Siddarth Chandrasekaran “Advanced Spectroscopy in Chemistry” “Advanced Spectroscopy in Chemistry” University of Leipzig 18/12/2009 Module: Spectroscopy.

Slides:



Advertisements
Similar presentations
A Crash Course in Radio Astronomy and Interferometry: 4
Advertisements

Analysis of the Visible Absorption Spectrum of I 2 in Inert Solvents Using a Physical Model Joel Tellinghuisen Department of Chemistry Vanderbilt University.
Linear Algebra Applications in Matlab ME 303. Special Characters and Matlab Functions.
FTP Biostatistics II Model parameter estimations: Confronting models with measurements.
Rutherford Backscattering Spectrometry
SIMPLE MIXTURES THERMODYNAMIC DESCRIPTION OF MIXTURES ARYO ABYOGA A ( ) GERALD MAYO L ( ) LEONARD AGUSTINUS J ( )
S-SENCE Signal processing for chemical sensors Martin Holmberg S-SENCE Applied Physics, Department of Physics and Measurement Technology (IFM) Linköping.
No Data Left Behind Modeling Colorful Compounds in Chemical Equilibria Mike DeVries D. Kwabena Bediako Prof. Douglas A. Vander Griend.
Response Surface Method Principle Component Analysis
Principal Component Analysis
Singular Value Decomposition COS 323. Underconstrained Least Squares What if you have fewer data points than parameters in your function?What if you have.
Uncalibrated Geometry & Stratification Sastry and Yang
Independent Component Analysis (ICA) and Factor Analysis (FA)
Clustering In Large Graphs And Matrices Petros Drineas, Alan Frieze, Ravi Kannan, Santosh Vempala, V. Vinay Presented by Eric Anderson.
X-Ray Microanalysis – Precision and Sensitivity Recall… K-ratio Si = [I SiKα (unknown ) / I SiKα (std.) ] x CF CF relates concentration in std to pure.
Ordinary least squares regression (OLS)
X-ray Photoelectron Spectroscopy —— Application in Phase-switching Device Study Xinyuan Wang A
Atomic Absorption Spectroscopy
Unit 2, Part 3: Characterizing Nanostructure Size Dr. Brian Grady-Lecturer
Infrared spectroscopy of Li(methylamine) n (NH 3 ) m clusters Nitika Bhalla, Luigi Varriale, Nicola Tonge and Andrew Ellis Department of Chemistry University.
Calibration & Curve Fitting
The Study of Chemistry The Molecular Perspective of Chemistry
Chapter 1 Introduction: Matter & Measurement
NUS CS5247 A dimensionality reduction approach to modeling protein flexibility By, By Miguel L. Teodoro, George N. Phillips J* and Lydia E. Kavraki Rice.
Grade 10 Academic Science – Unit Chemistry The following is a set up “Flip Card” to help learn the definitions of the chemistry unit.
Algorithms for a large sparse nonlinear eigenvalue problem Yusaku Yamamoto Dept. of Computational Science & Engineering Nagoya University.
22 Feb 2005AGATA Week1 David Radford ORNL Signal Decomposition Algorithm for GRETINA.
Non Negative Matrix Factorization
Brookhaven Science Associates U.S. Department of Energy MUTAC Review April , 2004, LBNL Target Simulation Roman Samulyak, in collaboration with.
Geok Mei CHONG Master Candidate of Advanced Spectroscopy in Chemistry University of Leipzig, ASC Network 4 th December
Sheng-Fang Huang. 4.0 Basics of Matrices and Vectors Most of our linear systems will consist of two ODEs in two unknown functions y 1 (t), y 2 (t),
Rozhen 2010, June Singular Value Decomposition of images from scanned photographic plates Vasil Kolev Institute of Computer and Communications Systems.
Chemical Kinetics CHAPTER 14
© 2014 Carl Lund, all rights reserved A First Course on Kinetics and Reaction Engineering Class 6.
Distillation ... A Separation Method.
Progress in identification of damping: Energy-based method with incomplete and noisy data Marco Prandina University of Liverpool.
Optical Zeeman Spectroscopy of the (0,0) bands of the B 3  -X 3  and A 3  -X 3  Transitions of Titanium Monoxide, TiO Wilton L. Virgo, Prof. Timothy.
A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds Paris Smaragdis, Madhusudana Shashanka, Bhiksha Raj NIPS 2009.
Distillation... A Separation Method. Background Concepts - Definitions Vapor Pressure – Gas pressure created by the molecules of a liquid which have acquired.
Silver Nyambo Department of Chemistry, Marquette University, Wisconsin Towards a global picture of spin-orbit coupling in the halocarbenes June
Advanced Analytical Chemistry – CHM 6157® Y. CAIFlorida International University Updated on 9/26/2006Chapter 3ICPMS Interference equations Isobaric.
SIMULATION OF THE SPIN-VIBRONIC STRUCTURE IN THE GROUND ELECTRONIC STATE AND EMISSION SPECTRA INTENSITIES FOR CH 3 O RADICAL VADIM L. STAKHURSKY Radiation.
X-Ray Microanalysis – Precision and Sensitivity Recall… wt.fraction I = I SiKα (unknown) / I SiKα (pure std.) K-ratio I = [I SiKα (unknown ) / I SiKα (std.)
A users viewpoint: absorption spectroscopy at a synchrotron Frithjof Nolting.
1 Internal Alignment of VXD3 Overview VXD3 at SLD Observing misalignments with the track data Matrix technique to unfold alignment corrections Comments.
APPLICATION OF KOHLER THEORY: MODELING CLOUD CONDENSATION NUCLEI ACTIVITY Gavin Cornwell, Katherine Nadler, Alex Nguyen, and Steven Schill.
LITERATURE SEARCH ASSIGNMENT A) Properties of diatomic molecules A diatomic molecule is a molecule composed of two atoms. For homonuclear diatomics the.
ENERGY LEVELS OF THE NITRATE RADICAL BELOW 2000 CM -1 Christopher S. Simmons, Takatoshi Ichino and John F. Stanton Molecular Spectroscopy Symposium, June.
Vocabulary. Buoyancy the ability or tendency to float in water or air or some other fluid.
Large-Scale Matrix Factorization with Missing Data under Additional Constraints Kaushik Mitra University of Maryland, College Park, MD Sameer Sheoreyy.
42C.1 Non-Ideal Solutions This development is patterned after that found in Molecular Themodynamics by D. A. McQuarrie and John D. Simon. Consider a molecular.
Central limit theorem - go to web applet. Correlation maps vs. regression maps PNA is a time series of fluctuations in 500 mb heights PNA = 0.25 *
AME 513 Principles of Combustion Lecture 5 Chemical kinetics II – Multistep mechanisms.
Problem Research Hypothesis (proposed solution) Design Experiment Variable and control Record Observations Analyze data Conclusion.
Prentice Hall © 2003Chapter 1 Chapter 1 Introduction: Matter & Measurement CHEMISTRY The Central Science 9th Edition David P. White.
Correction of FTIR data for the effect of temperature variation Peter J. Melling, Remspec Corporation, Charlton MA.
Polarization Dependence in X-ray Spectroscopy and Scattering
Orbit Response Matrix Analysis
Image quality and Performance Characteristics
Motion Segmentation with Missing Data using PowerFactorization & GPCA
Strategies for Eliminating Interferences in Optical Emission Spectroscopy Best practices to optimize your method and correct for interferences to produce.
Application of Independent Component Analysis (ICA) to Beam Diagnosis
Singular Value Decomposition
Parallelization of Sparse Coding & Dictionary Learning
X.1 Principal component analysis
5.4 General Linear Least-Squares
Maths for Signals and Systems Linear Algebra in Engineering Lectures 13 – 14, Tuesday 8th November 2016 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR)
What is the difference between a group and a period?
Marios Mattheakis and Pavlos Protopapas
Hard X-ray Albedo: A Spatial Perspective
Presentation transcript:

Siddarth Chandrasekaran “Advanced Spectroscopy in Chemistry” “Advanced Spectroscopy in Chemistry” University of Leipzig 18/12/2009 Module: Spectroscopy of Fluid Interfaces ( )

Index  Understanding MIES spectra  Data Analysis  Linear Combination  Singular Value Decomposition  Applications of Data Analysis  Conclusion 18/12/09Spectroscopy of Fluid Interfaces2

18/12/09Spectroscopy of Fluid Interfaces3

Understanding MIES spectra  Max. B.E. depends on source  He 2 3 S – 19.8 eV  He 2 1 S – 20.6 eV  Low penetration, outermost orbitals interact  Information about spin-orbit coupling, too 18/12/09Spectroscopy of Fluid Interfaces4 Kim et al, J. Phys. Chem. B 107, (2003),

Understanding MIES spectra  Chemical shift can be observed  For example: lowering of Binding Energy, because of neighbors  Useful for characterizing surface reactions 18/12/09Spectroscopy of Fluid Interfaces5 Kim et al, J. Phys. Chem. B 107, (2003),

Chemical Shift  Sum of work function of surface and Binding energy of 5p 1/2 for adsorbed Xe constant 18/12/09Spectroscopy of Fluid Interfaces6 Kim et al, J. Phys. Chem. B 107, (2003),

18/12/09Spectroscopy of Fluid Interfaces7

Data 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 18/12/09Spectroscopy of Fluid Interfaces8

Data 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) 18/12/09Spectroscopy of Fluid Interfaces9

Linear Combination Method  When liquids with similar surface tensions are mixed  S mixture = a 1 S species,1 +a 2 S species,2 +….+a n S species,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 18/12/09Spectroscopy of Fluid Interfaces10

Linear combination Method  Reference Spectra 18/12/09Spectroscopy of Fluid Interfaces11 H. Morgner* & M. Wulf, J. of Elec. Spec. and Rel. Phen. 74 (1995) 91-97

