methods can also be used to create gold nanoparticles and thin films by controlling the growth stages and thickness [51].

Au NPs with thiol ligands have interesting physical and electronic proper-ties such as surface plasmonic behavior and current-voltage characteristics which allow them to be used as non-linear circuit elements (diodes) [39,52]. The characteristics strongly depend on the particle size and the ligands used for functionalization, which makes the particles form net-like clusters.

Structures produced with thiol ligand connected Au NPs have shown promising results for sensor applications [53–55]. Since a small number of molecules are sufficient to alter the electrical properties of a sensor made of Au NPs with thiol ligands, such an element can detect very low concentra-tions of target materials, i.e. VOCs [53]. Furthermore, such sensors do not need heating, since they are operated at room temperature, as opposed to metal oxide sensors, which makes Au NPs with thiol ligands a power-saving alternative and allows them to be used safely in flammable environments [56]. Such low detection levels using AuNPs and the flexibility of using different thiol ligands to monitor different VOCs, resulted in promising re-sults on diagnostic sensors which use breath analysis for the early detection of illnesses such as lung cancer via means of data treatment algorithms such as principal component analysis with results from Au NP sensors with dif-ferent thiols [57].

In this study formaldehyde detection results from sensors made of Au NPs with thiol ligands are presented. The sensors were manufactured by a new method composing of two steps: (i) physical evaporation to have dis-persed Au NPs and (ii) functionalization with organic thiol ligands. Surface chemical properties, crystalline properties and electronic properties of the sensor devices were inspected, followed by experiments on their gas sensing properties.

2.3. Graphene based gas sensors Graphene has attracted strong scientific and technological interest in recent years [58,59] due to its promise in different applications such as electronics, energy storage and conversion (batteries and supercapacitors, solar cells etc.) and also in biosensors. Graphene is formed of a single layer of carbon (C) atoms, a 2D material, and it has unique physical and chemical properties such as high surface area (2630 m2/g theoretically) [60,61], excellent thermal and electrical conductivity and high mechanical strength.

The simplest way to produce graphene flakes is the scotch-tape method; simple mechanical exfoliation of graphene flakes from graphite. It is still used in many laboratories to produce small amounts of graphene in order to conduct basic scientific research and to prove concept devices. However,

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this method has a low yield and it is not suitable for mass production. Ther-mal decomposition of SiC wafers and chemical vapor deposition (CVD) enable mass production of graphene sheets for electronic applications [59]. Another method is thermal decomposition or chemical decomposition of graphene from graphene oxide (GO) [61]. Thermal reduction is the most economical way of producing mass quantities of graphene sheets. Further-more, a graphene sheet produced with thermal reduction has many structural defects and functional groups, which make this method interesting for elec-trochemical applications such as sensors [59].

In addition to high speed electronic devices, graphene is a great candidate for making sensors due to its high surface area to volume ratio. As a single layer material, adsorption events on the surface of graphene become very significant to the resistivity. These led to the single molecule sensing devices on NO2 and NH3 in 2007 [62]. Furthermore, graphene based sensors were incorporated with metal oxide grains, such as TiO2, in order to boost sensi-tivity and selectivity of sensing devices [63].

In this study, results of a sensor made of graphene with TiO2 powder for formaldehyde detection are briefly presented. The sensing element was pro-duced by a simple method called doctor blading from the mixture of gra-phene flakes and TiO2 suspension in ethanol. Surface morphology was stud-ied with scanning electron microscopy, and the crystalline structure of the TiO2 in the sensor was examined with X-ray diffraction (XRD). Sensing measurements were done via monitoring the resistance change and noise measurement with sensors having different Graphene/TiO2 ratios.

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3. Material Preparation and Characterization Techniques

This chapter focuses on the main methods used in the preparation and char-acterization of the gas sensors mentioned in this study. Some principal prop-erties of methods and related equations to model them are presented. In addi-tion, some general results obtained for the material characterization, such as the physical and structural properties of the sensors, are presented.

3.1. Film preparation 3.1.1. Advanced Gas Deposition (AGD) NiO films and Au NPs were manufactured using the advanced gas deposi-tion method, which is also known as gas evaporation [64]. Ultra-fine parti-cles (UFPs) of metals, alloys and oxides can be obtained by having a narrow particle size distribution. The method to make ultrafine nanoparticles relies on heating and evaporating a metal in an inert atmosphere. After evapora-tion, a saturated vapor zone is formed above the evaporation source. Then saturated metal vapor cools down and condenses in the inert atmosphere. In the end, the condensed metal forms nanoparticles. The particle size can be regulated by adjusting some parameters, such as metal vapor and total pres-sure, type of carrier gas which directly dictates the growth conditions of nanoparticles.

In 1976, the classical technique of gas evaporation was introduced by Granqvist and Buhrman [65]. In their work they found that isolated spherical metal nanoparticles produced via gas evaporation had log-normal size distri-bution, which is formulated as:

√exp

(6)

where is the log-normal distribution function, is the diameter of the spherical particle, is the geometric standard deviation and is the statisti-cal median.

23

Figure 7: A schematic drawing of the advanced gas deposition equipment.

In figure 7, an illustration of the advanced gas deposition unit is shown. The main structure is made of two chambers, an evaporation chamber at the bot-tom and a deposition chamber at the top, and a connection pipe, which has 3 mm diameter, between them. Before evaporating the metal, the chambers are closed after placing the crucible that holds the metal seed in the evaporation chamber and substrates in the deposition chamber. After sealing, the unit is pumped down till ~3×10-2 mbar. After evacuation and pumping down, the carrier gas (He) is introduced to the evaporation chamber through a gas inlet. Using He as a carrier gas reduces the effect of pressure on particle size com-pared to Ar [65]. If metal oxides are desired, the additional oxygen flow from gas inlet can be adjusted. After adjusting the pressure and flow rate through control valves, the induction coil power is switched on to melt the metal seed in the source. Only the exhaust to the vacuum pump in the depo-sition chamber is open during coating. This creates a pressure gradient be-tween the two chambers and sends the formed nanoparticles to the deposi-tion chamber through the connection pipe at high speed. Since the particles are collected from a small region of the vapor zone in the evaporation cham-ber, they have approximately the same conditions to form. Thus, the parti-cles have a narrow size distribution.

To produce NiO films with AGD, 20 l/min He and 100 ml/min O2 was in-troduced to the chamber after evacuation. After that, different films were manufactured with different induction heating power levels, pressure values in chambers and thicknesses. In order to have a thicker film with the same chamber conditions, the substrate holder is driven at a lower speed. The evaporation sources were pure Ni pellets placed in carbon crucibles. Using carbon crucibles gives the seed a lifetime to be used since the crucible de-grades in time; however the life time of the crucibles was sufficient to make

24

tens of coatings depending on crucible thicknesses. The evaporation pres-sures for the NiO samples varied from 53.5 mbar to 102.5 mbar and the dep-osition pressures were varied from 2.74×10-1 mbar to 5.84×10-1 mbar in cor-relation with the evaporation pressures. The power on the induction coil heater varied between 2.5 kW to 3 kW. The nozzle diameter for most of the samples was 1 mm. However, in order to have loose films, which enabled peeling them off for making powders, a 3 mm nozzle diameter was used.

Dispersed Au NPs have been produced with AGD as well. An iterative approach was taken to produce such dispersed nanoparticles. First a thick film was produced in order to calculate the yield, and then the induction power, pressure and substrate holder speed were adjusted to have dispersed nanoparticles at a desired coverage which enabled some gap between Au NPs. In order to achieve such films, 20 l/min He was used as a carrier gas flowing the nanoparticles to the deposition chamber through the transfer pipe, which had a diameter of 3 mm. The pressure in the evaporation cham-ber was ~90 mbar and the pressure in the deposition chamber was ~9 mbar during coating. The heater coil was operated with a power of 4 kW. The growth of Au NPs was controlled by a substrate holder speed of 0.04 mm/s and the number of coating layers, i.e. number of passes above the nozzle.

3.1.2. Reactive DC Magnetron Sputtering Reactive direct current (DC) magnetron sputtering is one of the most widely used physical vapor deposition (PVD) techniques for making thin films and coatings. Sputtering is a reliable method which is also used for making large scale thin films which cannot be manufactured with AGD. The main princi-ple of sputtering relies on hitting the target material with highly energized ions in the plasma for ejecting target atoms. Generally Ar is used as the working gas and it is ionized by a strong electrical field between the ground-ed chamber and the conducting target. After applying the strong electrical field, electrons ejected from the target ionize Ar atoms and ionized Ar atoms hit the target at high speed to tear atoms from the target surface. With the collision of Ar ions, secondary electrons are released from the target. These secondary electrons are trapped near the target and move around the magnet-ic field lines of the magnetrons which increases the amount of sputtered at-oms [66,67]. Released target atoms then move towards the substrate and form the coated film. In figure 8 a schematic drawing explains the structure of the sputtering device as well the phenomena.

25

Figure 8: Schematic figure for DC magnetron sputtering device, used for thin film deposition.

The DC magnetron sputtering only allows the use of conductive materials to be used as targets. For insulating samples radio frequency (RF) sputtering, which relies on alternating current, must be used. Such high frequency oscil-lations on the target allow simultaneous sputtering and discharging on the target material.

