Applications of XANES and EXAFS to nanoparticle studiesable to provide structural information on NPs structure : • Diffraction methods are not applicable in case of small NPs (

$Page 1: Applications of XANES and EXAFS to nanoparticle studiesable to provide structural information on NPs structure : • Diffraction methods are not applicable in case of small NPs ($
Office of Institutional Research, Planning & Effectiveness

Applications of XANES and EXAFS to nanoparticle studies

Janis Timoshenko

https://www.bnl.gov/chemistry/SDAN/ Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, NY 11794

XAFS Short Course 2016

https://www.bnl.gov/chemistry/SDAN/


NPs properties can be very different from those of bulk materials: • Large surface-to-volume

ration • Structure relaxation • Different electronic

structure

Properties of NPs can be tailored by changing • NPs size • NPs shape • NPs composition • Intra-particle distribution

of different atoms • NPs support material • Ligands & adsorbates • ...

It is important to correlate atomic structure of NPs with their properties!

Why nanoparticles?

11/9/2016 XAFS short course 2016 2

J. Kleis, et al, Cat. Letters 141, 1067 (2011)

Alaqad and Saleh, J Environ Anal Toxicol 2016, 6:4


Why XAS?


Bulk

Nano

Bulk

Nano

TEM pictures of Thiol-protected Pd NPs:

XRD spectra of nanosized tungstates:

Sun et al, Langmuir, Vol. 22, No. 2, 2006

Anspoks et al, Solid State Commun. 183 (2014) 22.

XAS – one of a very few experimental techniques that are able to provide structural information on NPs structure : • Diffraction methods are not

applicable in case of small NPs ( < 3 - 4 nm)

• Electron-microscopy does not provide sufficient sensitivity to resolve structure in details

• Total scattering/PDF analysis provides information, complimentary to XAS, but lacks chemical sensitivity.


Why XAS?


Structure of NPs can change in situ: it is important therefore to study NPs in reaction conditions! Due to high-penetration depth of X-rays, complex sample environments can be used in synchrotron XAS experiments. XAS thus is and ideal technique for in situ and operando investigations Time-dependent XAS measurements are now enabled by high intensities of modern synchrotron sources.


XANES and EXAFS


EXAFS analysis: • Nearest neighbors:

types and numbers

• Bond length distributions

• Disorder effects

• Atomic dynamics

XANES analysis: • Oxidation state of

absorbing atom

• Local electronic structure

• Local symmetry

6900 7200 7500 7800 8100 8400

-1.0

-0.5

0.0

µ(E)

Energy E (eV)

EXAFS XANES hν

Office of Institutional Research, Planning & Effectiveness XAS studies of nanoparticles


EXAFS



EXAFS in NPs


W Co O

Bulk

Nano • NPs large surface-to-volume ratio →

reduced coordination numbers • Large disorder → large MSRD factors,

reduction of EXAFS amplitude • Limited contribution from distant

shells • Structure relaxation → changes in

interatomic distances • Asymmetric bond lengths

distributions

J. Timoshenko, Phys. Status Solidi A 212 (2015) 265-273.


Size & shape

A.I. Frenkel et al J. Phys. Chem. B 105, 12689 (2001). J. Matos et al, Phys. Chem. Chem. Phys., 14 11457 (2012)

S2

S3

S4

S1

B. R. Cuenya et al, J. Am. Chem. Soc. 132, 8747 (2010) B. Roldan Cuenya, AIF, et al, Phys. Rev. B. 82, 155450 (2010)

One of the quantities, commonly accessible from EXAFS spectra, is average coordination number of absorbing atoms. Due to size effect, coordination numbers for nanoparticles are typically smaller than for bulk materials, and are directly linked to nanoparticle size and shape. NPs of different shapes will have different surface–to-volume ratios, hence different average coordination numbers and EXAFS spectra.



Size & shape

Relation between NPs size and average coordination numbers can be obtained either analytically or numerically by modelling 3D geometries of clusters of different sizes and shapes


Cuboctahedron

Icosahedron

13, 55, 147, 309, 561…

13, 55, 147, 309, 561…

13, 38, 79, 140, 225, 338, 483, 664…

Truncated octahedron

HCP

13, 26, 57, 89, 153, 214, 323, 421, 587

D. Glasner, A. Frenkel, AIP Conf. Proc. 882, 746-748 (2007).

Truncated (111) Cuboctahedron

10, 37, 92, 185, 326, 525,…

Truncated (001) Cuboctahedron

9, 34, 86, 175, 311, 504…

( )3111510

1352423

2co1 +++

++=

LLLLLLn

bulk1

3

1 161

431 n

Rr

Rrn

+

−≈

S. Calvin, et al, JAP 778, 94 (2003)

Montejano-Carrizales, et al, Nanostruct. Mater. 8, 269 (1997)


Size & shape

Information on coordination number for the 1st coordination shell only is not sufficient to find the size and shape of NPs unambiguosly. Average coordination numbers for more distant coordination shells are more challenging to extract from EXAFS analysis, but they usually are more sensitive to size and shape effects.


