siddarth chandrasekaran “advanced spectroscopy in chemistry” “advanced spectroscopy in...

Siddarth ChandrasekaranSiddarth Chandrasekaran “ “Advanced Spectroscopy in Chemistry”Advanced Spectroscopy in Chemistry”

University of LeipzigUniversity of Leipzig18/12/200918/12/2009

Module: Spectroscopy of Fluid Interfaces (13-122-0412)

IndexIndex

Understanding MIES spectra

Data Analysis Linear Combination Singular Value Decomposition

Applications of Data Analysis

Conclusion

18/12/09Spectroscopy of Fluid Interfaces 2

18/12/09Spectroscopy of Fluid Interfaces3

Understanding MIES spectra Understanding MIES spectra

Max. B.E. depends on source He 23S – 19.8 eV He 21S – 20.6 eV

Low penetration, outermost orbitals interact

Information about spin-orbit coupling, too


Kim et al, J. Phys. Chem. B 107, (2003), 592-596

Understanding MIES spectraUnderstanding MIES spectra

Chemical shift can be observed

For example: lowering of Binding Energy, because of neighbors

Useful for characterizing surface reactions



Chemical ShiftChemical Shift

Sum of work function of surface and Binding energy of 5p1/2 for adsorbed Xe constant



Data AnalysisData Analysis

What Data? MIES spectra Important Prerequisite: Good spectra, so try to

record best possible spectra

Why Analysis? Improve quality of data

varies from simple baseline corrections to complicated mathematical calculations


Data analysisData analysis

Helps to extract hidden (latent) information, but cannot create information

Multicomponent mixtures - Fraction of species present on the surface – QUANTITATIVE Analysis

In this talk focus is on Linear Combination method and Singular Value Decomposition (SVD)


Linear Combination MethodLinear Combination Method

When liquids with similar surface tensions are mixed Smixture = a1Sspecies,1+a2Sspecies,2+….+anSspecies,n

S - spectra a – surface fraction of the species

Only possible in the case of physical homogeneous (macroscopically homogeneous) mixtures No orientational effects No large domain formations

We need to know the pure spectra of the components


Linear combination MethodLinear combination Method

Reference Spectra


H. Morgner* & M. Wulf , J. of Elec. Spec. and Rel. Phen. 74 (1995) 91-97

Linear Combination MethodLinear Combination Method


H. Morgner et aI. , Molecular Physics, 73, (1991), No. 6, 1295-1306

Smix = aBA* SBA + aFA* SFA

aBA + aFA = 1

Inference: Linear combination of spectra are very effective in a few simple cases

Example where linear combination not Example where linear combination not possiblepossible

The reaction has at least two intermediates with variable conc.'s which couldn’t be identified in this paper


Lescop et al, Surface Science 565, (2004), 223-231

Why Singular Value Decomposition Why Singular Value Decomposition (SVD)(SVD)

When linear combination of individual spectra not enough to reproduce the total spectra


When & what SVD?When & what SVD?

What information can we get from SVD No. of components & their compositions Spectra of unknown components possible

Pure spectra of one species can be obtained from mixture of species, especially useful when Single monolayer spectra cannot be recorded Orientational effects or chemical reactions


Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)

Handy mathematical technique that has application to many problems

Given any mn matrix A, algorithm to find matrices U, V, and W such that

A = U W VT

U is mn and orthonormal

W is nn and diagonal

V is nn and orthonormal


SVDSVD

code used in Matlab [U,W,V]=svd(A,0);

Matrix A contains the spectra recorded


T

1

00

00

00

VUA

nw

w

T

1

00

00

00

VUA

nw

w

SVD on 27 different spectraSVD on 27 different spectra(optical spectroscopy)(optical spectroscopy)

SVD to be performed on the above spectra


Performed SVD to get U,W & V matrix

W- MatrixW- Matrix


The W-Matrix obtained by using the SVD algorithm

The diagonal elements in percentage values to highlight the importance of the value

Choice of no. of componentsChoice of no. of components

Red and Green line overlaps almost perfectly

Two components not enough to reproduce spectra


U- Matrix for first three componentsU- Matrix for first three components

The columns of the U-matrix have no physical significance. Negative peaks

Linear combinations of the elements of the U-Matrix can represent spectra


Obtaining spectra of unknown Obtaining spectra of unknown componentscomponents

Lets consider three species system

Smixture = aαSspecies α+aβSspecies β+aγSspeciesγ

aα+ aβ + aγ = 1

In ideal case we know Sspecies α & Sspecies β

Sspecies γ = a1B1 + a2B2 + a3B3

B1, B2, & B3 are basis of the U matix


PROBLEM : Pure spectra of solute (e.g.: salt) cannot be observed in liquid state

Earlier Methods used Difference spectra Ssalt = S salt+solvent – a * Ssolvent

S is spectra & a is scaling factor (both are input parameters)

Peak areas fitting by ratio of salt/solvent Intrinsic knowledge of intensity, position and linewidth of

solvent spectra Lots of assumptions


Determination of pure spectra of TBAIDetermination of pure spectra of TBAI

J. Oberbrodhage*,J. of Elec. Spec. and Rel. Phen.107 (2000) 231–238


MIE reference data of the pure solvents formamide and hydroxy-propionitrile.



Three base spectra sufficient

We expect three species – FA, TBAI & HPN




Results obtained by SVD comparable with that by difference spectra method

Greater sensitivity because of lower noise




MIES used to evaluate the surface fraction of each of the species


Determination of pure spectraDetermination of pure spectra


Determination of spectra of unknown Determination of spectra of unknown component component

Mixture of Pentadecane (PD) and Formamide (FA)

The linear combination using only two species was not enough and hence need for third component


H. Morgner*, J. Oberbrodhage, J. of Elec. Spec. and Rel. Phen. 87 (1997) 9-18

Third component spectra similar to that of a standing alkane – orientation of the alkane (PD) can be seen




Percentage contribution of each species is shown in the graph to the left




ConclusionConclusion

MIES – Surface specific

Data Analysis techniques like SVD & Linear Combinations are tools to extract hidden information

SVD is rather simple when we have acquired good quality spectra But there is a need for good computational

abilities and high speed computers


THANK YOU for your THANK YOU for your attentionattention


Metastables Electron Emission Microscopy (MEEM)MEEM)

Controlling Helium beam diameter difficult

Area from which electrons are abstracted can be controlled – spatial resolution

Surface electron can be mapped non-destructively


Harada et al*, Nature 372 (1994) 657-659

siddarth chandrasekaran “advanced spectroscopy in chemistry” “advanced spectroscopy in...

Documents