In order to produce metal oxide films with DC magnetron sputtering, ox-ygen gas is introduced with the argon gas. Oxygen reacts with the target atoms to form metal oxides. Oxygen is introduced to the chamber from a separate inlet and the composition of the film can be varied by adjusting the partial pressure of the oxygen. A low flow rate of oxygen can result in non-complete reaction and, on the other hand, a high amount of oxygen can de-crease the sputtering rate. The method can also be applied to nitrides by us-ing nitrogen.

In this work metal oxide, InSnO, films were prepared with a DC magne-tron sputtering device having a Balzers UTT 400 vacuum chamber with a base pressure of 2x10-5 Pa [68]. The unit enables the preparation of thin films with more than one target.

The InSnO films were deposited on glass substrate without any heating. Two magnetron sources were used with two 5-cm-diameter targets consist-ing of 99.99% pure In(3 wt.%)–Sn(97 wt.%) and In(90 wt.%)–Sn(10 wt.%) which were positioned 13 cm above the substrate which kept rotating during deposition to obtain even films. Ar and O2 flow rates were adjusted in order to keep the pressure, p, constant in the sputter plasma. Adjusting the power on both targets allowed changing In/Sn ratios in the InSnO films. The pres-sures during deposition of the films were in the range of 0.53 Pa and 0.58 Pa. The Ar flow rate, fAr, was kept at 25 ml/min where the oxygen flow rate, fO2, was changed from 9.0 ml/min to 17.2 ml/min in correlation with the power on the In(3 wt.%)–Sn(97 wt.%) containing target.

Thin films of VO2 were prepared by reactive DC magnetron sputtering of a metallic vanadium disk (95.5% purity and a diameter of 50.8 mm and 6.35

26

mm thick) in a deposition system based on the Balzers UTT 400 unit. The deposition chamber was evacuated to a base pressure of 6.3×10-7 mbar and then the pressure was raised to 1.2×10-2 mbar after letting in Ar and O2 gas in different ratios, Γ, defined as:

Γ 100% (7)

where ΦO2 and ΦAr are oxygen and argon fluxes, respectively. Oxygen flux rates for sample A, B, C, and D were 6.75 (Γ=7.78%), 6.50 (Γ=7.51%), 6.25 (Γ=7.25%) and 6.75 (Γ=7.78%) mbar/sccm, respectively. The Ar flux rate was kept the same 80 mbar/sccm, for all samples, while the sputtering power density, Pd, was fixed at 8.58 W/cm2. All samples were produced at a sub-strate temperature of 385 °C except sample A, which had a substrate temper-ature of 375 °C.

3.1.3. Doctor – Blading Method Some of the sensors in this study, Graphene/TiO2 gas sensors, were manu-factured by using the Doctor-blading method which is a very simple method that uses a mixture of a viscous liquid to produce thick films. The mixture is applied on the desired substrate and a ‘blade’ is swiped over the mixture to apply it equally to a larger area and to clean up the excess. Then the mixture is dried with further heating.

TiO2/graphene films were synthesized with doctor-blading of mixed col-loids of graphene and TiO2 nanoparticles. Graphene flakes were obtained from conductive graphene dispersion in n-butyl acetate having 23 wt% gra-phene in the mixture (commercially available as Graphene Supermarket UHC-NPD-100ML). Since ethanol and n-butyl acetate are miscible in each other, ethanol was used in the TiO2 mixture. Hydrophilic fumed TiO2 nano-particles (commercial as AEROXIDE TiO2 P 25) were used to prepare a colloid having 20 wt% in ethanol. Then different mixtures were prepared to have 1, 5, 10 and 20 wt% graphene/TiO2 ratios. The colloids of TiO2/graphene mixture were used to prepare films on SiO2/Si substrates with Au electrodes. The substrates used had 300 µm gap between each contact and consisted of four contacts. After applying the mixture on the substrates, the residue of the mixture was cleaned by using a ‘blade’, simply a sharp and flat knife or object. After removing the residue the mixture dried quickly to form a film on the substrate due to the use of ethanol as a solvent. However, films were heated at 50 °C for 30 min to get rid of remaining solvents. The procedure is summarized in figure 9, showing all its steps.

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Figure 9: Preperation of TiO2/graphene sensors is as follows: a) TiO2 powder mixed with ethanol b) Graphene suspension and TiO2 suspension were mixed together c) Drop-casting of mixture on sensor substrate and cleaning of residue by a blade d) Baking sensors at 50 °C for 30 min to remove remaining ethanol and n-butyl ace-tate.

3.1.4. Functionalization of Nanoparticles The Au NPs produced with AGD were dispersed on the substrate which enabled them to have a distance in between them. However, in order to have a conducting path between the Au NPs and establish a path between elec-trodes thiol ligands were used to cover the Au NPs. The procedure involved multiple steps as follows: Firstly, thiol ligands were mixed and solved in ethanol. The solution of the specific thiol ligand was immersed on the sub-strate which was coated with dispersed Au NPs in advance. After immersion of the thiol solution, the substrates were dried for 30 minutes in a preheated oven at 50 °C.

In this study AuNPs were functionalized with 1-dodecanethiol (C12H25SH) and 2-mercaptobenzoxazole (C7H5NOS). These organic com-pounds have thiol groups (-SH) which makes them bind strongly on Au NPs due to the strong affinity between sulfur and gold [69]. The choice of ligands were founded on previous studies, which showed that the Au NPs function-alized with 1-dodecanethiol and 2-mercaptobezoxazole show promising results for monitoring VOCs, such as formaldehyde and acetaldehyde [57,70].

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Figure 10: Functionalized Au NPs are covered with thiol ligands, 1-dodecanethiol in this case, enabling electrical connection between them and electrodes.

3.1.5. Heat Treatment Produced samples were subjected to heat treatment with a programmable oven, Logotherm S17 Nabertherm. The oven has a connection hole to out-side air and has temperature stabilization. Metal oxide films made via AGD and the sputtering method were subjected to heat treatments for different amounts of time, as mentioned in papers attached in this thesis. Ligand acti-vated Au NPs and Graphene/TiO2 samples were heated at 50 °C in order to get rid of remaining solvents.

3.2. Material Characterization Techniques Thickness measurements were performed with a surface profilometer and for some samples the results were validated with cross-section images obtained from scanning electron microscopy (SEM) images. Surface chemical analy-sis and material concentrations of metal oxide and Au NP sensors were ob-tained with X-ray photo electron spectroscopy (XPS). Crystal structures of thin films were investigated by using grazing incidence X-ray diffraction (XRD). Surface morphologies of the samples were examined with SEM and material concentrations in InSnO films were also examined with energy dis-persive X-ray spectroscopy (EDX). The local structure of NiO samples was studied with extended X-ray absorption fine structure (EXAFS) analysis. Nitrogen adsorption and desorption isotherms were performed for the sake of analyzing the mesoscale structure and the porosity of NiO samples.

3.2.1. Thickness Measurements A surface profilometry device, Veeco Dektak XT, was used to measure the thickness of metal oxide samples and TiO2/Graphene samples. The device

29

has a stylus sensor with 12.5 µm radius which scanned over the samples from uncoated to coated parts. The stylus was connected to X-Y-Z stage that moves across the sample surface. The height resolution of the profilometer was 0.5 nm and it had a scan range of 55 mm.

3.2.2. X-ray Photoelectron Spectroscopy (XPS) Photoelectron spectroscopy is a technique that relies on the photo electric effect. The specific case of the incoming high energy X-ray beam taking out electrons from the core shells in an atom is used for XPS. The method is also named as “Electron Spectroscopy for Chemical Analysis” (ESCA) which was introduced by Kai Siegbahn at Uppsala University, Sweden [71]. The reason for the naming comes from the fact that Siegbahn and his colleagues were the first to develop the device and demonstrate that chemical infor-mation could be obtained by this method.

An instrument for making XPS measurements is shown in figure 11. The working principle of the device is as follows. Firstly, a monochromatic beam of X-rays is generated from an Al or Mg source. Typically Mg or Al Kα radiations which have photon energies at 1253.6 eV and 1486.6 eV, respec-tively, are used in the instruments. The incoming photon beam takes out electrons from the core shells of the specimen. Then, the kinetic energy of the electrons taken from the atoms can be written as follows:

(8)

where, KE denotes the kinetic energy of the electrons, denotes the energy of the incoming photon and BE denotes the binding energy of the core elec-tron, i.e. the minimum energy needed to take out the electron from a core shell. The electrons are passed through a hemispherical analyzer which al-lows only certain ones with specific energy to reach the detector. Lastly, the electron detector takes the count of electrons from the hemispherical analyz-er with a specific kinetic energy.

The XPS experiments produce interesting results about the elemental composition and chemical states on a very thin surface layer less than 2 nm. The surface sensitivity of the technique arises from the mean free path of electrons with a certain kinetic energy in the solid, rather than the X-ray absorption. The elemental composition of the sample surface can be acquired through broad scans and high resolution scans can be used to acquire chemi-cal identification through chemical shifts, multiplet structure and satellites.

30

Figure 11: Simplified schematic of an XPS instrument. The incoming monochro-matic X-ray beams are generated in the Al anode. Then, produced photoelectrons are detected and counted after analysis in the hemispherical energy analyzer.