0 100 200 300 400 500 600 7005

6

7

8

9

10

11

12

CO309 or TO225 or IH147

EXAFSN1

CO55 or TO38

IH13

CO13 = TO13

Bulk (fcc) N1 = 12

Coordination numbers in CO, IH and TO clusters

Number of Atoms

CO IH TO

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

2

4

6

8

10

12

14

16

18

20

22

24Bulk 3NN and 5NN

Bulk 2NN

Bulk 1NN and 4NN

Coor

dina

tion

num

ber

L

1NN 2NN 3NN 4NN 5NN

0 10 20 30 40 50 60 70 80

Cluster diameter, Å

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

2

4

6

8

10

12

14

16

18

20

22

24

No overlap between 1NN and 2NN

1NN 2NN 3NN 4NN 5NN

Coor

dina

tion

num

ber

L

0 10 20 30 40 50 60 70 80

Bulk 2NN

Bulk 1NN and 4NN

Bulk 3NN and 5NN

Cluster diameter, Å

Spherical Hemi-spherical


Crystallographic structure

J.A Varnell et al, Nature Commun. 7 (2016).

L. Menard et al, J. Phys. Chem. B. 110, 14564(2006)

Knowledge of coordination numbers, especially of those for more distant coordination shells and of multiple-scattering effects, allows one also to distinguish between different crystallographical structures, e.g., between close-packed and icosohedral structures, between fcc and bcc structures.



Order in heterometallic NPs

Chemical sensitivity of XAS method allows one to determine the ordering and distribution of atoms of different types with the nanoparticles of bimetallic (or more complex) materials. Again, coordination numbers obtained from EXAFS analysis can be used for this purpose. For such kind of analysis typically XAS data, obtained at absorption edges of both metals, are necessary.

A B

or + or

BMAM nn <

Random Intra-particle segregation

Inter-particle segregation

B

A

AB

AA

xx

nn

= 0=ABn

A.Frenkel, Z. Kristallogr. 222, 605 (2007) A.Frenkel, Chem. Soc. Rev. 41, 8163 (2012)


A

ABAB N

Nn =

BMBAMAMM nxnxn +=

Partial coordination number: average number of specific bonds per atom Total average coordination number (can be used for size analysis)

A

AAAA N

Nn 2=


Ordering parameter

Quantitavely, order in bimetallic alloy can be characterized by ordering parameter α. Positive (α >0) indicates tendency to clustering (segregation), while α = 0 corresponds to random arrangements of A and B atoms.

A.Frenkel, Z. Kristallogr. 222, 605 (2007) A.Frenkel, Chem. Soc. Rev. 41, 8163 (2012)

𝛼𝛼 = 1 −𝑛𝑛𝐴𝐴𝐴𝐴/𝑛𝑛𝐴𝐴𝐴𝐴

𝑥𝑥𝐴𝐴


A B

A A

A

A A A

A B

B

B B

B B B

B A

A A

A A A

A

B B

B B

B

B

B A

A A A

A A

B B B B

B

031

≤≤− ABα

For 3D fcc bulk material:

𝛼𝛼𝐴𝐴𝐴𝐴 = −1 𝛼𝛼𝐴𝐴𝐴𝐴 = −1

𝛼𝛼𝐴𝐴𝐴𝐴 = 1 𝛼𝛼𝐴𝐴𝐴𝐴 = 1

𝛼𝛼𝐴𝐴𝐴𝐴 = 0 𝛼𝛼𝐴𝐴𝐴𝐴 = 0


XAFS is sensitive to archtiecture of bimetallic NPs, and can e.g., distinguish between core-shell and random alloy structure models (which exhibit quite different catalytic properties).

S. Alayoglu et al, ACS Nano 3 3127 (2009)

Ru@Pt: 4.0nm Ru@Pt: 4.0nm

EXAFS Modeling

Bimetallic NPs: core-shell vs. random alloy



Simultaneous analysis of Multiple-edge EXAFS data provides composition and structure information on a system with multiple elements.

