Post on 17-Jun-2020
Electron microscopy for multi-scale porous materials
Part II – Electron tomography in material science
Jeremie Berthonneau*, O. Grauby, A. Baronnet, D. Ferry, D. Chaudanson, F.-J. Ulm, and R.J.-M. Pellenq
* <MSE>², CNRS-MIT Joint Laboratory, Massachusetts Institute of Technology, Civil and Environmental Engineering Department, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
jeremieb@mit.edu
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
[New Yorkers Magazine, 1991]
Why tomography?
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
?¾ A single projection is insufficient to infer the structure of a 3D object
[Yang, E-Tomo introduction, 2016; images: Encyclopedia Britannica, 2008; Martin et al., 2015]
Basics
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Tomography = tomos (part, slices) + graphein (to write)
� Tomography is a method in which a higher dimensional structure is reconstructed from a series of lower dimensional projections (usually by sampling the structure from many different directions)… Projections may be generated from various sources:
Radio-frequency waves
X-rays
Electrons
Ions
Magnetic Resonance Imaging
X-rays Computed Tomography
Electron Tomography
Atomic Probe
Wav
elen
gth
Res
olut
ion
+
+
-
-
[Moldovan et al., ICPMS conf, 2010; * Lucic et al., 2010; **Bals et al., 2013]
History
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
20001971196819481917
First applications in Biology
¾ Fundamental insights into cellular organization and
ultrastructure*
Extension to Nanomaterials
¾ Fundamental insights into solid state and material
science**
Hounsfield & CormackX-ray computed tomography (CT)
De Rosier & KlugET reconstruction of bacteria
The Radon transform: Allows the reconstruction of the volume of an object from its projections
Tomography = tomos (part, slices) + graphein (to write)
Outlines
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
1. Electron tomography principle
2. Image processing and denoising
3. Disordered porous silica glass
4. Organic porosity of source rocks
CINaM facility
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Tomography holder
CCD camera GATAN Ultrascan® 1000XP
Tilt axis
Parallel Electron Beam
Focal planObject projection in2D
+
Jeol JEM-2010 TEM
+60°-60°
0°
Electron Microscopy FacilitySerge Nitsche (IR) & Damien Chaudanson (IE)
¾ Recent (2015) « tunning » to allow for electron tomography
Sample preparation
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
[figures from phD thesis, T. Dahmen, 2015]
Single-tilt Double-tilt Conical-tilt
Reconstruction ease and quality- +
1. Drop deposit
� Deposition of any nanomaterials in solution on a “holey carbon” TEM grid
¾ Cheap and easy but limited to individualized nano-particles
Wide variety of TEM tomography holder allowing for various tilt axis geometries:
Available at CINaM so far
Sample preparation
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
1. Drop deposit
2. Focused Ion Beam (FIB) sectioning
� Extraction of a thin section from any parent sample
¾ Broader range of materials…
[Stardust mission, H. Leroux; Col. with M. Gabié – CP2M, AMU]
3 mm
[*Franck, ed. Springer, 2005; Messaoudi et al., BMC BioInf vol 8, 2007]
2D projections with Δθ = 1°
Source (Electron beam)
y
z
x
ReconstructionAlignmentAcquisition
xy plans
xz plans
yz plans
Analysis
Principle
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
The principle of electron tomography* resides in the “visualization of slices in the context of a rotational tilt axis”
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
AcquisitionParallel
Electron Beam
Focal planObject projection in 2D
Reconstruction
Acquisition
AlignmentBright Field Imaging
� Aperture in the back of the focal plane of the objective lens (direct beam)
� Image resulting from the weakening of the direct beam intensity (~ Beer’s law) by its interaction with the sample
� Mass-thickness and diffraction contrast contribute to the image formation
The electron microscope available at CINaM allows electron tomography only on non-crystalline solids (inability to account for the diffraction contrast)
Resolution
λ𝑒 =ℎ
2𝑚0𝑒𝑉 1 + Τ𝑒𝑉 2𝑚0𝑐2
𝑑 =λ𝑒
2𝑛 sin 𝛼
…depending on the acceleration voltage, 0.05 < d < 0.23 nm
Acquisition
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Tilt axis
Parallel Electron Beam
Focal planObject projection in 2D
θ = -30°θ = -20°
θ = -10° θ = 10°θ = 20°
θ = 30°
Reconstruction
Acquisition
AlignmentBright Field Electron Tomography
� High tilt tomography holder
� Goniometer
� Good microscope alignment (eucentricity)…Limited angular tilt whendealing with FIB thinsections… (~ +/- 40°)
[GATAN Digital Micrograph]
Image alignment
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Reconstruction
Acquisition
Alignment
1. Lateral alignment combining translation correction (cross correlation) and filtering (bandpass/hanning window)
Cro
ss c
orre
latio
n
608.9 nm
639.