The XPS measurements of the samples in this thesis were recorded within a PHI Quantum 2000 Scanning ESCA Microprobe with a monochromatic Al Kα1 radiation X-ray source, having a beam diameter of 200 µm. To control charging of the samples, a neutralizer filament was used in all measure-ments. Adventitious carbon 1s peak at 248.8 eV was used to calibrate spectra in order to correct peak shifts due to charging. Elemental compositions were acquired with broad scans having a binding energy range between 1100 eV and 0 eV. High resolution scans had ranges depending on the element with a resolution of 0.025 eV.

3.2.3 X-ray Diffraction (XRD) The X-ray diffraction is a widely used method for the characterization of solid materials [72,73]. The method gives, a considerable amount of infor-mation about the composition, crystal structure and size, defects and orienta-tion in a solid. The physical phenomena that leads to XRD are elastic scatter-ing of X-rays (Thomson scattering) and Bragg’s law which defines the an-gles for coherent and incoherent scattering from a crystal lattice.

31

Figure 12: Schematic drawing of D5000 X-ray diffraction unit setup.

The schematic drawing of the XRD unit is shown in figure 12. The elements are placed on a goniometer in order to measure angles with high precision. The X-ray source is a Cu Kα1, type which generates a beam having a wave-length of 1.54 Å. A Göbel mirror is used to obtain parallel beams. Parallel X-ray beams passing through the solid sample interact with the atoms in the crystal planes either constructively or destructively depending on the dis-tance between planes. This interaction has been formulated by Bragg’s Law as follows:

2 (9)

where n is an integer and order of reflection, λ is the wavelength (1.54 Å for Cu Kα1), dhkl represents the distance between planes with Miller indices (hkl) and the angle θ is the scattering angle of the beam. The beam intensity is monitored by a detector at the back end in order to monitor the intensity of diffracted X-ray beams at various scattering angles.

XRD was used for the determination of crystalline sizes and crystal struc-tures of metal oxide films and Au NPs. Furthermore, graphene/TiO2 films were analyzed with XRD to monitor the crystallographic phase of TiO2 in those films. In addition to crystal structure, mean crystallite sizes, denoted as τ, were calculated according to the Scherrer’s formula [72]:

(10)

where κ is a dimensionless constant to denote the shape factor and is gener-

32

ally taken as 0.9, λ is the wavelength of the X-ray beam (1.54 Å), β is the full width half maxima of the diffraction peak to denote the broadening in radians and θ is the scattering angle. All XRD measurements were per-formed by using a grazing incidence Siemens D5000 diffractometer, having a resolution of 0.05 degrees.

3.2.4 Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) Microscopy has been an effective tool to resolve small details on a sample. However, light based microscopes are not efficient below µm scales since the wavelength of the visible light is between 390 and 740 nm. In order to observe smaller scale details, electrons can be used instead of photons due to wave-particle duality. Electron wavelengths can be adjusted by altering their energy which can be done by accelerating them in an electric field.

An SEM instrument is composed of an electron gun, magnetic lenses, de-flector coils and detectors for monitoring electrons and X-ray beams. Elec-trons are produced in the electron gun and they are accelerated by an electric field. Magnetic lenses and deflector coils are used for focusing the electron beam and scanning across the sample.

Figure 13: Interaction of incident electrons on the surface of a sample in the SEM results in emission of electrons and X-rays.

The incoming focused electrons interact on the sample surface as shown in figure 13. Secondary electrons are the ones having lower energy compared to backscattered electrons and are used to monitor topographical details of the sample surface by using a secondary electron detector. Backscattered electrons, on the other hand, are formed at deeper parts in the sample and carry compositional information about the sample. In addition to electrons, X-rays are emitted from the sample-electron beam interaction. The incoming beam can excite an atom in the sample and take out one of the core elec-trons. An electron from an outer shell can replace the core electron and X-rays are emitted in this process. The emitted X-rays and their energies can be

33

measured by an EDX detector which allows monitoring the elemental com-position of the sample.

Samples to be monitored and analyzed must be vacuum compatible due to the use of electrons for imaging. Furthermore, a sample must be conducting enough to be monitored in SEM since an insulating sample charges up or even decomposes after being subjected to the electron beam. To prevent the charge up effect a sample can be coated with a thin Au layer.

In this study, SEM was extensively used to monitor the topography of al-most all samples. For InSnO based SMOX sensors, EDX analysis was used to confirm In and Sn ratios in the samples with respect to each other, i.e. ratio of In or Sn to total amount of In and Sn. All SEM imaging and EDX analyses were done in the same instrument, Zeiss 1550 Leo with AZtec EDS. The device is a high resolution SEM with a resolution of 1 nm and has a Schottky filed emission gun having acceleration voltages between 0.1 to 30 kV. The SEM unit has detectors for both secondary and backscattered elec-trons, but all images were acquired with an InLens secondary electron detec-tor. EDX measurements were performed using an 80 mm2 Silicon Drift De-tector in the SEM unit and analyzed with AZtec software. None of the sam-ples were coated with Au or any other conducting material before imaging.

3.2.5 Extended X-ray Absorption Fine Structure (EXAFS) The electromagnetic radiation in the X-ray region is a powerful probe to monitor the structural properties of the matter since the wavelengths of the X-rays are about 0.1 to 50 Å. The oscillating electric field of the X-rays in-teracts with the electrons in the atoms; either the X-ray beam is scattered or it excites the electrons in the atoms after absorption. When a parallel beam of monochromatic X-rays, having an intensity of I0, pass through a sample having a thickness of x, the intensity of the beam reduces to I according to the expression [74]:

ln (11)

where µ is the linear absorption coefficient and it depends on the atoms and the density, ρ, of the material. Thus it is more convenient to use mass ab-sorption coeffient (µ/ρ) as a measure of photoelectric absorption, which is independent of the physical state of absorbing atoms of the sample. Thus, the above equation 11 can be rewritten as [74]:

/ (12)

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where the mass absorption coefficient increases with the incoming X-ray wavelength λ, except at certain points where absorption decreases suddenly and gives rise to an absorption edge as shown in figure 14. The equation 12 is also known as the mass absorption law. In the points where absorption edges occur, the energies of the incident photons from X-ray beams are just sufficient to excite a core electron of an absorbing atom to continuum state, where a photoelectron is produced. As a result, energies of the absorbed photons correspond to binding energies of electrons in the shells of the ab-sorbing atom.

Figure 14: X-ray absorption spectrum of a nickel oxide powder showing XANES, NEXAFS and EXAFS regions where the vertical axis denotes ratio of X-ray intensi-ties. Note that the absorption edge corresponds to K 1s binding energy of Ni at 8333 eV.

The high energy side of the absorption edge has a fine structure which is directly related to structural properties of the sample. This phenomenon is called the X-ray absorption fine structure (XAFS) and it is a remarkable development for the study of local structures around elements[75]. The XAFS is investigated in different regions according to their positions with respect to the corresponding absorption edge namely; X-ray absorption near edge structure (XANES), X-ray absorption near edge fine structure (NEXAFS) and extended X-ray absorption fine structure (EXAFS).

8200 8400 8600 8800 9000 9200

log

(I0/

I 1)

Energy (eV)

XANES

NEXAFS

EXAFS

35

Figure 15: Schematic representation of backscattering photoelectrons from a neigh-boring atom to a central atom with two different energies of X-ray photons. a) Con-structive interference occurs between outgoing and backscattering photoelectron waves b) Destructive interference occurs at a different wavelength.

The photoelectrons ejected from the core absorbing atom can be modelled as waves according to de Broglie’s expression where their wavelength, λe, is defined as [76]:

(13)

where me is the mass and v is the velocity of an electron, h is the Planck constant, E is the incident energy of the X-ray photon and E0 is the binding energy or the threshold energy. The photoelectron waves can be backscat-tered from the neighboring atoms which gives rise to constructive and de-structive interference as shown in figure 15. Such events change the electron density around the absorbing atom, so that constructive interference results higher electron density and destructive interference results lower electron density. At certain energy of the incoming X-ray photon, the higher electron densities result in higher absorption. Similarly the lower electron densities, at a different energy, give lower absorption. Thus, changes in interference result in oscillations in the absorption after the edge.

The oscillations after the edge can be explained due to modulations in the absorbance according to equations 11 and 12. Thus, one can define the EX-AFS function, χ(E) as a modulation of absorbance in the form of:

(14)

where µ(E) is the measured absorbance and µ0(E) is the “atomic background absorption” which states the absorption from the isolated atom in its neigh-boring atoms without interactions. The steps to further analyze the XAFS function are pre-edge background removal, normalization of the function, calibration of the edge, conversion from energy to k-space, and spline fitting to isolate fine structure oscillations. When an experimental absorption spec-

36

trum, as in figure 14, is considered, a linear approximation is first taken at the pre-edge. Then, the plot is normalized so that the pre-edge value be-comes zero and long after the edge the function approximates to one. Later, energy calibration is done by assigning tabulated values for the electron binding energies to the maximum of the first derivative of absorption data in the edge region. Finally, a spline fit is done in order to isolate fine structure oscillations. As a result of equation 13, one can convert the energy scale from eV to photoelectron wavenumber or wave vector, k (Å-1) by using the equation [77]:

/ (15)

which follows from the photoelectron momentum, which is defined as ∙ 2⁄ . Note that the threshold energy corresponds to the energy where

k is 0. The expression, which is now defined as χ(k), is multiplied by k3, in order to magnify the signal at larger k values since ripples decrease after the edge with increasing energy. The Fourier transforms of the k3-weighted EXAFS spectra can be subsequently calculated to compute the bond distance from the central atom to neighboring atoms. In the final form, EXAFS equa-tion of a central atom i can be expressed as follows:

∑

sin 2 (16)

where Nj is the number of backscattering atoms in the jth shell, Rj is the dis-tance between the central atom and the backscatterers in the jth shell for single scattering (half of the total path length for multiple scattering), S0

2(k) is the amplitude reduction factor, feff(k) is the effective amplitude function for each scattering path and exp(-2k2σ2) is the Debye-Waller factor in the harmonic approximation which can be used to address the configurational and thermal disorder, Λ(k) is the photoelectron mean free path and [2kRj+ϕij(k)] is the total phase. From the equation and the experiments it is in our interest to investigate the Debye-Waller factors and the interatomic distances of the samples.