W. Du et al, J. Mater. Chem. 21 8887 (2011)

Ternary alloy structure



“A” edge

“B” edge

>1000 eV

Energy (ev)

As K

Au L3

µ(E

)

Au-As Au-Au As

-In

As-In, Au-As, Au-Au …

A-X1, A-X2, …

B-Y1, B-Y2, …

L. Menard et al, Phys. Rev. B 80, 064111 (2009) J. Liu et al, Chem. Mater (in press, 2016)

Overlapping edges

If no overlap:

Otherwise:

Similar situation, e.g., with Pt L3 and Au L3 edges, Pt L3 and Ir L3 edges,

The analysis is still possible!



Bond lengths

Due to surface tension, the bond lengths for atoms at metallic NPs surface can be significantly shorter than in bulk. EXAFS probes configuration average interatomic distance, averaged over all absorbing atoms (both at the surface and in the core of NPs). Thus the bond length, as estimated from EXAFS, will also be sensitive to surface-to-volume ratio. Changes in interatomic distances with accuracy better than 0.01 Å can usually be detected.


W. Huang, et al, Nature Materials 7, 308 (2008)

S. I. Sanchez et al. J. Am. Chem. Soc. 131 7040 (2009)


Bond lengths

Other factors that can influence the bond lengths in the NPs are • interactions with

ligands

• interactions with the support

• interactions with the adsorbates

• vacancies • ...


A.I. Frenkel, et al, J. Chem. Phys. 123, 184701 (2005).

0 1 2 3 4 5 60.0

0.2

0.4

0.6

0.8

1.0 a)

Au-S

Au-Au

FT M

agni

tude

, Å-3

r, Å

Au foil 6:1 3:1 1:1 1:2 1:3 1:6

0.1 1 100.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

R bulk-

R nano

(Å)

Au/thiol ratio ξ

M.W. Small et al, ACS NANO 6 5583 (2012) J. Chem. Phys. 123, 184701 (2005).

Anspoks et al Phys. Rev. B 86, 174114 (2012)


Dynamics

Damping of EXAFS oscillations is a result of static and thermal disorder. The latter is related to the density of vibrational states. By measuring temperature dependency of disorder factor σ2, one can separate effects of structural and thermal disorder and also estimate the effective bond strength constants.

S. I. Sanchez et al., J. Am. Chem. Soc. 131 7040 (2009)

A. Frenkel, J. Rehr, Phys. Rev. B, 48, 585, (1993).

Disorder:

Einstein model:

Bond Strength:

projected VDOS:



Dynamics

NPs with different sizes, in different atmospheres and on different substrates may exhibit quite different dynamic properties due to changes in static disorder and bond strengths.

S. I. Sanchez et al., J. Am. Chem. Soc. 131 7040 (2009)


Under He

0

0.002

0.004

0.006

0.008

0.01

0.012

0 100 200 300 400 500 600 700 Temperature (K)

MS

RD

(Å2 )

Pt Foil Pt 2.9 nm Pt 1.1 nm Pt 0.9 nm

0

0.002

0.004

0.006

0.008

0.01

0.012

0 100 200 300 400 500 600 700 Temperature (K)

MS

RD

(Å2 )

Pt Foil Pt 2.9 nm Pt 1.1 nm Pt 0.9 nm

Under H2


Static disorder and strain energy

Static disorder can be used to characterize the relaxation of NPs structure, influence of adsorbates and NPs support. Knowing contribution of static disorder and effective bond strength constants, one can estimate the residual strain energy of the nanoparticle.

A. I. Frenkel, et al, Solid State Commun. 99, 67 (1996).

M. Small, et al ACS Nano 6, 5583 (2012)

4 6 8 10 12

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

2.9 nm1.8 nm bulk

Pt/γ-Al2O3 in He Pt/C in H Pt/C in He Pt/γ-Al2O3 in H

Stat

ic di

sord

er in

Pt-P

t (Å2 )

Pt-Pt Coordination number

1.0 nm

222ds σσσ += 2

21 σNkU =

WTVU += )( 2

21

sNkW σ=

Lower disorder for C support and for large sizes



Surface sensitivity

Surface effects are very important for understanding of NPs properties. Differential analysis of EXAFS data allow detection of small changes in the coordination environment of absorbing atoms on the surface of NPs, that would otherwise be undetected with conventional XAS techniques. Such approach can be useful, e.g., to study the influence of adsorbates on the local structure of nanoparticle.