6 nmθ = -30° θ = -20° θ = -10°
θ = 10° θ = 20° θ = 30°
[GATAN Digital Micrograph]
Noticeable stage shifts occur during tilting (imperfections of mechanical tilt system) …
¾ Lateral shift (x’y’): need to bring the projections into a common coordinate system
Image alignment
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Translation/Rotation with local minima
639.
6 nm
y y
1. Lateral alignment combining translation correction (cross correlation) and filtering (bandpass/hanning window)
2. Optimized alignment (translation/rotation) using 3D landmarks (local minima) inTomoJ*
608.9 nm 316.9 nm
Reconstruction
Acquisition
Alignment
[*Messaoudi et al., 2007]
Noticeable stage shifts occur during tilting…
¾ Lateral shift (x’y’)
¾ Axial shift: need to optimize the spatial positions of the focus planes
Reconstruction
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Reconstruction
Acquisition
Alignment
[*Messaoudi et al., 2007]
Final step for tomogram computation, can be achieved with different algorithms (WBP, ART, SIRT, etc.)
WBP ART SIRT
¾ Coefficient of determination =
Back projection Vs. Iterative techniques on
noisy projections of phantom data*
1.5 % 25.4 % 25.6 %
Reconstruction
The reconstruction of the volume in 3D is based on the sum of the projected 2D images (tilt series), therefore the number of images
acquired is determinant … beware of the missing wedge !
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Acquisition
Alignment
Treatment
Reconstruction
Grey level projections according to a single
direction*
80 projections (-/+ 80° with Δθ = 2°)
60 projections (-/+60° with Δθ = 2°)
[*McIntosh et al., 2005]Original image
Outlines
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
1. Electron tomography principle
2. Image processing and denoising
3. Disordered porous silica glass
4. Organic porosity of source rocks
� The reconstructed tomograms usually have three majors defects:a) Variable grey level lines in the xy plansb) Smears at θmin and θmax due to the “missing wedge”c) The features are elongated according to the z-direction
¾ Denoising, filtering, and elongation correction using dedicated software
x
z
ab
110
nm
c
Main artifacts
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Tomogram denoising1. Suppression of the background noise within the tomogram
[D. Lottin, PhD thesis – CINaM/CNRS, 2013; *IDDN.FR.001.380022.000.RP.2011.000.31235]
Background suppression
608.9 nm
639.
6 nm
� Denoising of the background: homogenization of the average gray level and suppression of “hot spots”
� Superposition of experimental and calculated projections over the angular range
y
Alignment Reconstruction (SIRT)
y
200 nm
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Tomogram filtering1. Suppression of the background noise within the tomogram
2. Control of the reconstruction fidelity with respect to the object
¾ Optimal gray level thresholding by superposition
[D. Lottin, PhD thesis – CINaM/CNRS, 2013; *IDDN.FR.001.380022.000.RP.2011.000.31235]
1. Suppression of the background noise within the tomogram
2. Control of the reconstruction fidelity with respect to the object
3. Suppression of missing wedge artefacts: elongation correction*
¾ Limited specimen tilting range gives rise to a region in the Fourier space of the reconstructed object where experimental data are unavailable = missing wedge
[*Kovacik et al., J Structural Bio, vol. 186, 2014]
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Elongation correction
Geometric area of available data
Impulse response of Ω2showing the main artefacts due to the missing data
Fourier transformation
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Elongation correction
¾ Fourier Angular Filter*Damping of the sharp transition of the non-zero
data region to the zero-filled missing wedge region in the Fourier space
[*Kovacik et al., J Structural Bio, vol. 186, 2014]
1. Suppression of the background noise within the tomogram
2. Control of the reconstruction fidelity with respect to the object
3. Suppression of missing wedge artefacts: elongation correction*
Intensity map of the difference between the impulse response before (Ω2) and after filteringa. Suppression of the side rays (1 and 2)b. Suppression of the side minima in x (3
and 4)c. Reduction of the magnitude of central
peak
¾ Comparison before versus after denoising, filtering, and elongation correction
y
z
110
nm50
nm
[n.b. color of the pore phase was changed for clarity]
Treatment’s result
Outlines
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
1. Electron tomography principle
2. Image processing and denoising
3. Disordered porous silica glass
4. Organic porosity of source rocks
Why does it matter?
