<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>10(1)</volume><submitter>Hammer P</submitter><pubmed_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 &lt;i>fingerprint&lt;/i> method, an alternative approach that eliminates these long-standing constraints. By applying the &lt;i>fingerprint&lt;/i> 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 &lt;i>fingerprint&lt;/i> - unattainable with traditional methods. We further showcase by manual and automated analysis how the such obtained &lt;i>fingerprints&lt;/i> can be used to uniquely distinguish the TDS spectra of each alloy independent of the heating rate. Thus the &lt;i>fingerprint&lt;/i> method also increases experimental efficiency by reducing the amount of necessary heating rates for TDS down to one.</pubmed_abstract><journal>Npj Materials degradation</journal><pagination>7</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12799485</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>The fingerprint method for analysis of thermal desorption spectra.</pubmed_title><pmcid>PMC12799485</pmcid><pubmed_authors>Depover T</pubmed_authors><pubmed_authors>Vandewalle L</pubmed_authors><pubmed_authors>Hammer P</pubmed_authors><pubmed_authors>Verbeken K</pubmed_authors><pubmed_authors>Peil OE</pubmed_authors><pubmed_authors>Razumovskiy VI</pubmed_authors><pubmed_authors>Azizpour A</pubmed_authors></additional><is_claimable>false</is_claimable><name>The fingerprint method for analysis of thermal desorption spectra.</name><description>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 &lt;i>fingerprint&lt;/i> method, an alternative approach that eliminates these long-standing constraints. By applying the &lt;i>fingerprint&lt;/i> 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 &lt;i>fingerprint&lt;/i> - unattainable with traditional methods. We further showcase by manual and automated analysis how the such obtained &lt;i>fingerprints&lt;/i> can be used to uniquely distinguish the TDS spectra of each alloy independent of the heating rate. Thus the &lt;i>fingerprint&lt;/i> method also increases experimental efficiency by reducing the amount of necessary heating rates for TDS down to one.</description><dates><release>2026-01-01T00:00:00Z</release><publication>2026</publication><modification>2026-06-11T05:20:52.28Z</modification><creation>2026-06-11T03:08:03.343Z</creation></dates><accession>S-EPMC12799485</accession><cross_references><pubmed>41540998</pubmed><doi>10.1038/s41529-025-00718-z</doi></cross_references></HashMap>