The EXAFS measurements can be performed with very bright X-ray beams which can be produced in a synchrotron. A synchrotron is a ring par-ticle accelerator where electrons are injected and accelerated to high speeds in order to produce X-ray beams. When electrons pass through cavities with magnets, wigglers or undulators, electrons lose their energy and X-ray beams are produced. A schematic of an electron storage ring in a synchro-tron is shown in figure 16.

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Figure 16: Schematic drawing of an electron storage ring in a synchrotron [76].

In this study, local structural studies on NiO samples were performed with EXAFS. Extended X-ray Absorption Fine Structure (EXAFS) measurements of the samples were performed in fluorescence mode at beamline I811, MAXLab in Lund, Sweden [78,79]. The beamline has a photon energy be-tween 2.4 and 21 keV, corresponding to wavelength interval of 0.6 and 5 Å. Si(111) and Si(311) changeable double crystal mirrors were used as mono-chromators in order to remove higher harmonics. The spot size of the beam on the sample is 0.5 mm ×0.5 mm. Energy calibrations were carried out us-ing the Ni K edge at 8333 eV. To eliminate energy shift problems, the X-ray absorption spectrum of Ni metal foil was measured in every measurement with the NiO samples. All calculations were performed with the ATHENA software within the Demeter software package [80], which uses ATOMS and FEFF6 to perform interatomic bond calculations [81].

3.2.6 Absorption and Desorption Measurements Nitrogen (N2) adsorption and desorption measurements were performed us-ing an ASAP 2020 instrument from Micromeritics to monitor porosity of NiO samples at 77 K. The NiO powder was collected from thick layers of NiO film on glass substrates simply by removing it mechanically. The sam-ple mass measured, after degassing, was 39.0 mg. The specific surface area (SSA) of the powder sample was calculated by applying the Brunauer–Emmet–Teller (BET) model [82] using the adsorption branch of the isotherm

38

where P/P0 is between 0.05 and 0.35. The average pore diameters were cal-culated using the Barrett-Joyner-Halenda (BJH) method [83] with ASAP 2020 V3.04 E software from Micromeritics.

3.3 Gas Sensing Measurements Responses of the sensors were recorded either measuring resistance changes upon exposure to agents or fluctuation enhanced sensing, i.e. noise meas-urements. In this section, experimental setups for both methods and some mechanisms in fluctuation enhanced sensing are explained.

3.3.1 Resistance measurements The responses of the sensors were recorded via changes of resistance values upon exposure of agents. Resistance measurements were performed via re-cording the current and/or voltage signal with a bias voltage over the sen-sors. These methods were applied for NiO, InSnO, VO2 and activated AuNP sensor samples.

The electrical response, S, of the gas sensor is defined as the change in re-sistance upon exposure to acetaldehyde gas divided by the initial resistance according to

(17)

where, Rgas and Rair are the resistance after exposure to acetaldehyde and synthetic air, respectively [84]. An average value of S over three measure-ment cycles was used in the analysis of our In–Sn oxide films. Another im-portant parameter for the sensors is its resistance drift per gas exposure cy-cle, Rdriftc, which is defined (in percent) as resistance change in synthetic air from the beginning of the measurement (Rbegin) to the end of the measure-ment (Rend) divided by the number of exposure cycles, nc, i.e.

100% . (18)

However, for NiO and VO2 samples, resistance drift is defined without

considering the exposure cycles. As a result, the resistance drift, R, is de-fined as the ratio of the time averaged resistance in synthetic air, after (Rend) and before (Rbegin) the three cycles of gas exposure.

(19)

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3.3.2 Noise measurements The dictionary definition of noise is given as “any unwanted change in a signal, especially in a signal produced by an electronic device” [20]. In this work noise is referred to as random fluctuations in the voltage, current and conductivity in an electronic device [85]. Such ‘unwanted signals’ arise from the fluctuations in the velocity and the number of the charge carriers. Several types of noise are defined, each having a different source and frequency dependence [86]. For example, thermal noise, also called as white noise, is defined from its flat spectrum. In addition, pink noise, also known as flicker noise and 1/f-noise, is defined from its higher values at lower frequencies and is approximately inversely proportional with the frequency.

The resistance fluctuations arising from surface chemical reactions can be used for monitoring gases. This method is also known as fluctuation-enhanced sensing (FES)[85,87,88]. There are two methods to implement the FES method. The first one is called regular sensing (RS), where the sample is constantly heated during operation. The second method is sample-and-hold (SH), where the sensor is cooled while recording the noise signal. The aim of cooling is to trap target gas molecules on the sensor and to modify the dc characteristics of the sensor depending on the temperature, to improve response [89].

The voltage noise can be characterized through power spectral density (PSD), where the voltage noise, SU(f), is a function of the frequency after blocking the DC component on the circuit element, which is a gas sensor in this study. In order to compare the noise response of the elements at different voltages or bias points, PSD must be normalized according to the square of the voltage across the sensor. This yields:

(20)

where SN(f) is the normalized PSD, SU(f) is the measured PSD at a corre-sponding dc sensor signal at UDC. From the normalized values the response to a target gas compared to synthetic air, Gn(f) can be calculated as:

(21)

where tg denotes the target gas and sa denotes synthetic air [90].

In this work, FES is used to monitor responses of NiO samples to formal-dehyde, ethanol and methane. NiO nanoparticles were produced via AGD, and were coated onto aluminum oxide ceramic substrates for use as sensors. FES was used in RS mode to monitor the response of NiO films by measur-

40

ing power density spectra (noise spectra) of sensors from two-point meas-urements.

Figure 17: a) Block diagram of the setup for recording power spectra. The power supply is used for heating the sensor and a fixed current is supplied to the sensor via PC control. Power spectral densities were recorded by the power spectrum analyz-er. b) The circuit connection on the amplifier showing that sensor, Rs, is in the nega-tive feedback loop. The resistance R is adjustable to control input current.

The NiO sensors were placed in a negative feedback loop of a low noise operational amplifier (MAX4478, Maxim Integrated) and were driven by a DC current as shown in figure 17b. The input resistor, R, was used to bias the sensor with DC current values 2.65 µA, 12.4 µA, 56.75 µA and 124.5 µA since the sensor resistance varies according to ambient environment. The voltage fluctuations at the output of the operational amplifier were propor-tional to the resistance fluctuations of the sensor. A spectrum analyzer, Stan-ford SR760, was used to measure power spectral densities of the voltage fluctuations.

In addition to NiO, Au NP sensors activated with 2-mercaptobenzoxazole were tested with FES to monitor formaldehyde. Lastly, TiO2/Graphene sen-sors were tested using FES to monitor NO2. The only difference in their set-up was that the power spectrum analyzer was replaced and electrical signals were recorded in time, u(t), and their Fourier transforms were taken in order to record the voltage noise, SU(f), during measurements.

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4. Results and Discussion

This chapter is about each sensor material. Firstly, material characterization results for the materials are explained, then gas sensing results are presented for each topic.

4.1 NiO Sensors 4.1.1 Material Properties The NiO samples were prepared using the AGD. The samples were coated on Al2O3 substrates with contact electrodes made of Pt/Au. Further samples were coated on glass for EXAFS and XRD characterization, and single crys-tal Si pieces from a <100> wafer for SEM imaging and XPS measurements. A powder was extracted for adsorption and desorption measurements. Coat-ing parameters for the samples are shown in table 1. In addition, some of samples in the first 3 sets were also subjected to further annealing at 400°C and 500°C for 12 hours in addition to 2h ramp-up times.

Table 1. NiO coating parameters with AGD are listed below where (*) symbol de-notes powders that were extracted from set 4.

Sample name Evaporation Pressure (mbar)

Deposition Pressure (mbar)

Heating Power (kW)

Nozzle Diameter (mm)

Film thickness (µm)

Set 1 101.7 5.81×10-1 3.0 1 1.7Set 2 102.5 5.84×10-1 3.0 1 0.6Set 3 53.5 2.74×10-1 3.0 1 0.2Set 4* 86.5 8.45×10-1 2.5 3 1.5

XRD analyses of the samples showed that NiO films have the face centered cubic structure with a reference pattern indexed according to ICDD: 04-001-9373 (International Centre for Diffraction Data) where the lattice parameter for the unit cell is 4.171 Å. Average crystallite sizes were calculated from <200> reflection peaks by applying Scherrer equation [72,73] with a shape factor 0.9. As expected, the average crystallite diameters increased with the annealing temperature. The average diameters from the figure 18 were calcu-lated to be approximately 3.7 nm, 5.9 nm and 11.4 nm for the as deposited

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sample, the sample annealed at 400 °C for 12 h, and the sample annealed at 500 °C for 12 h, respectively.