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
IUPAC
d
[Foam structure from Subramanian et al., 2013]
Pore size, d
microporous mesoporous macroporous2 nm 50 nm
Quantitative knowledge of the 3D arrangement (morphology + topology) of pore networks is crucial to understand and predict the transport and mechanical properties
Morphology� Pore size distribution (PSD)
� Pore volume, Vp� Specific surface area, As
Topology� Tortuosity
� Connectivity� Percolation threshold
+
¾ Evaluation of the potential of Electron Tomography to realistically describe the 3D arrangement (morphology) of a disordered mesoporous solid
𝜑 =𝑉𝑝𝑉𝑡
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Vycor ® porous glass[*Off lattice reconstruction of Vycor from Pellenq et al., 2001; Data set Vycor ® type 7930 from Corning Inc.]
Solid density Bulk density Porosity Specific surface area Average pore sizeρ s (g/cm3) ρ (g/cm3) φ (%) A s (m2/g) r H (nm)
Vycor 7930 2.18 1.50 28.0 250.0 4.0
Sample
Porous Vycor Glass (PVG)
� Prepared by phase separation of an alkali borosilicate glass and acid leaching
� Strongly interconnected and almost pure SiO2 skeleton (biphasic media)
¾ Model structure of disordered mesoporous media*
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Morphological characterization[*Gille et al., J. Porous Materials, 2002]
Standard methods allowing morphological characterization of porous networks are:
• Mercury intrusion porosimetry (MIP) for the macropores (> 50 nm)
• Small angle scattering (SAS) for the mesopores (1.5 – 50 nm)
• Gas adsorption isotherms for the micro to macropores (0.4 − 100 nm)
¾ Definition of the pore size distribution densities from these three methods*
Vp = 0.227 cm3/g dp = 7.0 nm lp = 10.6 nm~ cylindrical pores
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Electron tomography
Tilt axis
Parallel Electron Beam
0°
θ = -40°θ = -25°
θ = -10° θ = 10°θ = 25°
θ = 40°
-40° 40°
Tilt series Reconstruction
Treatment
SiO2 skeleton Porous network
Pore size distribution
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
[*Vicente et al., J. Porous Media vol 16, 2013; computation performed using iMorph: http://imorph.fr]
Aperture computation*: the pore size at any given point (P) within the pore phase corresponds to the largest sphere (with aperture radius r from 0.35 to 5.0 nm) that contains P
Ape
rtur
e ra
dius
cla
ss (n
m)
0.0
10.0
5.0
2.5
7.5
210 nm
Vp = 0.115 cm3/g
dp = 3.6 nm
210 nm
Allows a granulometricanalysis of the pore network (PSD)
s p
[*Pellenq & Levitz, Mol Phy, vol. 100, 2002]
� Chord length distribution* = stochastic geometrical tool describing the structural disorder of the pore network, fp(r), and the solid matrix, fs(r)
Chord length distribution
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
𝜑 = 16.8 %
� solid
� pores
210 nm
� The in-pore chord length distribution allows one to estimate the geometric specific surface area (Ssp) from the mean chord length* (<ℓ>)
[*Ioannidou et al., PNAS, vol 113, 2016; **Pellenq & Levitz, Mol Phy, vol. 100, 2002]
𝑆𝑠𝑝 =4𝜑
𝜌𝑠 1 − 𝜑 ℓ
With ρs = 2.18 g/cm3
(solid density of amorphous silica)
Τ𝑆𝑔𝑒𝑜 0.65𝑟𝑐 + 1
