Computational Studies of Pd-based Hydride Catalysts for CO2 Reduction

Changzhi Ai

Research output: Book/ReportPh.D. thesis

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Abstract

Emission of the greenhouse gas carbon dioxide (CO2) has increased rapidly with the development of industrialization in the past decades. The electrochemical CO2 reduction reaction (CO2RR) is considered as a promising strategy to convert CO2 into valuable chemicals. Palladium (Pd)-based hydride catalysts hold promise for producing syngas via both CO2RR to CO and hydrogen evolution reaction (HER) to H2, which can be effectively synthesized into valuable chemicals. In this thesis, Pd-based hydride catalysts are systematically studied to improve the CO2RR performance of pure Pd hydride (PdH) by doping and alloying methods with various theoretical approaches.

First, density functional theory (DFT) is used to systematically screen for stability, activity, and selectivity of transition metal dopants in PdH(111). Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb, Mo, Ru, Rh, Ag, Cd, Hf, Ta, W, and Re are doped into PdH surface with six different doping configurations: single, dimer, triangle, parallelogram, island, and overlayer. We find that several dopants, such as Ti and Nb, have excellent predicted catalytic activity and selectivity towards CO2RR compared to the pure PdH.

Second, using DFT calculations in combination with active learning cluster expansion (ALCE) and Monte Carlo simulated annealing (MCSA), we identify 12 stable PdHx(111) configurations on the DFT convex hull and investigate the binding energies of intermediates during the CO2RR and the competing HER. Through analysis of intermediate binding energies and a microkinetic model, we identify the atomic structures of the PdHx phase most likely to produce syngas. The high activity of the PdH0.6 surface can be attributed to the fact that the H segregation in the PdHx(111) surface breaks the linear relation between HOCO* and CO* adsorbates.

Third, an ALCE surrogate model equipped with MCSA, CO* filter and the kinetic model are used to screen out excellent PdxTi1 – xHy and PdxNb1 – xHy catalysts with both high stablity and superior activity. Since the calculations of the convex hull are finally verified by DFT and all binding energy calculations of the limited candidates are also calculated by DFT, the calculations of all enregies are more reliable compared to those only using the surrogate model with uncertainty. Finally, the stable and active 24 candidates of PdxTi1 – xHy and 6 active candidates of PdxNb1 – xHy are found according to our approach.

Finally, a deep learning-assisted multitasking genetic algorithm is used to screen PdxTi1 – xHy surfaces containing multiple adsorbates for CO2RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and keep a high accuracy as well as low uncertainty of energies and forces. The multitasking genetic algorithm is used to simultaneously globally find stable surface structures at each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among them, Pd0.56Ti0.44H1.06+25%CO, Pd0.31Ti0.69H1.25+50%CO, Pd0.31Ti0.69H1.25+25%CO, and Pd0.88Ti0.12H1.06+25%CO are found to be very active for CO2RR and suitable to generate syngas.
Original languageEnglish
Place of PublicationKgs. Lyngby
PublisherTechnical University of Denmark
Number of pages268
Publication statusPublished - 2023

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