Figure 18: X-ray diffraction spectra of samples with various annealing temperatures from set 1 (1.7 µm thickness).

Figure 19: X-ray diffraction spectra of as deposited samples in the sets.

As deposited samples, as expected, have smaller crystalline sizes, 3.7 nm for the first two sets and 2.2 nm for set 3 (Figure 19).

The SEM images clearly show that the films are mostly composed of par-ticles of approximately 5 nm diameter. The images show that surface mor-phologies are quite rough and particle sizes indicate large surface areas. Fur-thermore, the cross section images show that the porous morphology extends through the films.

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Figure 20: SEM images of NiO thin films. a) Surface of NiO film from set2 showing rough surface morphology. b) Cross section image of NiO film on single crystal Si substrate. The film was annealed at 400 °C.

Figure 21: XPS spectra of a Ni 2p3/2 peak obtained from sample set 1 before anneal-ing and after annealing at 500 °C.

XPS spectra of NiO films were recorded for C 1s, O 1s and Ni 2p3/2 spectra. The C 1s peak scans were used for calibration. For the Ni 2p3/2 peak spectra, as shown in figure 21, which were expected to be complex for oxides, there are mainly two peaks around 854.6 eV and 856.1 eV, due to multiplet-splitting [91,92]. The split in the annealed sample is more visible and corre-lated to an increase in the crystal size after annealing. There is a shake-up satellite at 6 eV higher to binding energy as well.

The O 1s spectra exhibit two major peaks at 529.7 eV [93,94] and 531.4 eV [93], having a wider peak at higher binding energy, as shown in figure 22. The component of the O 1s peak at 531.4 eV is attributed to either a Ni2O3 defect structure [92] or to Ni3+ defects in the structure [92]. In addition to major components, there is a small contribution at 533 eV in the region expected for intercalated water. Annealing increases the intensity of the peak at 529.7 eV and there is a decrease of the peak at 531.4 eV. This also indi-cates that the number of defects decrease, at least in the surface region, which is consistent with the crystal growth.

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Figure 22: XPS spectra of O 1s peak obtained from sample set 1 before annealing and after annealing at 500 °C.

Figure 23: a) Experimental Fourier filtered k3χ(k) spectra of NiO films and b) Cor-responding Fourier transforms. Experimental data is shown in black lines whereas the fit results are in red.

As seen from the FT magnitudes (figure 23b) of the EXAFS spectra (figure 23a), major contributors in the first shell are the first two peaks in FT, which are Ni-O, and Ni-Ni peaks, respectively [95]. Similarly, in the third peak, Ni-Ni(2) pair of the second shell contributes as well as multiple scattering paths of Ni-O(1)-Ni(2) and Ni-O(1)-Ni(2)-O(1) paths due to the focusing effect in forward scattering where the scattering angle is 180° and forming a linear path. Finally, in the fourth peak includes a Ni-Ni(4) single scattering path as well as Ni-Ni(1)-Ni(4) and Ni-Ni(1)-Ni(4)-Ni(1) multiple scattering paths, due to the focusing effect. Note that the numbers in parentheses represent the shells counted from the central atom. The corresponding atomic distances are calculated and presented with other major EXAFS equation parameters in the appendix of paper III.

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Figure 24: a) N2 adsorption and desorption isotherm of the NiO powder sample from set 4 at 77 K and b) Pore volume per unit pore width (dV/dw) obtained from nitrogen adsorption.

Figure 24a displays the N2 adsorption and desorption isotherms for the as deposited NiO powder. The N2 sorption isotherm exhibits a typical Type IV shape according to IUPAC classification and indicates a mesoporous materi-al [96]. Furthermore, the type for the hysteresis is H4, according to IUPAC classification [96], confirming the mesoporous structure. The first rounded knee in the adsorption isotherm, where P/P0 is approximately 0.05, is due to monolayer formation. A specific surface area (SSA) 154.7089 m²/g for NiO powder was calculated from the Brunauer-Emmet-Teller (BET) equation [82] in the range 0.05<P/P0<0.35. The BET constant, c is calculated as ~138.7, indicating strong adsorption of N2. Figure 23b shows the pore vol-ume versus average pore width, which is derived from the adsorption iso-therm by applying Barrett-Joyner-Halenda (BJH) method [83] on the N2 sorption isotherm. The figure shows that most of the pores have a width be-tween 2 and 10 nm with an average pore width of 3.42 nm from the BJH method.

4.1.2 Gas Sensing Results Changes in resistance and noise fluctuations were used to detect the response of NiO sensors to ethanol, formaldehyde and methane. The underlying mechanism for the resistance change is oxygen adsorption and dissociation and ensuing electron transfer reactions due to oxidation of gases on the NiO film surface [97]. Since NiO is a p-type semiconductor, the adsorbed oxygen species take electrons from the Ni atoms in the film, thus increasing the number of holes and decreasing the resistance of the film as:

→ . (22)

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Then, gases to be monitored react with the adsorbed oxygen species and as a result of the surface reactions, electrons are released. For example the reac-tions for formaldehyde occur as follows:

→ (23)

2 → 4 . (24) As seen from the equation 24, the oxidation of gases is dominant in the re-sponse. The chemical reaction for the gas sensing can be generalized as fol-lows [98]:

/ / → (25) As a result of increased electrons on the surface, the number of majority charge carriers, holes for a p-type material, decrease and this leads to an increase in the resistance of the NiO film.

The first measurements were done with an as-deposited sensor from set 4 at 150 °C, with formaldehyde. The sensor was exposed to synthetic air and formaldehyde-synthetic air mixture at various concentrations with 30 min cycles for each. The change in resistance upon exposure and the rate of the resistance drift which is the resistance under synthetic air before and after experiment are shown in figure 25.

Figure 25: Resistance change and resistance drift of an as deposited NiO sensor (set4), operating at 150 °C, and exposed to different concentrations of formalde-hyde.

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Further measurements were done with NiO samples in the first 3 sample sets at 200 °C both to monitor resistance response (figure 26) and noise re-sponse (figures 27, 28). After increasing the operating temperature, the re-sponse of the sensors increased drastically due to the oxidation rate at a higher surface temperature. For comparison, dc resistance and noise re-sponse at 10 Hz were defined as the ratio of the signals after gas exposure and before gas exposure in synthetic air (figure 29).

Figure 26: Resistive response of NiO samples upon exposure of a) formaldehyde and b) ethanol and methane .

Figure 27: Normalized noise power spectral densities for an as-deposited NiO sen-sor, from set 3, in synthetic air (SA) and formaldehyde (1 ppm).

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Figure 28: Normalized noise power spectral densities for an as-deposited NiO sen-sor, from set 3, in synthetic air (SA), ethanol C2H5OH (25 ppm) and methane CH4 (50 ppm).

Figure 29: Normalized power spectral density responses of as-deposited NiO films from set 3, a) to formaldehyde and b) to ethanol and methane, at 10 Hz.

The NiO sensors showed selective detection both in resistive and noise measurements where the response to formaldehyde was higher than ethanol and no significant response was measured for methane. Furthermore, two detection methods relying on DC resistance and resistance noise were com-pared. For most of the cases noise and conductive responses were close and consistent with each other.

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4.2 InSnO sensors 4.2.1 Material Properties

Indium–tin oxide thin films were deposited onto unheated glass substrates by reactive DC magnetron co-sputtering and table 2 shows the thickness and chemical composition of films after deposition (as-deposited) and after an-nealing at 400 °C for 6 h.

Table 2: Film thickness, d, and composition of In–Sn oxide films are shown in as-deposited and annealed states.

Sample d [nm] In/[In+Sn](%) (as-deposited) (XPS)

Sn/[In+Sn](%) (as-deposited) (XPS)

In/[In+Sn](%) (6h annealed) (EDS)

Sn/[In+Sn](%) (6h annealed) (EDS)

O [at%] (as-deposited) (RBS)

O [at%] (6h annealed) (RBS)

A 170 3 97 4 96 65 63 B 180 10 90 12 88 65 65 C 190 21 79 24 76 65 66 D 150 31 69 35 65 65 66 E 120 49 51 52 48 64 66 F 110 75 25 76 24 64 60

SEM images show that all films exhibit a nanostructured morphology con-sisting of aggregates with dimensions between 10 and 20 nm. The nanostruc-ture was essentially the same in an as-deposited sample and after its anneal-ing post-treatment. Typical film morphologies are shown in figure 30. A few larger aggregates, 50–100 nm in size, were occasionally observed in the films, and they had the same In and Sn content as the rest of the films, as demonstrated by EDX.

Figure 30: SEM images of sample E, characterized in Table 2, after annealing for 30 min (a) and 6 h (b).

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Figure 31: X-ray diffraction patterns of samples characterized in Table 2. All sam-ples were annealed at 400 °C for 30 min (a) and 6 h (b). The diffraction peaks for the crystalline samples were assigned to SnO2 (samples A and B) and In2O3 (sample F).