¾ Sgeo = 253.3 m2/g which is in good agreement with the specific surface area provided by Corning (As = 250 m2/g)
Geometric surface area
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
We compute Ssp at different rc(cutoff length for the CLD neglecting pores, anfractuosity, and roughness sizes)*
Extrapolation of rc to zero gives Sgeo
[*Pellenq et al., 2001; **Pellenq & Levitz, Mol Phy, vol. 100, 2002; ***Coasne et al., Langmuir, 2010]
Discussion
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Polarizability (a03)
0 5 10 15 20 25 30
SB
ET
(m2 /
g)
60
80
100
120
140
160
180
200
220
Ne N2
Exp. data
GCMC data (this work)
H2
Xe
Kr
Ar
Intrinsic Ssp
1 2 3Dads /�DO
¾ This discrepancy was also documented on similar materials were the underestimation was
evaluated at ~ 20%, in agreement with our observation***
� The surface area may be considered in term of geometry (Sgeo) or specifically to a probing gas (As or Ssp)
Studied through GCMC on off lattice 3D reconstruction*,**
Discussion
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
¾ The main limitation of Electron Tomography resides in the restricted field of view (here only the mesopore network is reconstructed)
� Evaluation of the potential of Electron Tomography to realistically describe the 3D arrangement (morphology) of a disordered mesoporous solid
200 nm
Gas sorption E Tomo
Vp (cm3/g) 0.227* 0.115
rH (nm) 3.5* 1.8
As/Sgeo (m2/g) 195.0** 253.3
[*Gille et al., 2002; **Pellenq & Levitz, 2002]
Outlines
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
1. Electron tomography principle
2. Image processing and denoising
3. Disordered porous silica glass
4. Organic porosity of source rocks
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Why does it matter?[Hanchen, 2014; Louks et al., J Sed Res vol 79, 2009; Obliger et al., J Phys Chem Let, 2016]
How can we make the link between the hydrocarbon recovery from source rocks… and the diffusion mechanisms of alkanes (CnH2n+2) in complex porous structures?
¾ Calls for a multiscale strategy where an accurate characterization of the pore networks is paramount
5 nm
Free volume theory
Surface diffusion
¾ The organic pore network plays a central role but cannot be fully covered by conventional techniques
[Louks et al., J Sed Res vol 79, 2009; Louks et al., AAPG bul vol 96, 2012; Curtis et al., AAPG bul vol 96, 2012]
FIB tomography
X-Ray tomography
Molecular simulation
Fracture pores
Organic matter pores
Interparticle pores
Intraparticle pores
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
A multi-scale porosity…
[scheme modified after Bjørlykke, 1989; *molecular models from Bousige et al., Nature Materials, 2016]
LEF
MAR
Molecular models* showed a large amount of micro-pores and that, for a given ρ, mature kerogens (MAR-K) exhibits slightly larger pores than immature ones (LEF-K)
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
0.1 nmResolution
…Evolving with thermal maturity
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
[scheme modified after Bjørlykke, 1989; *Hubler et al., 2016]
…Evolving with thermal maturity
¾ What is the impact of thermal maturity on the organic meso-pore network ?
