Mechanism of CO2 reduction by H2 on Ru (0 0 0 1) and general selectivity descriptors for late-transition metal catalysts T Avanesian, GS Gusmão, P Christopher Journal of Catalysis 343, 86-96, 2016 | 120 | 2016 |
Direct aromatization of CO2 via combined CO2 hydrogenation and zeolite-based acid catalysis I Nezam, W Zhou, GS Gusmão, MJ Realff, Y Wang, AJ Medford, ... Journal of CO2 Utilization 45, 101405, 2021 | 68 | 2021 |
Kinetics-informed neural networks GS Gusmão, AP Retnanto, SC Da Cunha, AJ Medford Catalysis Today 417, 113701, 2023 | 30 | 2023 |
A general and robust approach for defining and solving microkinetic catalytic systems GS Gusmão, P Christopher AIChE Journal 61 (1), 188-199, 2015 | 20 | 2015 |
Process modeling and simulation of an industrial-scale plant for green ethylene production JGSS Maia, RB Demuner, AR Secchi, PA Melo, RW Carmo, GS Gusmão Industrial & Engineering Chemistry Research 57 (18), 6401-6416, 2018 | 14 | 2018 |
Modeling of Catalyst Deactivation in Bioethanol Dehydration Reactor RB Demuner, JG Soares Santos Maia, AR Secchi, PA Melo, ... Industrial & Engineering Chemistry Research 58 (8), 2717-2726, 2019 | 5 | 2019 |
Role of Catalyst Domain Size in the Hydrogenation of CO2 to Aromatics over ZnZrOx/ZSM-5 Catalysts I Nezam, W Zhou, DR Shah, MP Bukhovko, MR Ball, GS Gusmão, ... The Journal of Physical Chemistry C 127 (13), 6356-6370, 2023 | 4 | 2023 |
Quantifying the impact of temporal analysis of products reactor initial state uncertainties on kinetic parameters A Yonge, GS Gusmão, R Batchu, MR Kunz, Z Fang, R Fushimi, ... AIChE Journal 68 (9), e17776, 2022 | 3 | 2022 |
Training Stiff Dynamic Process Models via Neural Differential Equations W Bradley, GS Gusmão, AJ Medford, F Boukouvala Computer Aided Chemical Engineering 49, 1741-1746, 2022 | 3 | 2022 |
Maximum-likelihood estimators in physics-informed neural networks for high-dimensional inverse problems GS Gusmão, AJ Medford Computers & Chemical Engineering 181, 108547, 2024 | 2 | 2024 |
Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks D Nai, GS Gusmão, ZA Kilwein, F Boukouvala, AJ Medford arXiv preprint arXiv:2406.13904, 2024 | | 2024 |
Model-Based Design of Experiments for Temporal Analysis of Products (TAP): A Simulated Case Study in Oxidative Propane Dehydrogenation A Yonge, GS Gusmão, R Fushimi, AJ Medford Industrial & Engineering Chemistry Research 63 (11), 4756-4770, 2024 | | 2024 |
Using Neural Networks to Interpret Transient Kinetic Data A Medford, G S. Gusmão, D Nai 2023 AIChE Annual Meeting, 2023 | | 2023 |
Dimensionality Reduction of Chemical Kinetics Based on Extent-of-Reaction in a Physics-Inspired Machine Learning Framework G Gusmão, A Medford 2022 AIChE Annual Meeting, 2022 | | 2022 |
Online Graduate Certificate in Data Science for the Chemical Industry AJ Medford, F Boukouvala, MA Grover, D Sholl, C Meredith, P Cheng, ... Chemical Engineering Education 56 (4), 2022 | | 2022 |
Physics-Informed Neural Networks for Kinetic Parameter Estimation and Uncertainty Quantification G Gusmão, A Retnanto, S da Cunha, MR Kunz, A Medford 2021 AIChE Annual Meeting, 2021 | | 2021 |
Multifunctional catalysts for solar-driven fuel production and chemical conversion P Christopher, M Kale, J Matsubu, G Gusmao North American Catalysis Society Meeting, 2013 | | 2013 |