XRD analysis of samples annealed for 30 min and 6 h at 400 C revealed different film structures depending on their In and Sn contents. As shown in figure 31, when the In/(Sn + In) ratio is below 20% the films have a tenden-cy to form a tetragonal SnO2 structure corresponding to the reference pattern ICDD:00-021-1250. Furthermore, it is observed that the crystallite size var-ied depending on composition. By applying the Scherrer equation [72] to the <101> reflection peak at 33.89°, the crystallite sizes for sample A were cal-culated to be ~4.8 nm and ~6.0 nm after annealing for 30 min and 6 h, re-spectively. The corresponding crystallite sizes for sample B were ~4.3 nm and ~6.1 nm. Samples C, D and E were found to be amorphous according to XRD irrespective of annealing time up to 400 °C. Film F exhibited a crystal structure that can be assigned to cubic In2O3 in agreement with the reference pattern ICDD:00-006-0416. By applying Scherrer’s equation to the <222> peak at 30.58° for sample F, crystallite sizes of ~18.1 nm and ~18.4 nm were calculated for those films annealed for 30 min and 6 h up to 400 °C, respec-tively, i.e. the cubic In2O3 crystallites were much larger than for the SnO2 structure. It is apparent that the crystallite sizes do not agree with the fea-tures observed in SEM images and that the nanostrucures seen in SEM con-sist of aggregated crystals.

4.2.2 Gas Sensing Results Resistance changes were recorded for the In–Sn oxide films during con-trolled acetaldehyde gas exposure cycles. The mechanism for gas sensing is the resistance change arising from the surface chemical reactions of acetal-dehyde with the adsorbed oxygen species in the thin film sensors [22,38,99]. At first, atomic O fragments accept electrons from metal atoms thus giving a decreased electron concentration in the film. The In–Sn oxide film is an n-

51

type semiconductor, which leads to an increased film resistance. After expo-sure, acetaldehyde reacts with the adsorbed O on the film according to equa-tion 25. In addition the following reaction occurs with acetaldehyde:

𝐻𝐻2𝐻𝐻4𝑂𝑂 + 𝑂𝑂(𝑎𝑎𝑑𝑑𝑎𝑎)

− → 𝐻𝐻𝐻𝐻3𝐻𝐻𝑂𝑂𝑂𝑂𝐻𝐻 + 𝑒𝑒− , (26)

which results in a decrease in the resistance of the sensor. Three exposure cycles were used to obtain Rdrift, except for samples E and

F where one cycle was used. An illustrative figure showing the parameters (for equations 17 and 18) is shown in figure 32.

Figure 32: Electrical resistance vs. time measured at 200 °C for sample F, annealed for 6 h at 400 °C. Data were taken for alternating exposure to 25 ppm of acetalde-hyde and synthetic air.

Figure 33: Response and resistance drift per acetaldehyde exposure cycle for sam-ples A–F, characterized in Table 2, with increasing In content on the abscissa. Data are shown for samples annealed at 400 °C for 30 min (a) and 6 h (b), and the re-cordings were performed with 25 ppm of acetaldehyde at 200 °C as illustrated in fugure 31. Vertical bars signify experimental uncertainties (standard deviations). Symbols indicating measured results are connected by straight lines for conven-ience. Arrows indicate applicable vertical axis.

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Responses and resistance drifts of the sensors were measured with 25 ppm of acetaldehyde, as shown in figure 33. In figure 33, there is a slightly en-hanced response of the sample with In/(In+Sn) close to 50%. Although the result lies within experimental uncertainties, this observation supports a sim-ilar result in work by Kemmler et al. [100], but with the important difference that samples with intermediate In-Sn composition are amorphous. Then, samples having the highest responses were tested for lower concentrations of acetaldehyde as shown in figure 34. A lower detection point of 200 ppb has been achieved with high SnO2 containing samples.

Figure 34: Response (a) and resistance drift per exposure cycle (b) as a function of acetaldehyde concentration for representative sensor samples characterized in Ta-ble 2. The annealing times are indicated in the legends, and the annealing tempera-ture was 400 °C. Vertical bars signify experimental uncertainties (standard devia-tions). Symbols indicating measured results are connected by straight lines for con-venience.

4.3 Au NP sensors 4.3.1 Material Properties Crystalline properties of the AuNPs were investigated on a glass substrate with a film of 300 nm thickness. The crystal orientation of the AuNPs was characterised by X-Ray Diffraction (XRD), as shown in Figure 35. A strong diffraction peak at 38.3° was attributed to Au (111) planes parallel to the surface of the glass substrate. The other three diffraction peaks, attributed to Au (200) at 44.4°, Au (220) at 64.8° and Au (311) at 77.6°, exhibited weaker relative intensities than for Au (111). The diffraction pattern is in good agreement with that of elemental gold and shows no evidence of contamina-tion or the formation of any secondary gold phase. Applying Schererr’s for-mula to the (111) peak gives average crystal diameter, d ≈ 11 nm, which is in good agreement with the particle diameter seen from SEM images in Figure 36a (≈ 10 nm). Importantly, the number of cycles used during AuNPs depo-sition did not have any direct influence on particle grain size despite the fact

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that an increased number of deposition cycles led to AuNPs aggregation in clusters or to the formation of a thin film of AuNPs.

Figure 35: X-ray diffractogram of the AuNPs in a thick film.

In the second processing step, the Au NPs were capped with dodecanethiol and 2-mercaptobenzoxazole. These organic compounds contain a thiol group, which is likely to bind strongly to the Au NPs because of the strong affinity of sulfur to gold [69]. After functionalization, the morphology of the films changed from discontinuous NPs to web-like continuous clusters as shown in figure 36b.

Figure 36: SEM images of a) AuNPs deposited by AGD and b) isolated AuNPs func-tionalized with dodecanethiol.

The XPS characterization of the samples, shown in figure 36, has a major component of Si, which indicates incomplete coverage of the substrate by the nano-assembly matrix. Elemental compositions obtained from the XPS measurements, figure 37, are shown in table 3. Note that these results show the composition from the 200 µm wide circular area where the X-ray beam falls, not only the Au NP with the ligands.

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Table 3: Atomic concentrations from XPS survey spectra.

Sample C1s Si2p O1s Au4f S2p N1s

AuNPs functionalised with dodecanethiol

30.1% 38.4% 12.1% 13.7% 5.6% -

AuNPs functionalised with 2-mercaptobenzoxazole 44.9% 21.3% 16.2% 7.3% 5.5% 4.8%

Figure 37: X-ray photo electron spectra of AuNP functionalized with 2-mercaptpbenzoxazole on Si substrate.

The functionalized Au NP sensors showed Shottky diode behavior. The sample functionalized with dodecanethiol had 0.4V threshold voltage, which is in line with data for typical metal-semiconductor Schottky diodes [89]. Measured I-V curve showing the non-linear diode behavior is shown in fig-ure 38.

1000 800 600 400 200 00.0

2.0x103

4.0x103

6.0x103

8.0x103

1.0x104

1.2x104

1.4x104

N 1

s

Au

4f7

Au

4f5

Si 2

pSi 2

sS

2p

S 2

sC

1sAu

4d

5A

u 4

d3

Au

4p3

O 1

s

Cou

nts/

seco

nd

s

Binding energy (eV)

O K

LL

Au NPs functionalized with 2-mercaptobenzoxazole

55

Figure 38: I–V characteristic of a monolayer-caped AuNP-based device showing Schottky-diode behaviour. The zoom shows the value of the threshold voltage.

4.3.2 Gas Sensing Results Sensors made using Au NPs capped with dodecanethiol, were tested with acetaldehyde, ethanol and ethylbenzene at room temperature. The responses of the sensors were recorded using the current change at 0.33 V steps of forward and backward bias points, within 10 V sweep range. Bias voltages varied after 0.115 seconds during sweep. Thus the sensitivity of those sam-ples was defined as

% (27)

where is the sensor’s sensitivity at the voltage V, is the current at

voltage V in the presence of the VOC, and is the current at voltage V in synthetic dry air prior to VOC exposure. Responses were calculated by aver-aging 3 cycles, as shown in figure 39.

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Figure 39: Response of a Schottky-diode sensor, extracted from the sensor’s I–V characteristic at 3.33 V on the backward curve.

The diode was operated in the positive polarization mode, with the metal (i.e., the AuNPs) at a higher potential than the organic material. By increas-ing the voltage, the potential barrier decreased. As the voltage exceeded the conduction threshold voltage of 0.4 V, electrons from the organic material crossed over to the AuNPs. When the monolayer capped AuNPs sensing material was exposed to acetaldehyde, there was charge exchange between the sensing material and acetaldehyde, which altered the charge carrier transport via film swelling due to analyte adsorption [53]. The accumulation of electrons generated by the VOC adsorbed on the sensor resulted in a sud-den enhancement of the electrons’ carrier transport and sensor’s response reached its maximum value. Then the response decreased as the capacity of the adsorbed VOC to generate new electrons was limited by the total VOC concentration. When the sweep was reversed, the observed phenomena were also reversed, with the remark that the sensor’s response reached its maxi-mum value at a lower voltage compared with the forward characteristic (2.5 V). This can be attributed to the hysteretic behavior of the Schottky barrier height variation arising from the polarization reversal of the material [101]. The responses calculated by using equation 27 are shown in figure 40 in-cluding other agents (ethanol, ethylbenzene) to show selectivity.

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Figure 40: Response curves obtained from sensor’s exposure to different VOCs.

