Engineering topics

Cai, Yuting

Publications and source records attributed to Cai, Yuting.

3 records

DOE OSTI2024

Graphene-supported single atom catalysts for high performance lithium-oxygen batteries

The optimal choice of d-block metals in single atom catalysts (SACs) is crucial for designing efficient electrocatalysts for activating the Oxygen reduction reaction (ORR)/ Oxygen evolution reaction (OER) in lithiumoxygen batteries (LOBs). Herein, we used the Quantum Mechanics methods to understand the origin of reactivity for a series of 16 d-block metals supported on nitrogen-doped graphene as SACs for ORR and OER in LOBs. Based on the Gibbs free energy calculations, we found that among the 16 SACs investigated, Zn-SAC exhibits the highest electrochemical activity with the lowest overpotential of 0.17 V. Here we then used machine learning (ML) to develop an intrinsic descriptor, phi, that correlates the catalytic activity with electronic and chemical properties of the catalytic centers at the M-N 4 active site on graphene surface. We established a linear relationship between phi and the catalytic activity that provides guidance for designing efficient SACs for electrocatalysis in LOBs. To validate these predictions, we report electrochemical measurements showing that Zn-SAC exhibits an ultra-stable cyclability with reduced overpotentials over Mo-SAC and nitrogen-doped graphene (NG), confirming our theoretical prediction. This fundamental work provides a deep understanding on the rational design of efficient SACs for OER/ ORR in LOBs.

25 ENERGY STORAGE↗

DOE OSTI2022

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships. Here, this review provides recent progress in the ML-aided rational design of heterogeneous catalysts with the focus on SACs in terms of structure–activity relationships, feature importance analysis, high-throughput screening, stability, and metal–support interactions for electrochemistry. Support vector machine (SVM), random forest regression (RFR), and deep neural networks (DNN) along with atomic properties are mainly used for the design of SACs. The ML results have shown that the number of electrons in the d orbital, oxide formation enthalpy, ionization energy, Bader charge, d-band center, and enthalpy of vaporization are mainly the most important parameters for the defining of the structure–activity relationships for electrochemistry. However, the black-box nature of ML techniques occasionally makes a physical interpretation of descriptors, such as the Bader charge, d-band center, and enthalpy of vaporization, non-trivial. At the current stage, ML application is limited by the lack of a large and high-quality database. Future prospects for the development of a large database and a generalized ML algorithm for SAC design are discussed to give insights for further studies in this field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Highly Reversible Sodiation/Desodiation from a Carbon-Sandwiched SnS2 Nanosheet Anode for Sodium Ion Batteries

The further improvement of sodium ion batteries requires the elucidation of the mechanisms pertaining to reversibility, which allows the novel design of the electrode structure. Her; through a hydrogel-embedding method, we are able to confine the growth of few-layer SnS 2 nanosheets between a nitrogen- and sulfur-doped carbon nanotube (NS-CNT) and amorphous carbon. The obtained carbon-sandwiched SnS 2 nanosheets demonstrate excellent sodium storage properties. In operando small-angle X-ray scattering combined with the ex situ X-ray absorption near edge spectra reveal that the redox reactions between SnS 2 /NS-CNT and the sodium ion are highly reversible. Furthermore, on the contrary, the nanostructure evolution is found to be irreversible, in which the SnS 2 nanosheets collapse, followed by the regeneration of SnS 2 nanoparticles. This work provides operando insights into the chemical environment evolution and structure change of SnS 2 -based anodes, elucidating its reversible reaction mechanism, and illustrates the significance of engineered carbon support in ensuring the electrode structure stability.

25 ENERGY STORAGE↗