# The fingerprint method for analysis of thermal desorption spectra

## TL;DR

A new method called the fingerprint method improves the analysis of thermal desorption spectra by resolving activation energy distributions more effectively than traditional approaches.

## Contribution

The fingerprint method eliminates assumptions about peak numbers and shapes in TDS analysis, enabling unique material identification with fewer heating rates.

## Key findings

- The fingerprint method resolves activation energy distributions in TDS spectra with higher sensitivity than traditional methods.
- Fingerprints obtained using the method can uniquely distinguish Fe-C alloys regardless of heating rate.
- The method reduces the number of required heating rates for TDS experiments to just one.

## Abstract

The analysis of thermal desorption spectra (TDS) and the calculation of hydrogen detrapping activation energies rely on Gaussian peak deconvolution and Choo-Lee plot regression since 1982. However, this method imposes important assumptions about the number and shape of the TDS peaks used for fitting. In this study, we propose the fingerprint method, an alternative approach that eliminates these long-standing constraints. By applying the fingerprint analysis to eight TDS spectra from three different Fe-C model alloys, we demonstrate its exceptional sensitivity and ability to resolve activation energy distributions – the material fingerprint – unattainable with traditional methods. We further showcase by manual and automated analysis how the such obtained fingerprints can be used to uniquely distinguish the TDS spectra of each alloy independent of the heating rate. Thus the fingerprint method also increases experimental efficiency by reducing the amount of necessary heating rates for TDS down to one.