Further measurements were performed to monitor the effect of relative hu-midity (RH) on the sensor’s response to acetaldehyde. The response de-creased at lower humidity levels, yet, at high humidity the response to acet-aldehyde greatly increased, as shown in figure 41.

Figure 41: Response curves to 30 ppm of acetaldehyde obtained under different RH levels.

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The samples functionalized with 2-mercaptobenzoxaozole were tested using fluctuation enhanced sensing. The responses of the samples to gases were measured in a similar way as was done for the NiO samples, where fast-Fourier transform was applied on recorded signals instead of using a spec-trum analyzer [102]. As seen in figure 42 and 43, samples, with 2-mercaptobenzoxazole showed more selective response to formaldehyde compared to ethanol and their response “turned on” at higher bias voltages, 11.3 V whereas at 5 V bias the response was much lower.

Figure 42: Power spectral density Sr(f ) of the sensor's resistance fluctuations, mul-tiplied by frequency f and divided by the square of the sensor's DC resistance (246 kΩ), upon exposure to the shown gases at a bias voltage UB of 11.3 V.

Figure 43: Power spectral density Sr(f ) of the sensor's resistance fluctuations, mul-tiplied by frequency f and divided by the square of the sensor's DC resistance (526 kΩ), upon exposure to the shown gases at a bias voltage UB of 5 V.

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4.4 VO2 Sensors 4.4.1 Material Properties Thin films of were prepared as explained in section 3.1.2. The four samples were named with letters A, B, C and D, according to substrate tem-perature and oxygen flux during preparation. Increasing O2 flux rate and temperature, was observed to increase rod like structures as can be seen from the SEM images in figure 44.

X-ray diffraction, in figure 45, showed that the samples were composed of monoclinic VO2 having signals from (011) peak in the structure of ICDD: 00-043-1051, marked with *, and (002) peak in the structure of ICDD: 04-007-0514, marked with **.

Figure 44: SEM images of four samples at x100K magnification.

VO2

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Figure 45: X-ray diffractogram for VO2 films.

4.4.2 Gas Sensing Results The sensor resistance responses for acetaldehyde and formaldehyde were recorded at a substrate temperature of 185°C. Synthetic air was used as car-rier gas, and three cycles of exposure to acetaldehyde (15 min) and synthetic air (15 min) were performed.

The responses and the resistance drifts of sensors to formaldehyde and acetaldehyde are shown in figures 46 and 47, respectively. Samples had slightly higher responses to acetaldehyde than to formaldehyde. The higher response to formaldehyde was obtained with sample A, which later convert-ed to V2O5. Note that sample B had slightly lower response to formaldehyde than sample C. Yet, the acetaldehyde response of sample B is higher than that of sample C.

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Figure 46: Sensitivity and resistance drift of samples to formaldehyde at 185 °C. Films were heated to 185 °C for an hour before each measurement.

Figure 47: Sensitivity and resistance drift of samples to acetaldehyde at 185 °C. Films were heated to 185 °C for an hour before each measurement. Sample A was converted to V2O5 at preheating, thus not included.

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4.5 TiO2/Graphene Sensors 4.5.1 Material Properties TiO2/Graphene films had varying thicknesses depending on graphene amount and a concave geometry as recorded with a Veeco Dektak 150 sur-face profilometry instrument.

As seen from the X-ray diffraction pattern, shown in figure 48, TiO2 na-noparticles are mostly in anatase phase. Moreover, a (002) graphite peak emerges after 5 wt% of graphene/TiO2 ratio and increases with graphene content.

Figure 48: X-ray diffraction patterns of the films show an increasing intensity for the <002> graphite peak (marked with dashed line) as the amount of graphene increases.

SEM images, as in figure 49, showed that most of the graphene flakes are covered by TiO2 particles. These TiO2 particle sizes are of the order of 20 nm.

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Figure 49: SEM image shows that TiO2 particles having size around 20 nm are scattered on graphene flakes. (Sample has 10 wt% graphene/TiO2 ratio).

4.5.2 Gas Sensing Results Before proceeding to fluctuation enhanced sensing, preliminary measure-ments were done. The response to 25 ppm NO2 was observed at room tem-perature by simply monitoring the resistance with a multimeter. Exposure time was 5 min. Not long after checking NO2, the NH3 response is also measured. Measurements were done in room temperature via monitoring the resistance by using a multimeter (figure 50). Exposure time to NH3 was 10 min. No response to NH3 was observed at 200 °C. Both resistance values are from the inner electrodes, i.e. with a gap of 300 µm.

Figure 50: Comparison of resistance changes TiO2 film with 20% graphene upon exposure to NO2 and NH3 at room temperature, in dark.

The normalized product of frequency f and power spectral density S(f ) of voltage fluctuations across a sensor biased by the DC voltage U when the

64

sensor was operated at 60 °C in ambient atmospheres of synthetic air (SA) and 15 ppm of NO2 diluted in SA are shown in figure 51 and 52. The voltage noise was measured when the sensor was in the dark and under irradiation by UV light using two diodes having the same maximal optical power at the wavelengths 362 nm (LED1) and 394 nm (LED2).

When noise measurements were done at an operating temperature of 60 °C, the changes in noise level induced by UV light became more promi-nent. The product f∙S(f )/U

2 was changed by a factor 2–3 (Figure 50 and 51) after introducing NO2 gas while the DC resistance was reduced by about 20% only. Additional information on gas detection was acquired by observ-ing some changes in the slope of the noise versus frequency curves for dif-ferent UV wavelengths upon NO2 exposure (Figure 51 and 52). These changes can improve gas detection when an adequate detection algorithm is applied.

Figure 51: Normalized product of frequency f and power spectral density S(f) of voltage fluctuations across a sensor at 60 °C in synthetic air (SA). Voltage noise was measured when the sensor was in the dark and under irradiation by UV light using two diodes having the same maximal optical power at the wavelengths 362 nm (LED1) and 394 nm (LED2).

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Figure 52: Normalized product of frequency f and power spectral density S(f) of voltage fluctuations across a sensor at 60 °C in 15 ppm NO2 diluted in SA. Voltage noise was measured when the sensor was in the dark and under irradiation by UV light using two diodes having the same maximal optical power at the wavelengths 362 nm (LED1) and 394 nm (LED2).

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5. Summary and Conclusions

Indoor air quality is of great importance for the well-being of residents since people spend majority of their times inside buildings or vehicles. Poor in-door air quality causes sick building syndrome, which is defined as the situa-tion where some acute symptoms such as fatigue or headache are observed in a person, who has no specific illness, depending on time spent in a build-ing. There are various reasons that cause sick building syndrome. Among them, volatile organic compounds have a specific importance. Furthermore, monitoring volatile organic compounds can be used for diagnostic purposes through monitoring breath samples, since some of the volatile organic com-pounds are specific markers for some diseases. Thus, sensors to monitor VOCs are an attractive topic for research. In order to produce gas sensors, metal oxides, thiol functionalized Au nanoparticles and graphene combined with TiO2 were studied.

Advanced reactive gas deposition was used for producing NiO sensors that are used to monitor formaldehyde. As-deposited NiO films had average crystallite sizes varying from 2.2 nm to 3.7 nm with a narrow particle size distribution. Furthermore, high porosity of the films, with ~154.7 m2/g spe-cific surface area, provided good conditions for gas sensing.

The NiO films showed selective response to formaldehyde compared to ethanol and methane. The sensors, which were tested at 200°C, were able to monitor 0.5 ppm of formaldehyde both with noise and resistance changes. Furthermore, a comparison of two different sensing methods gave close, consistent results with different agents.

InSnO films prepared with DC magnetron sputtering have varying In/(In+Sn) ratios starting from 3% to 75%. The samples were annealed at 400 °C, showing that if the In/(In+Sn) ratio was below 20%, the films had a tendency to have SnO2 crystalline structure. Similarly, at 75%, they formed the crystalline structure of In2O3. However, the SEM images showed that the grains are formed as clusters of crystals since the grain size was larger than the average crystallite sizes of the samples. Different analysis methods, EDX and XPS, gave consistent results on elemental ratios within small experi-mental error rates.

InSnO films were used to monitor acetaldehyde, which is a biological marker used to detect breast cancer early as well as a VOC. The response of the sensors varied with their In/(In+Sn) ratios. Samples having less than 20% In/(In+Sn) had higher response but a local maximum occurred where In and

67

Sn ratios were close. Furthermore, at 185°C down to 200 ppb of acetalde-hyde detection was achieved.

Dispersed Au NPs were produced by using advanced gas deposition. The Au NPs had ~10 nm crystallite and grain size, showing that each grain is single crystal. The Au NPs were functionalized with dodecanethiol and 2-mercaptobenzaxozale. After functionalization, Au grains formed web-like structures on the Si substrate, having the properties of a Schottky diode.

Dodecanethiol capped AuNP sensors were used to monitor acetaldehyde and formaldehyde. Acetaldehyde was monitored using resistance changes at various bias points of the sensor. The results showed the sensors’ selective response to acetaldehyde compared to ethanol and ethylbenzene. The re-sponse to acetaldehyde increased at high relative humidity at 70%, making the sensor useful for breath analysis.

2-mercaptobenzaxozale capped Au NPs were used to monitor formalde-hyde by using fluctuation enhanced sensing. Distinguishable noise response to formaldehyde at 1.5 ppm was obtained, compared to 50 ppm ethanol. Thus, a novel sensor manufacturing method has combined with fluctuation enhanced sensing, which can lead to further diagnostic applications.