CT scans* showed that the Euclidian distance between macro-pores (dexp = 50 nm) and organic phase become consistently shorter with thermal maturity
ANT
HAY
50 nmResolution
MOLECULAR MODELING*
- Thermal maturity affects the micro-porosity of
kerogens- Box sizes of 5 nm
ELECTRON TOMOGRAPHY
- Direct observation of pores (1 < d < 50 nm)- Topology from 3D
reconstructions
X-RAY TOMOGRAPHY- Euclidian distance
between macro-pores and organic phases decreases
with maturity- Spatial resolution: 50 nm
Multiscale
[*Bousige et al., Nature Materials, 2016]
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Experimental approach
Organic-rich source rock samples
(LEF, HAY, MAR)
Kerogens isolated by acid demineralization
method*Gas (N2 and CO2)
adsorption isotherms
Organic matter extracted using dual
beam FIB
3D geometrical characterization
(Electron tomography)
Gas (N2) adsorption isotherms
Objectives:1. Reconstruct the organic porous network at the nano-scale from TEM imaging2. Characterize the 3D volumes and compare with BET results3. Study the evolution of the pore network with respect to thermal maturity4. Provide insights in the features that govern fluid transport
Studied materials
Specific preparation
Methods
[*Suleimenova et al., Fuel vol 135, 2014]
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Experimental approach
LEF is thermally immature (oil-prone) whereas MAR and HAY are thermally overmature (dry gas reservoir)
� Source rocks from three different formations (Lower Eagle Ford, Marcellus, and Haynesville) containing variable amounts of organic material (TOC, wt.%) with different thermal maturities
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Materials[*from Leco TOC and RockEval analysis performed by SDR and Geomark Research LTD]
� N2 adsorption isotherms performed on the source rocks show type IV (MAR/HAY) and type V (LEF) adsorption branches
[*IUPAC classification of isotherms]
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Adsorption isotherms
IV: typical of meso-porous materials
V: porous materials with weak interaction between the adsorbate and adsorbent
Thermal maturity
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Adsorption isotherms
Sample As (m2/g) Vp (10-2 cm3/g) rH (nm)
LEF/K 2.28/20.80 1.36/10.71 23.9/20.6
HAY 7.22 1.06 5.9
MAR/K 26.33/160.89 2.30/13.47 3.5/3.4
¾ Overall, the average pore size* (rH) decreases with thermal maturity as As increases more than Vp
𝑟𝐻 =4𝑉𝑝𝐴𝑠
rH
…very similar to the grounded source rocks…
[*Clarkson et al., Fuel vol 103, 2013; **Wang et al., Energy & Fuels vol 28, 2014]
� As and rH as a function of VR0 in North American* and Chinese** source rocks
¾ Hypothesis: the transition from oil-prone to gas-prone is accompanied with the closure of meso and macro-pores and the formation of meso to micro-pores (r < 3 nm)
Oil-prone
Gas-prone
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Adsorption isotherms
[*collaboration with Jae Jin Kim and Ruarri Day-Stirrat, Shell Technology Center in Houston]
Organics in primarylocation (kerogen)
Migrated hydrocarbons(~bitumen)
LEF: fresh outcrops from the Lower Eagle Ford formation (TX, USA)
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
FIB sampling
Ker
ogen
~Bitu
men
[*collaboration with Jae Jin Kim and Ruarri Day-Stirrat, Shell Technology Center in Houston]
LEF MARHAY
¾ Approach allowing to compare the organic hosted porosity at different thermal maturities (LEF / HAY-MAR) as well as different nature of organic material (kerogen/bitumen)
HCs generation: oil-prone HCs generation: gas-prone
Organic material
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
TEM samples
Organicmeso-pores(2 – 50nm)
Organicmacro-pores
Interparticle porosity
Inclusion
Inclusions
Clay particle
5 nm
[*Bousige et al., Nature Materials, 2016]
Kerogen microstructure*
Organicmicro-pores
(< 2 nm)
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Bright field imaging
Organicmeso-pores(2 – 50nm)
Organicmacro-pores
Interparticle porosity
Inclusion
Inclusions
Clay particle
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Bright field electron tomography
Kerogen mesostructure
Electron tomographyacquisition on the porous
organic matter...