M. Small, et al Phys. Chem. Chem. Phys., 16 26528 (2014)

∆˗EXAFS ≈ 𝑆𝑆02𝑁𝑁𝑠𝑠𝑓𝑓 𝑘𝑘 𝑒𝑒−2𝑟𝑟1′𝜆𝜆 𝑘𝑘 𝑒𝑒

−2𝑘𝑘2σ1′2

𝑘𝑘𝑟𝑟1′2 2𝑘𝑘𝑘𝑘𝑘𝑘 sin 2𝑘𝑘𝑘𝑘1′ + 𝛿𝛿 𝑘𝑘 + 𝜋𝜋

2



Validation of theoretical models

EXAFS data can be used for validation of structure models, obtained in DFT, ab-initio MD or classical MD simulations. By comparing the calculated configuration- and time-averaged EXAFS spectra with experimental data, one can demonstrate the ability of structure models to reproduce the 3D local structure and dynamics of the material.

Structure models, obtained in ab-initio MD simulations for thiol-protected Au NPs, corresponding theoretical EXAFS spectra and their comparison with experimental data

EXAFS spectra for structure models, obtained in classical MD simulations with empirical potentials for Au NPs, and their comparison with experimental data

D. F. Yancey et al, Chem. Science 4, 2912-2921 (2013) J. Timoshenko et al, Cat. Today. (2016), http://dx.doi.org/10.1016/j.cattod.2016.05.049



3D NPs structure via RMC method

EXAFS data can also be used directly to construct a 3D structure model of the nanoparticle using reverse Monte Carlo approach. In the RMC-EXAFS approach a 3D structure model is optimized by random displacements of atoms with the aim to minimize the difference between experimental and calculated configuration-averaged EXAFS data. This approach allow to reconstruct the distribution of atoms within the NPs taking into account contributions of distant coordination shells, multiple-scattering effects and non-Gaussian shapes of bond lengths distributions.

J. Timoshenko et al, Cat. Today. (2016), http://dx.doi.org/10.1016/j.cattod.2016.05.049



Non-homogeneous samples

Often the investigated sample will contain different NPs with different sizes and compositions. Such non-homogeneity of sample may result in significant artifacts in EXAFS analysis. E.g., if sample of bimetallic random alloy NPs consists of NPs, where concentration of metal A differs significantly from particle to particle, the apparent A—A coordination number will be higher than expected for average A concentration in the sample.


Theoretical coordination numbers for clusters of N = 100 atoms, calculated assuming Gaussian compositional distribution:

( )

−−∝ 2

2

2exp)(

c

xxxσ

ρ

Assume that particles are random alloys of the same size (nMM), but have inter-particle compositional disorder:

The apparent coordination number then is:

∫

∫= 1

0

1

0

)(

)()(~

dxxx

dxxnxx

nAA

AA

ρ

ρx – concentration of metal A

A. I. Frenkel, Chem. Soc. Reviews, 41, 8163 (2012)

Atomic % Ir

0 10 20 30 40 50 60 70 80 90 100N

umbe

r of P

artic

les

0

4

8

12

16

20


Non-homogeneous samples

Similarly, if the broad distribution of NPs sizes is present in the sample, EXAFS analyis may yield an overestimated NPs size.


Distribution of NPs cluster order:

The apparent coordination number then is:

A. I. Frenkel, Annu. Rev. Anal. Chem. 4 23 (2011)

Apparent coordination number as a function of the standard deviation of the size distribution.

σ= 0.15 will cause 10-20% error in CN


Mixtures


EXAFS for mixture: 𝜒𝜒 𝑘𝑘 = ∑ 𝜒𝜒𝑖𝑖 𝑘𝑘 𝑤𝑤𝑖𝑖𝑖𝑖 𝑤𝑤𝑖𝑖 - molar fraction of i-th component

A.Frenkel, Z. Kristallogr. 222, 605 (2007)

Common scenario: sample is a mixture of two species: 1) reduced monometallic nanoparticles (fraction w1) and 2)molecular precursors (fraction w2)

Obtained coordination numbers:

𝑛𝑛 𝑃𝑃𝑃𝑃 − 𝑃𝑃𝑃𝑃 = 4.0 ± 0.9 The interpretation depends critically on the independent knowledge of the

state of heterogeneity of the system! Assuming a homogeneous model: ca. four Pd––Pd and two Pd––S bonds per Pd atom → clusters should have fewer than 13 atoms! But TEM measurements indicated much larger clusters (26 Å in diameter)... Assuming a two-component system → 40% of Pd atoms are in clusters, remaining part - Pd-sulfide complexes.


