Analysis of temperature programmed desorption (TPD) data for the characterisation of catalysts containing a distribution of adsorption sites
Literature Information
This paper discusses some methods of analysing TPD data for samples obeying first-order desorption kinetics and proposes several improvements to existing practice. The methods apply in the case when the Arrhenius parameters A and E for each site are independent of coverage, and thus are normally suitable for the characterisation of porous solids. An improved implementation of the condensation approximation method is proposed to gain an initial estimate of the adsorption site distribution. Further, a variation of the method is proposed that can be used when A is a function of E. The initial estimate of the distribution can be used to analyse data obtained by an interrupted TPD experiment, in which heating is halted at a specified temperature. This method provides a reliable method of determining the parameter A for a peak in the distribution. Finally, regularisation procedures for obtaining physically sensible distributions from “noisy” TPD data are discussed. It is shown that a penalty function based on the square of the second derivative of the distribution is normally most suitable for analysing TPD data, at least in the case when the L-curve method is used to select the regularisation parameter.
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DOI: 10.1039/B001766N
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.











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