Linear Combination Method 18/12/09Spectroscopy of Fluid Interfaces12 H. Morgner et aI., Molecular Physics, 73, (1991), No. 6, S mix = a BA * S BA + a FA * S FA a BA + a FA = 1 Inference: Linear combination of spectra are very effective in a few simple cases

Example where linear combination not possible  The reaction has at least two intermediates with variable conc.'s which couldn’t be identified in this paper 18/12/09Spectroscopy of Fluid Interfaces13 Lescop et al, Surface Science 565, (2004),

Why Singular Value Decomposition (SVD)  When linear combination of individual spectra not enough to reproduce the total spectra 18/12/09Spectroscopy of Fluid Interfaces14

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 18/12/09Spectroscopy of Fluid Interfaces15

Singular Value Decomposition (SVD)  Handy mathematical technique that has application to many problems  Given any m  n matrix A, algorithm to find matrices U, V, and W such that A = U W V T U is m  n and orthonormal W is n  n and diagonal V is n  n and orthonormal 18/12/09Spectroscopy of Fluid Interfaces16

SVD  code used in Matlab  [U,W,V]=svd(A,0);  Matrix A contains the spectra recorded 18/12/09Spectroscopy of Fluid Interfaces17

SVD on 27 different spectra (optical spectroscopy) SVD to be performed on the above spectra 18/12/09Spectroscopy of Fluid Interfaces18 Performed SVD to get U,W & V matrix

W- Matrix 18/12/09Spectroscopy of Fluid Interfaces19 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 components  Red and Green line overlaps almost perfectly  Two components not enough to reproduce spectra 18/12/09Spectroscopy of Fluid Interfaces20

18/12/09Spectroscopy of Fluid Interfaces21

U- 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 18/12/09Spectroscopy of Fluid Interfaces22

Obtaining spectra of unknown components  Lets consider three species system  S mixture = a α S species α +a β S species β +a γ S species γ  a α + a β + a γ = 1  In ideal case we know S species α & S species β  S species γ = a 1 B 1 + a 2 B 2 + a 3 B 3  B 1, B 2, & B 3 are basis of the U matix 18/12/09Spectroscopy of Fluid Interfaces23

18/12/09Spectroscopy of Fluid Interfaces24

PROBLEM : Pure spectra of solute (e.g.: salt) cannot be observed in liquid state  Earlier Methods used  Difference spectra S salt = S salt+solvent – a * S solvent  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 18/12/09Spectroscopy of Fluid Interfaces25 Determination of pure spectra of TBAI J. Oberbrodhage*,J. of Elec. Spec. and Rel. Phen.107 (2000) 231–238

Determination of pure spectra of TBAI  MIE reference data of the pure solvents formamide and hydroxy- propionitrile. 18/12/09Spectroscopy of Fluid Interfaces26 J. Oberbrodhage*,J. of Elec. Spec. and Rel. Phen.107 (2000) 231–238

 Three base spectra sufficient  We expect three species – FA, TBAI & HPN 18/12/09Spectroscopy of Fluid Interfaces27 Determination of pure spectra of TBAI J. Oberbrodhage*,J. of Elec. Spec. and Rel. Phen.107 (2000) 231–238

 Results obtained by SVD comparable with that by difference spectra method  Greater sensitivity because of lower noise 18/12/09Spectroscopy of Fluid Interfaces28 Determination of pure spectra of TBAI J. Oberbrodhage*,J. of Elec. Spec. and Rel. Phen.107 (2000) 231–238

 MIES used to evaluate the surface fraction of each of the species 18/12/09Spectroscopy of Fluid Interfaces29 Determination of pure spectra J. Oberbrodhage*,J. of Elec. Spec. and Rel. Phen.107 (2000) 231–238

Determination of spectra of unknown component  Mixture of Pentadecane (PD) and Formamide (FA)  The linear combination using only two species was not enough and hence need for third component 18/12/09Spectroscopy of Fluid Interfaces30 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 18/12/09Spectroscopy of Fluid Interfaces31 Determination of spectra of unknown component H. Morgner*, J. Oberbrodhage, J. of Elec. Spec. and Rel. Phen. 87 (1997) 9-18

 Percentage contribution of each species is shown in the graph to the left 18/12/09Spectroscopy of Fluid Interfaces32 Determination of spectra of unknown component H. Morgner*, J. Oberbrodhage, J. of Elec. Spec. and Rel. Phen. 87 (1997) 9-18

18/12/09Spectroscopy of Fluid Interfaces33

Conclusion  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 18/12/09Spectroscopy of Fluid Interfaces34

THANK YOU for your attention 18/12/09Spectroscopy of Fluid Interfaces35

MEEM) Metastables Electron Emission Microscopy (MEEM)  Controlling Helium beam diameter difficult  Area from which electrons are abstracted can be controlled – spatial resolution  Surface electron can be mapped non-destructively 18/12/09Spectroscopy of Fluid Interfaces36 Harada et al*, Nature 372 (1994)