Standard test data(STD) are simulations of analytical instrument responses that help determine the veracity of computer-based, data analysis procedures that are typically used with instruments. The STD were developed for determining errors in peak parameters obtained from data analysis algorithms used in x-ray photoelectron spectroscopy (XPS). The STD were mainly C 1s doublet spectra constructed from spline polynomial models of measured C 1s polymer spectra. Different spectra were created based on a replicated factorial design with three factors: peak separation; relative intensity of the component peaks; and fractional Poisson noise. These doublet spectra simulated XPS measurements made on different two-component specimens. Single-peak C 1s spectra for individual polymers were also simulated, to provide the null case for identification of the doublet spectra. Twenty analysts used a variety of data analysis programs and a variety of curve-fitting approaches to determine peak binding energies. Results indicate that data analysis of doublet spectra may be problematic, because up to 50% of the STD doublets were assigned incorrectly as singlets. For spectra that were correctly identified as doublets, bias and random error in peak binding energies depended on the amount of separation between the component peaks and on their relative intensities. Biases ranged from -0.055 eV to 0.34 eV, while random errors ranged from 0.012 eV to 0.13 eV. Use of the Gaussian-Lorentzian fitted to spectra resulted in smaller biases than the use of a Gaussian function alone. As a guide to evaluating peak energy uncertainties in their own analyses, analysts may find it useful to analyze the STD themselves and then compare their results with those reported here. The spectra may be obtained at http://www.acg.nist.gov/std/main.html.
Citation: Surface and Interface Analysis
Pub Type: Journals
algorithm test, bias, peak-energy parameters, random error, reference data