Engineering topics
Liu, Mingjie
Publications and source records attributed to Liu, Mingjie.
On the Sensitivity to Density-Functional Approximations for CO Binding Energies of Single-Atom Catalysts in Nitrogen-Doped Graphene
Density functional theory (DFT) methods are the working horse in screening new catalytic materials. They are widely used to predict trends in binding energies, which are then used to compare the activity of different materials. Here, the binding strength of CO is an important descriptor to the CO 2 reduction catalytic activity of the single transition metal atoms embedded on nitrogen-doped graphene (TM/NG). In this work, however, we show that CO binding strengths in different TM/NG has very different sensitivity to DFT methods. Specifically, Fe/NG CO binding energy changes dramatically with the percentage of exact exchange in the functional; Co/NG does less so, while Ni/NG nearly has no change. Such varying behaviors is a direct result of different local spin configurations, similar to the performance of DFT methods for metal porphyrin complexes. Therefore, caution should be exercised when using DFT binding energies for quantitative predictions in TM/NG single atom catalysis.
Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores
Designing single-species selective membranes for high-precision separations requires a fundamental understanding of the molecular interactions governing solute transport. Here, we comprehensively assess molecular-level features that influence the separation of 18 different anions by nanoporous cellulose acetate membranes. Our analysis identifies the limitations of bulk solvation characteristics to explain ion transport, highlighted by the poor correlation between hydration energy and the measured permselectivity (R 2 = 0.37). Entropy-enthalpy compensation, spanning 40 kilojoules per mole, leads to a free-energy barrier (ΔG‡) variation of only ~8 kilojoules per mole across all anions. We apply machine learning to elucidate descriptors for energetic barriers from a set of 126 collected features. Notably, electrostatic features account for 75% of the overall features used to describe ΔG‡, despite the relatively uncharged state of cellulose acetate. Our work presents an approach for studying ion transport across nanoporous membranes that could enable the design of ion-selective membranes.
Stable and Efficient Single-Atom Zn Catalyst for CO2 Reduction to CH4
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Semi-grand canonical Monte Carlo simulation of the acrolein induced surface segregation and aggregation of AgPd with machine learning surrogate models
The single atom alloy of AgPd has been found to be a promising catalyst for the selective hydrogenation of acrolein. It is also known that the formation of Pd islands on the surface will greatly reduce the selectivity of the reaction. As a result, the surface segregation and aggregation of Pd on the AgPd surface under reaction conditions of selective hydrogenation of acrolein are of great interest. In this work, we lay out a workflow that can predict the surface segregation and aggregation of Pd on a FCC(111) AgPd surface with and without the presence of acrolein. We use machine learning surrogate models to predict the AgPd bulk energy, AgPd slab energy, and acrolein adsorption energy on AgPd slabs. Then, we use the semi-grand canonical Monte Carlo simulation to predict the surface segregation and aggregation under different bulk Pd concentrations. Under vacuum conditions, our method predicts that only trace amount of Pd will exist on the surface at Pd bulk concentrations less than 20%. However, with the presence of acrolein, Pd will start to aggregate as dimers on the surface at Pd bulk concentrations as low as 6.5%.
A Physical Model for Understanding the Activation of MoS 2 Basal‐Plane Sulfur Atoms for the Hydrogen Evolution Reaction
Abstract Weak binding of hydrogen atoms to the 2H‐MoS 2 basal plane renders MoS 2 inert as an electrocatalyst for the hydrogen evolution reaction. Transition‐metal doping can activate neighboring sulfur atoms in the MoS 2 basal plane to bind hydrogen more strongly. Our theoretical studies show strong variation in the degree of activation by dopants across the 3d transition‐metal series. To understand the trends in activation, we propose a model based on the electronic promotion energy required to partially open the full valence shell of a local S atom and therefore enable it to bond with a H atom. In general, the promotion is achieved through an electron transfer from the S to neighboring metal‐atom sites. Furthermore, we demonstrate a specific, electronic‐structure‐based descriptor for the hydrogen‐binding strength: Δ dp , the local interband energy separation between the lowest empty d‐states on the dopant metal atoms and occupied p‐states on S. This model can be used to provide guidelines for chalcogen activation in future catalyst design based on doped transition‐metal dichalcogenides.
A Physical Model for Understanding the Activation of MoS 2 Basal‐Plane Sulfur Atoms for the Hydrogen Evolution Reaction
Abstract Weak binding of hydrogen atoms to the 2H‐MoS 2 basal plane renders MoS 2 inert as an electrocatalyst for the hydrogen evolution reaction. Transition‐metal doping can activate neighboring sulfur atoms in the MoS 2 basal plane to bind hydrogen more strongly. Our theoretical studies show strong variation in the degree of activation by dopants across the 3d transition‐metal series. To understand the trends in activation, we propose a model based on the electronic promotion energy required to partially open the full valence shell of a local S atom and therefore enable it to bond with a H atom. In general, the promotion is achieved through an electron transfer from the S to neighboring metal‐atom sites. Furthermore, we demonstrate a specific, electronic‐structure‐based descriptor for the hydrogen‐binding strength: Δ dp , the local interband energy separation between the lowest empty d‐states on the dopant metal atoms and occupied p‐states on S. This model can be used to provide guidelines for chalcogen activation in future catalyst design based on doped transition‐metal dichalcogenides.
A Physical Model for Understanding the Activation of MoS 2 Basal-plane Sulfur Atoms for the Hydrogen Evolution Reaction
All the DFT calculations are done with the Vienna Ab Initio Simulation Package (VASP) using the projector augmented wave method. The Bayesian error estimation exchange-correlation functionals (BEEF) with van der Waals interactions are employed. A plane-wave cutoff energy of 400 eV is used together with PAW-PBE potentials where semi core p states are treated as valence. All the calculations allow for spin-polarization. The structures are relaxed until the force is converged to < 0.01 eV/Å. The lattice parameter of MoS 2 unit cell, optimized with this functional, is 3.19 Å. A (4×4) supercell is used to model all the transition metal doped MoS 2 systems studied here, including those with S vacancies. For calculations in the initial dopant structure exploration, the Brillouin zone is sampled with a 3x3x1 Monkhorst-Pack k-point mesh. A 6×6×1 Monkhorst-Pack k-point mesh is used for the H binding energy and density of states calculations. In all calculations, the vacuum layer is set as 15 Å to eliminate periodic interaction perpendicular to the basal plane.