… therefore excluding the inter and intra particle porosity (due to the diffraction contrast)
MAR HAY
HAY
LEF
210 nm
Filtered tomogramsLEF
252 nm
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
210 nm
y
zx
The tomograms evidencesignificantly differentkerogen mesostructures…
Quantitative approach
[*Vicente et al., J. Porous Media vol 16, 2013; computation performed using iMorph: http://imorph.fr]
Aperture computation*: the pore size at any given point (P) within the pore phase corresponds to the largest sphere (with aperture radius r from 0.7 to 15.0 nm) that contains P
Ape
rtur
e ra
dius
cla
ss (n
m)
0.0
10.0
5.0
2.5
7.5
Pore size distribution
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
210 nm
HAY
210 nm
210 nm
y
x
Allows a granulometricanalysis of the pore network (PSD)
Easily visualized on the aperture map*
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
[computation performed using iMorph: http://imorph.fr]
Pore size distribution
¾ Support the hypothesis of closure of meso and macro-pores and formation of meso to micro-pores (cf. BET) with respect to thermal maturity
The volumetric distribution of the aperture diameter is used to obtain the incremental and cumulative distribution… Noticeable
difference between LEF and MAR/HAYrH
¾ Chord length distributions within the porous and solid phases
[computation performed using iMorph: http://imorph.fr]
LEF MARHAY
210 nm252 nm 210 nm
Chord length distribution
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
¾ Confirm the increase of accessible surface area as a function of thermal maturity and the predominance of micro-pores in MAR and HAY with respect to LEF
� The in-pore chord length distribution allows one to estimate the geometric specific surface area (Ssp) from the mean chord length* (<ℓ>)
[*Ioannidou et al., PNAS, vol 113, 2016]
𝑆𝑠𝑝 =4𝜑
𝜌𝑠 1 − 𝜑 ℓ
With 0.8 < ρs < 1.4 g/cm3
Geometric surface area
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Thermal maturity
HAY
[*Pardo-Alonso et al., Procedia Materials Science vol 4, 2014]
� Definition of the minimal geometrical path in a porous media, performed using Fast Marching method* from face to face
𝜏 =λ𝐿
X Y Z
LEF - - 1.45 + 0.27
HAY 1.21 + 0.05 1.38 + 0.10 1.20 + 0.15
MAR 1.71 + 0.16 1.73 + 0.12 1.30 + 0.27
LEF
Tortuosity
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Shortest distance maps from one face of the image to any point in the pores (here z direction)
zz
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
ConnectivityLEF
HAY5.6 vol.% of the organic pores are isolated
94.4 vol.% of the organic pore volume is connected to all faces
One macro-pore accounts for 79.1 vol.% of the organic pore volume and connect the faces in z-direction
� Connectivity is one of the main topological properties
Linked to the notion of path-connected space through a percolation threshold of one voxel (0.35 < χ < 0.42 nm)… regardless of the microporosity
¾ τ and χ will define the regime of hydrocarbon transport*
[*See for instance Obliger et al., J Phys Chem Let, 2016; Falk et al., Nature Com., 2015]
5 nm 40 nm¾ Implement a multiscale transport model that accounts for both the micro and meso
structure of kerogen and allows coupling between diffusion and potential hydrodynamic flow (geometrical contribution of the structure)
Summary
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
MICRO-SCALE• Alkane transport is governed by
diffusion, and diffusion coef. of alkane mixtures follow a free volume theory*
• Transition between flexible and rigid kerogen matrix triggered by thermal maturity**
MESO-SCALE• Progressive closure of meso and macro-
pores and formation of meso to micro-pores (nm size)
• Switch from a disconnected macro-porosity to a connected micro to meso-porosity
[*Obliger et al., J Phys Chem Let, 2016; **Valdenaire, GameChanger Project, 2016]
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Toward carbon neutral energy[*Friedlingstein et al., Nature Geoscience, 2014; **Tao & Clarens, Env. Sci. Technol. vol . 47,2013]
Slow the growth of CO2 emissions (political agreements)
Capture and store CO2 Geological sequestration
Two main solutions
Deep saline aquifers Source rocks**
1990 1995 2000 2005 2010 2015 2020 2025 2030
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
Carbon Capture and Storage[*Tao & Clarens, Env. Sci. Technol. vol . 47,2013]
Marcellus shale formation
¾ Based on this model, the authors* estimate that the Marcellus shale could store between 10.4 and 18.4 billion tones of CO2 between now and 2030 (~ half of the expected total U.S. CO2
emissions from power plants over that time)
Existing fracking wells…(average production life ~ 10 years)
� Computational model* based on historical and projected CH4 production and CH4/CO2sorption equilibria and kinetics
Winter School on Multi-Scale Porous Materials, Marseille Jan. 24, 2017
CO2/CH4 adsorption[*Brochard et al., Langmuir, 2012]
¾ Theoretical study* of the competitive adsorption of CO2 and CH4 in disordered microporous carbon structure (akin of an overmature kerogen) showed…
i) Preferential adsorption of CO2 and desorption of CH4
ii) Differential swelling
5 nm
« Le microscope est un prolongement de l’esprit plutôt que de l’œil » Gaston Bachelard (1884-1962)
Thank you