XANES



EXAFS - tool of choice for many structural studies But...

Why XANES?

Analysis gets very complex for heterogeneous systems

Disorder reduces significantly the amplitude of signal

Works well for systems with symmetric, Gaussian like bond-length distributions, but it is often not the case for nanoscale systems

XANES: • Less sensitive

to disorder

• Good for time-resolved studies

• Analysis of heterogeneous systems is well-established

• Good for electronic structure and site symmetry studies, but more complex information can also be extracted

6900 7200 7500 7800 8100 8400

-1.0

-0.5

0.0

µ(E)

Energy E (eV)

EXAFS XANES hν


Valence state

Analysis of XANES data can be used straightforwardly to characterize changes in the electronic state and local symmetry of absorbing atoms. For instance, changes in valence state result in shift of absorption edge.


Belli et al, Solid State Communications 35 355 (1980)


Symmetry

In particular, the K-edge absorption is caused by the transition of a 1 s electron of absorbing atom from its core atomic state to a final unoccupied state. It follows dipole (Δl = ± 1) or quadrupole (Δl = 0, ± 2) selection rules, hence is sensitive to symmetry.


Ti K-edge XANES in PbTiO3

Vedrinskii et al, J. Phys.: Condens. Matter 10 9561 (1998) Jaoen et al, Phys. Rev. B 75, 224115 (2007) Frenkel et al, Phys. Rev. B 71, 024116 (2005). Frenkel et al Phys. Rev. Lett. 99, 215502 (2007). Anspoks et al, Phys. Scr. 89 044002 (2014)

The 2nd pre-peak in Ti K-edge XANES in titanates originates from transitions of Ti 1s electron to the p–d hybrid orbitals (Ti 3d orbital is mixed with the O 2p orbitals due to the Ti displacement from the center of the octahedra. The intensity of the pre-peak is proportional to the squared displacement of the Ti.


Coordination


The XANES region of 1 mM [Cr3+(OH2)6]3+ (aq) and [Cr6+O4]2- (aq) are very different.


Charge transfer

Changes in intensities of NPs XANES features upon temperature / pressure changes can be related to charge transfer processes between NPs, support and adsorbates.


Energy (eV)11564 11566 11568 11570 11572 11574 11576

Nor

mal

ized

inte

nsity

0.4

0.6

0.8

1.0

1.2

1.4

673 K550 K488 K393 K294 K

Energy (eV)11564 11566 11568 11570 11572 11574 11576

Nor

mal

ized

inte

nsity

0.4

0.6

0.8

1.0

1.2

1.4

50,000 ppm 25,000 ppm10,000 ppm400 ppm

Energy (eV)11564 11566 11568 11570 11572 11574 11576

Nor

mal

ized

inte

nsity

0.4

0.6

0.8

1.0

1.2

1.4

50,000 ppm 25,000 ppm10,000 ppm400 ppm

Energy (eV)11564 11566 11568 11570 11572 11574 11576

Nor

mal

ized

inte

nsity

0.4

0.6

0.8

1.0

1.2

1.4

673 K550 K488 K393 K294 K


50,000 ppm H2 400 ppm CO

H2 at 673 K

CO at 673 K


Charge transfer

Changes in intensities of NPs XANES features upon temperature / pressure changes can be related to charge transfer processes between NPs, support and adsorbates. Integral differetial XANES signal may be used to highlight and quantify the differences.




Charge transfer

Changes in intensities of NPs XANES features upon temperature / pressure changes can be related to charge transfer processes between NPs, support and adsorbates. Integral differetial XANES signal may be used to highlight and quantify the differences.



.)(1

),( / PCBTe

APTS Tn +++

= −α

)(),( ),( PCBTPTAPTS ++= θ

Particle- adsorbate

Particle- support

Gas- support


Linear combinations

Commonly investigated sample is a mixture of different species (e.g., of reactants and reaction products). Common approach to XANES analysies relies on linear combination fitting, where the XANES spectrum of unknown material is expressed as linear combination of reference spectra. Relatively short time, required to acquire a single XANES spectrum, makes XANES studies especially attractive for time-resolved investigations of nanomaterial structure. J. A. Rodriguez et al J. Am. Chem Soc. 124 346 (2002)


