XPS Reference Pages

This site contains information gained from decades of X-ray photoelectron spectroscopy (XPS) analyses of an enormous variety of samples analyzed at Surface Science Western laboratories located at the University of Western Ontario. Originally this site was designed as a place for students and our clients to access valuable tips and information. It has since been opened to all those interested in the XPS technique. Summaries of literature data, relevant references and unpublished data taken of well characterized standard samples are presented. Also curve-fitting tips, instrument set-up tips (specifically for the Kratos AXIS Ultra and Kratos AXIS Nova), and CasaXPS tips pertaining to questions we normally get from our students and clients, and other odd bits of information are presented.

The fine print:
Surface Science Western and the University of Western Ontario does not warranty any of the information shown at this site. Any use of this data in scientific publications or other forms should include referencing to the originally published data referenced herein.

Systematic and Collaborative Approach to Problem Solving using X-ray Photoelectron Spectroscopy

Our recent article [1] in Applied Surface Science Advances highlights methodology developed as a result of years of interactions between many junior and senior X-ray Photoelectron Spectroscopy (XPS) users operating within the CasaXPS spectral processing and interpretation program framework. In particular, discussions arising from a series of workshops have been a significant source for developing the overall XPS data processing concept and are the motivation for creating this work. These workshops organized by the Institut des Matériaux Jean Rouxel (IMN), Nantes gather both experienced and novice users of XPS for a week of discourse in conceptual experiment design and the resulting data processing. However, the framework constructed and utilized within these workshops encouraged the dissemination of knowledge beyond XPS data analysis and emphasized the importance of a multi-disciplinary collaborative approach to surface analysis problem-solving. The material presented here embodies data treatment originating from data made available to the first CNRS Thematic Workshop presented at Roscoff 2013. The methodology described here has evolved over the subsequent workshops in 2016 and 2019 and currently represents the philosophy used in CasaXPS spectral data processing paradigm.

This article also serves as a useful reference descriptor of the CasaXPS software program. 

Reference:

[1] N. Fairley, V. Fernandez, M. Richard‐Plouet, C. Guillot-Deudon, J.Walton, E. Smith, D. Flahaut, M. Greiner, M. Biesinger, S. Tougaard, D. Morgan, J. Baltrusaitis, Systematic and collaborative approach to problem solving using X-ray photoelectron spectroscopy, Applied Surface Science Advances, 5 (2021) 100112.

Video - XPS: The Basics, Curve-Fitting and Advanced Studies

A recent lecture given by Dr. Biesinger to the Canadian Biomaterials Society - hosted by the Quebec City Student Chapter of the Canadian Biomaterials Society (CBS-QCSC) at Laval University.

Resources

The NIST XPS database is an absolutely essential tool for tracking down binding energy references and a wealth of other valuable data.

NIST X-ray Photoelectron Spectroscopy Database  

SESSA software is a convenient tool for simulating XPS intensities for simple/complex samples and experimental configurations (such as e.g. synchrotrons).

NIST Database for the Simulation of Electron Spectra for Surface Analysis (SESSA)

FEFF is an automated program for ab initio multiple scattering calculations of X-ray Absorption Fine Structure (XAFS), X-ray Absorption Near-Edge Structure (XANES) and various other spectra for clusters of atoms. The code yields scattering amplitudes and phases used in many modern XAFS analysis codes, as well as various other properties.

The FEFF Code

XPS Prediction Server - Caro Research Group (Aalto University): Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining DFT and GW.

XPS Prediction Server