Let’s look at one example from the literature. And why spectra on the right represent the “correct” way of curve fitting? What’s wrong with set of spectra shown on the left. So every time I give presentation, which show curve fitting results to people who are not doing it for living, I am talking about physical reasons behind Gaussian-Lorentzian shape of peaks, fundamental limits contributing to FWHM and generals rules of accurate reproducible curve fitting.Īnd the second reason is that even though we all know the rules behind good practices of curve fitting scientific literature is filled with poorly fitted spectra and, therefore, incorrectly interpreted XPS data. Their reaction is that curve fitting is meaningless as we can curve fit any particular spectrum with infinite number of combinations of peaks of different widths and shapes with the same goodness of fit. However, probably the best is the one that is provided with your instrument.Why talk about something so well known to the surface analysis community as curve fitting high resolution XPS spectra? Two reasons.įirst is the skepticism I am running into every time I am showing curve fits of spectra to non-surface analysis communities of scientists. This is not a free program but it is powerful and can be used for other spectrocopic methods.Īnother program used is Unifit and this one is free. Different shapes means more parameters to fit… With too many parameters, the result can be biased by the user.Īmong the softwares used (besides classical peakfit programs like PeakFit, Igor, Origin…) for XPS the most common software used is CasaXPS. In the old days we used Gaussian but nowadays pseudo-Voigt shapes are commonly used.

Of the two, peak shape is tough to solve. There are two problems in spectroscopy data fitting procedures : baseline and peak shape. Now, after this warning – and remember the Poisson distribution means that if you want data with a signal/noise ratios 10 times better … you need to acquire 100 times longer- the fitting software of the instruments is in general good. Contrary to what have you seen in recent Hollywood movies – where from a poor low resolution video, a smart tech person after 400 % zoom, does some fancy data enhancement procedure in order to see the face of the criminal- zooming and data treatement of poor data does nothing more than add to the noise. There is no way you can get good fit with bad data (data is bad either because the acquisition conditions were not adequate or, worse, because the sample was bad). Before going into detail about fitting softwares, let me remind you a sound principle : the fitting results can be as good as the data is.

There are several good software for fitting XPS data.

Xps peak fit software