Jay Shukla
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jay Shukla has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jorge Anibal Boscoboinik
Center for Functional Nanomaterials Brookhaven National Laboratory Upton New York USA
1 shared publication - Marija Iloska
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA
1 shared publication - Qin Wu
Center for Functional Nanomaterials
1 shared publication - Xiaohui Qu
Center for Functional Nanomaterials, Brookhaven National Laboratory
1 shared publication - Zubin Darbari
Department of Materials Science and Chemical Engineering Stony Brook University 11794 Stony Brook NY
1 shared publication
Latest Publications
- Open Access
- 10 December 2024
- Published by American Chemical Society (ACS) in ChemRxiv
Discovering CO Adsorption and Desorption Pathways from Chemical Reaction Neural Networks Modeling of Transient Kinetics Spectroscopy
We demonstrate a data-driven approach to interpret surface reactions by combining time-resolved gas-pulsing infrared spectroscopy with Chemical Reaction Neural Networks (CRNN). Using CO adsorption and desorption on Pd(111) at 460K-490K as a model system, we show how transient kinetic data can reveal detailed reaction mechanisms. Starting with a simple one-species model, we systematically evaluate increasingly complex mechanisms involving hollow- and bridge-site adsorption. Despite similar goodness of fit to the same experimental absorbance data, our models predict distinct coverage dynamics for different adsorption sites. Through analysis of spectral peak stability and predicted dynamics, we identify a mechanism where CO primarily adsorbs on bridge sites followed by rapid conversion to hollow sites as the most physically consistent with experimental observations. This work provides a framework for extracting mechanistic insights from limited experimental data, demonstrating how machine learning can bridge the gap between transient kinetic measurements and molecular-level understanding of surface reactions.
- 10 December 2024
- Published by American Chemical Society (ACS) in ChemRxiv
Open AccessDiscovering CO Adsorption and Desorption Pathways from Chemical Reaction Neural Networks Modeling of Transient Kinetics Spectroscopy
We demonstrate a data-driven approach to interpret surface reactions by combining time-resolved gas-pulsing infrared spectroscopy with Chemical Reaction Neural Networks (CRNN). Using CO adsorption and desorption on Pd(111) at 460K-490K as a model system, we show how transient kinetic data can reveal detailed reaction mechanisms. Starting with a simple one-species model, we systematically evaluate increasingly complex mechanisms involving hollow- and bridge-site adsorption. Despite similar goodness of fit to the same experimental absorbance data, our models predict distinct coverage dynamics for different adsorption sites. Through analysis of spectral peak stability and predicted dynamics, we identify a mechanism where CO primarily adsorbs on bridge sites followed by rapid conversion to hollow sites as the most physically consistent with experimental observations. This work provides a framework for extracting mechanistic insights from limited experimental data, demonstrating how machine learning can bridge the gap between transient kinetic measurements and molecular-level understanding of surface reactions.