Sputtering was used to produce VO2 thin films, which also have thermo-chromics properties. The O2 ratios during deposition affected their response to formaldehyde and acetaldehyde. However, one major drawback is that VO2 is not thermally stable and formed V2O5 in some samples.

Lastly, graphene/TiO2 samples were produced by using doctor-blading, which is a very simple method. TiO2 has photocatalytic properties, which can be combined in gas sensing. The sensors gave promising results with NO2 when fluctuation enhanced gas sensing was used with further data anal-ysis, such as principal component analysis (PCA).

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6. Swedish Summary

Då många mäniskor tillbringar merparten av sin tid inomhus så har inomhus-luftens kvalitet har stor betydelse för hur vi mår. Dålig luft orsakar vad som brukar kallas ”sick building syndrom” vilket definieras som att mäniskor uppvisar symptom som huvudvärk, trötthet och illamående i en byggnad utan att egentligen vara sjuka. Det finns olika orsaker till ”sick building syndrom” där flyktiga organiska förreningar (VOC) är en av dem. Utöver att orsaka dessa syndrom så kan den här typen av förreningar vara markörer för olika sjukdomar, exempelvis bröstcancer. Därför är det viktigt att forska kring och utveckla nya sensorer som kan detektera dem. I den här avhand-lingen har följande material används för att utveckla sensorer för detektion av VOCs: metaloxider, tiol funktionaliserade guldnanopartiklar samt TiO2 kombinerat med grafen.

En avancerad gasförångare använders för att producera tunnfilms NiO sensorer som kan användas för att detektera formaldehyd. Före värmebe-handling hade NiO filmens krystaliter en diameter på 2.2 nm till 3.7 nm med en snäv storleksdistribution. Filmerna har hög ytarea, ~154.7 m2/g, på grund av sin porositet och är därför optimala för gassensorer.

NiO-sensorerna var selektiva för detektion av formaldehyd och gav lågt utslag för etanol och metan i förhålland till formaldehyd. Testen utfördes vid 200°C och kunde detektera 0.5 ppm av formaldehyd och utfördes med meto-derna resistans och ”fluctation-enhanced sensing” (brus). De två metoderna gav liknande resultat.

InSnO tunnfilmer tillverkades med DC magnetron förstoftning (sput-tering) och hade olika In/(In+Sn) sammansättningar, från 3% till 75%. Pro-verna värmebehandlades vid 400°C. Prover med mindre än 20% hade SnO2 kristallstruktur medans prover med mer än 75% hade In2O3 struktur. Dock så visade SEM att kornen är bildade som kluster av kristaller, eftersom korn-storleken var högre än de genomsnittliga kristallitdiameter av proven. Olika analysmetoder, EDX och XPS, gav konsekventa resultat på elementära för-hållanden i små experimentella felprocent.

InSnO filmer användes för att detektera acetaldehyd, som är en biologisk markör för tidigt upptäcka bröstcancer samt en VOC. Svaret från sensorerna varierade med sina I /(I+Sn) förhållanden, prover har mindre än 20 % I/(I+ Sn) hade högre svar, men en lokal maxima inträffade var i och Sn förhållan-dena var nära. Vidare vid 185°C ned till 200 ppb acetaldehyd detektering uppnås.

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Spridda Au nanopartiklar producerades via hjälp av avancerad gasförång-are. Au nanopartiklar hade ~10 nm kristallitdiameter och kornstorlek, vilket visar att varje korn är enda kristall. Au nanopartiklar var funktionaliseras med dodekantiol och 2-mercaptobenzaxozale. Efter funktionalisering, Au nanopartiklar hade webbliknande strukturer på Si substrat med egenskaper Schottkydiod.

Dodekantiol funtionaliserade AuNP sensorer användes för att detektera acetaldehyd och formaldehyd. Acetaldehyd detekteras med användning av motståndsförändringar vid olika punkter i sensor förspänning. Resultaten visade att sensorerna selektiv respons på acetaldehyd jämfört med etanol och etylbensen. Svaret på acetaldehyd har ökat i hög relativ fuktighet på 70%, vilket gör sensorn användbar för analys av utandningsluft.

2-mercaptobenzaxozale utjämnade Au NP användes för att övervaka for-maldehyd med fluktuationer förbättrad avkänning. Urskiljbart buller svar på formaldehyd på 1,5 ppm har erhållits jämfört med 50 ppm etanol. Således har en ny sensor tillverkningsmetod i kombination med fluktuationer förbätt-rad avkänning som kan leda till ytterligare diagnostiska tillämpningar.

Förstoftning har använts för att producera VO2 tunnfilmer, vilka även har thermochromics egenskaper. The O2 förhållanden under nedfall har påverkat deras svar på formaldehyd och acetaldehyd. Men det är en stor nackdel att VO2 inte är termiskt stabil och bildade V2O5 i vissa prover.

Slutligen var grafen/TiO2 prover som framställts genom användning av doctor-blading som är en mycket enkel metod. TiO2 har fotokatalytiska egenskaper, som kan kombineras i gas detektion. Sensorerna gav resultat med NO2 när brus används med ytterligare dataanalys, såsom principalkom-ponentanalys (PCA).

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Acknowledgements

First off all, I am proud and happy with the results of my doctoral studies after such a long time. It would not have been possible to complete this task without the help of my supervisor Professor Gunnar Niklasson who guided me during my studies, especially with his theoretical knowledge. His calm attitude and working discipline have given me inspiration.

I am also grateful to my co-supervisor Professor Claes-Göran Granqvist for not only being a good mentor but also a great host. I have enjoyed his and Martha Garrett’s hospitality at their house parties which were fun to attend.

I thank my co-supervisor Professor Lars Österlund for his help during my studies and I am grateful for his help, especially in experimental issues. His help in finding contacts has been very useful in this time.

Furthermore, I thank Professor Janusz Smulko and Professor Radu Ionescu for co-operating in projects. It was joyful to work with them, and I am very happy with results achieved so far. I had a great time in Poland with Janusz’s guidance on Polish culture. In addition, I thank Tesfalem Welearegay for his hard work and co-operation.

During this time, I have received great help from post-doctoral fellows Dr. Zareh Topalian and Dr. Pia Lansåker. I learned the basics of lab tools in FTF from them at the beginning of my studies. It was a great pleasure to chat with them at fika times.

I cannot continue further without mentioning my Turkish family in Uppsala. Esat Pehlivan and Ilknur Bayrak Pehlivan started to help me even before I came to Sweden. I wish they have further joyful times with UmutP. Other members of my Turkish family, Arzu Graneberg and Yagmur Yagdiran Alatli were always there when I needed them. I wish them a happy life. Ser-kan Akansel was not only a colleague but a joyful friend who listened to me a lot. Lastly, I thank to Selcuk Yaldir, Fatma Gulen Yaldir, and Esra Bayoglu Flener for the times we had.

I’m thankful to a close friend, whom I met here, from my hometown, Ozden Baltekin (Ozzy) for all the discussions we had on science, politics and cur-

71

current events. It was fun to be in Sputnik Kollektiv for Katushka. I’d like to mention Ninnie Abrahamsson for joining him and keeping a company with us as well. I should also mention another Turkish friend Burak Aktekin for his friendship, especially when I had difficult times, and joyful chats.

My friends and colleagues in FTF made me feel special during my studies. It was such a great working environment and I was always happy whenever we gathered for board games or during Christmas dinners at FTF. I thank Bozhidar Stefanov for the chats when we shared our office and I wish him success. I’m thankful to Delphine Lebrun, especially for board games and being a nice host. I feel lucky that I worked with my colleagues in FTF. I always had fun with Rui Tao Wen, Jose Montero, Miguel Arvizu, Carlos Triana, Andreas Mattson, Ji Yuxia, Mikael Andersson, Daniel Hedlund, Annica Nilsson and Sofia Kontos. I’m also grateful to Bengt Götesson for his help, not only in work but also for practice in Swedish.

I had lots of fun with my friends in BMC, Alexandre Barozzo with whom I ran a marathon, Klev Diamanti and Tommy Athan, with whom we travelled through the whole of Greece (with Alex). In addition, Petar Kovachev, Miha Purg, Ania Pabis, Showgy Maayeh and Fabio Freitag; it was fun to hang out with you at ICM beer clubs.

I should mention the twins Juliana and Gabriela Lundholm for the good times we had. It was a joy to cook with you together. I’m thankful to Elisabeth Dahlkvist for being a good friend for years.

Finally I leave my last words for my family (in Turkish):

Cesaretimi kaybettigim anlarda daima yanimda olan ve beni bugunlere kadar yetistiren annem ve babam, size minnetarim. Ebeveyn olmanin yanisira sizinle bir arkadas gibi iletisim kurdugum icin kendimi cok sansli hissediyorum. Ikiz kardesim Ulas, seninle daima aramizda özel bir bag olduguna inandim. Cesaretin ve kararliligin bana daima ilham verdi. Cok sevdigim Buse, aramizdaki yas farkina ragmen seninle de özel bir bagimiz var, uzakta olsan da seninle oynadigimiz oyunlar, konusmalarimiz hep eglenceli oldu. Ben ögrencilige veda ederken, sana universite yasaminda basarilar diliyorum. Hepiniz iyi ki varsiniz!

Umut Çindemir

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