A. Kuzmin and J. Chaboy, IUCrJ 1 571 (2014)

𝜇𝜇 𝐸𝐸 = �𝜇𝜇𝑖𝑖 𝐸𝐸 𝑤𝑤𝑖𝑖𝑖𝑖

𝑤𝑤𝑖𝑖 - molar fraction of i-th component (fitting paremeter)


Linear combinations

Commonly investigated sample is a mixture of different species (e.g., of reactants and reaction products). Common approach to XANES analysies relies on linear combination fitting, where the XANES spectrum of unknown material is expressed as linear combination of reference spectra. Relatively short time, required to acquire a single XANES spectrum, makes XANES studies especially attractive for time-resolved investigations of nanomaterial structure. J. A. Rodriguez et al J. Am. Chem Soc. 124 346 (2002)


A. Kuzmin and J. Chaboy, IUCrJ 1 571 (2014)

𝜇𝜇 𝐸𝐸 = �𝜇𝜇𝑖𝑖 𝐸𝐸 𝑤𝑤𝑖𝑖𝑖𝑖

𝑤𝑤𝑖𝑖 - molar fraction of i-th component (fitting paremeter)

Isosbestic points


PCA analysis

If the reference compounds and/or their total number are not known, Principial Component analyis can be employed. PCA allows one to obtain minimal linear-independent set of reference spectra, required to fit all experimental data.


= . . A U λ2

λ1

… λN

VT

N data sets, M energy points in each measurement ⇒ data matrix A(N,M)

M×N

M×N

N×N

N×N

Data

Eigenvectors (components)

Eigenvalues (importance

of the components)

Eigenvectors (importance of the

components for individual spectrum)

λ1 > λ2 > … > λN

A. I. Frenkel et al, . Chem. Phys., 116, 9449 (2002). Q. Wang et al J. Chem. Phys. 129, 234502 (2008)


Principial Component Analysis allows one to discriminate the most important contributions to experimental spectra, and, hence, allows one to find the number of different components in the mixture. Identification of components can be done by comparing the PC with spectra of reference materials, or by fitting of XAS spectra, constructud from PC.

Q. Wang et al J. Chem. Phys. 129, 234502 (2008)

Time-resolved XANES of H2 reduction of Ce0.8Cu0.2O2

First Principial Components

Residual for fit with 2 PC

Fit of experimental spectrum with 2 PC

Residual for fit with 3 PC

Fit of experimental spectrum with 3 PC


PCA analysis of XANES data


XANES modelling

Similar approaches can be used for ab-initio XANES modelling as in case of EXAFS modelling. XANES modelling, however, currently usually is less accurate then EXAFS modelling.

J. Timoshenko et al, Phys. Chem. Chem. Phys. 18 19621 (2016)

Comparison of ab-intio Pd K-edge XANES spectra with experimental data for some Pd-containing reference compounds.


J. J. Rehr, et al, Phys. Chem. Chem. Phys. 12 5503 (2010).


XANES modelling

Nevertheless, XANES modelling still can be used for interpretation of experimental spectra. In particular, calculated XANES spectra can be used for linear combination analysis of experimental data, when the experimental spectra for corresponding reference compounds are not available. This approach can help, e.g., to find the placement of dopant atoms in the metallic nanoparticle.

J. Timoshenko et al, Phys. Chem. Chem. Phys. 18 19621 (2016)

Ab-intio XANES spectra for different locations of dopant atom:

Fit of experiment XANES with linear combinations of calculated spectra:



RIXS & HERFD

Electronic structure of absorbing atoms can be probed in even more details using Resonant Inelastic X-ray Scattering and High Energy Resolution Fluoroscence Detection techniques. These methods provides improved sensitivity to changes in electronic structure than conventional XANES, and can be used, e.g., to analyze charge transfer processes due to interaction with adsorbates.

A. Elsen et al, J. Phys. Chem. C 119 25615 (2015)



Combination with other techniques

The complete characterization and understanding of complexity of nanoscale systems is possilble only by combining the power of XAS with the possibilities provided by other experimental techniques (TEM, X-ray scattering, optical spectroscopy techniques, etc.).

Y. Li, et al, Nature Communications, 6, 7583 (2015) S. Zhao, Chem. Cat. Chem 7 3683 (2015)

Combined XAFS/XRD/Raman

Combined XAFS/XRD/DAFS Combined XAFS/DRIFTS


Documents

Applications of XANES and EXAFS to nanoparticle studiesable to provide structural information on NPs structure : • Diffraction methods are not applicable in case of small NPs (