Benchmarking the accuracy of coverage-dependent models: adsorption and desorption of benzene on Pt (1 1 1) and Pt3Sn (1 1 1) from first principles

Bimetallic catalysts have demonstrated properties favorable for upgrading biofuel through catalytic hydrodeoxygenation. However, the design and optimization of such bimetallic catalysts requires the ability to construct accurate, predictive models of these systems. To generate a model that predicts the kinetic behavior of benzene adsorbed on Pt (1 1 1) and a Pt₃Sn (1 1 1) surface alloy (Pt₃Sn (1 1 1)), the adsorption of benzene was studied for a wide range of benzene coverages on both surfaces using density functional theory (DFT) calculations. The adsorption energy of benzene was found to correlate linearly with benzene coverage on Pt (1 1 1) and Pt₃Sn (1 1 1); both surfaces exhibited net repulsive lateral interactions. Through an analysis of the d-band properties of the metal surface, it was determined that the coverage dependence is a consequence of the electronic interactions between benzene and the surface. The linear coverage dependence of the adsorption energy allowed us to quantify the influence of the lateral interactions on the heat of adsorption and temperature programmed desorption (TPD) spectra using a mean-field model. A comparison of our simulated TPD to experiment showed that this mean-field model adequately reproduces the desorption behavior of benzene on Pt (1 1 1) and Pt₃Sn (1 1 1). In particular, the TPD correctly exhibits a broadening desorption peak as the initial coverage of benzene increases on Pt (1 1 1) and a low temperature desorption peak on Pt₃Sn (1 1 1). However, due to the sensitivity of the TPD peak temperature to the desorption energy, precise alignment of experimental and theoretical TPD spectra demands an accurate calculation of the adsorption energy. Therefore, an analysis of the effect of the exchange-correlation functional on TPD modeling is presented. Through this work, we show the necessity of incorporating lateral interactions into theoretical models in order to correctly predict experimental behavior.