| In-situ ion-activated carbon nanospheres with tunable ultramicroporosity for superior CO2 capture Z Zhang, D Luo, G Lui, G Li, G Jiang, ZP Cano, YP Deng, X Du, S Yin, ... Carbon 143, 531-541, 2019 | 112 | 2019 |
| Imparting selective fluorophilic interactions in redox copolymers for the electrochemically mediated capture of short-chain perfluoroalkyl substances A Román Santiago, S Yin, J Elbert, J Lee, D Shukla, X Su Journal of the American Chemical Society 145 (17), 9508-9519, 2023 | 31 | 2023 |
| A DFT study for CO 2 hydrogenation on W (111) and Ni-doped W (111) surfaces M Zhang, S Yin, Y Chen Physical Chemistry Chemical Physics 22 (30), 17106-17116, 2020 | 8 | 2020 |
| Leveraging machine learning models for peptide–protein interaction prediction S Yin, X Mi, D Shukla RSC Chemical Biology 5 (5), 401-417, 2024 | 7 | 2024 |
| Substrate interactions guide cyclase engineering and lasso peptide diversification SE Barrett, S Yin, P Jordan, JK Brunson, J Gordon-Nunez, ... Nature chemical biology, 1-8, 2024 | 2 | 2024 |
| Exploring Molecular-Level Interactions in the Design of Redox Copolymers for Electrochemical Remediation of per-and Polyfluoroalkyl Substances (PFAS) AR Santiago, S Yin, J Elbert, D Shukla, X Su 2024 AIChE Annual Meeting, 2024 | | 2024 |
| Electrifying PFAS Cleanup: Remediation of per-and Polyfluoroalkyl Substances (PFAS) from Water Using Electrosorption and Electrooxidation Techniques AR Santiago, S Yin, JC Wu, J Elbert, CH Hou, D Shukla, HE Kim PRiME 2024 (October 6-11, 2024), 2024 | | 2024 |
| Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction S Yin, X Mi, D Shukla ArXiv, 2023 | | 2023 |
