| Electrochemical pumping for challenging hydrogen separations G Venugopalan, D Bhattacharya, E Andrews, L Briceno-Mena, ... ACS Energy Letters 7 (4), 1322-1329, 2022 | 26 | 2022 |
| Machine learning for guiding high-temperature PEM fuel cells with greater power density LA Briceno-Mena, G Venugopalan, JA Romagnoli, CG Arges Patterns 2 (2), 2021 | 20 | 2021 |
| PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems LA Briceno-Mena, JA Romagnoli, CG Arges Industrial & Engineering Chemistry Research 61 (9), 3350-3357, 2022 | 11 | 2022 |
| Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells LA Briceno-Mena, CG Arges, JA Romagnoli Computers & Chemical Engineering 171, 108159, 2023 | 9 | 2023 |
| Determining ion activity coefficients in ion-exchange membranes with machine learning and molecular dynamics simulations HK Gallage Dona, T Olayiwola, LA Briceno-Mena, CG Arges, R Kumar, ... Industrial & Engineering Chemistry Research 62 (24), 9533-9548, 2023 | 4 | 2023 |
| Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques LA Briceno-Mena, M Nnadili, MG Benton, JA Romagnoli Data-Centric Engineering 3, e18, 2022 | 3 | 2022 |
| Unsupervised learning: Local and global structure preservation in industrial data EE Seghers, LA Briceno-Mena, JA Romagnoli Computers & Chemical Engineering 178, 108378, 2023 | 2 | 2023 |
| Deconvoluting charge-transfer, mass transfer, and ohmic resistances in phosphonic acid–sulfonic acid ionomer binders used in electrochemical hydrogen pumps K Arunagiri, AJW Wong, L Briceno-Mena, HMGH Elsayed, JA Romagnoli, ... Energy & Environmental Science 16 (12), 5916-5932, 2023 | 2 | 2023 |
| Optimization of Multi-Modal Classification for Process Monitoring Z Webb, M Nnadili, E Seghers, L Briceno-Mena, J Romagnoli Frontiers in Chemical Engineering 4, 78, 2022 | 2 | 2022 |
| A Machine Learning Approach for Device Design from Materials and Operation Data LA Briceno-Mena, G Venugopalan, CC Arges, JA Romagnoli Computer Aided Chemical Engineering 50, 279-285, 2021 | 2 | 2021 |
| Feature Embedding of Molecular Dynamics-Based Descriptors for Modeling Electrochemical Separation Processes HKG Dona, T Olayiwola, LA Briceno-Mena, CG Arges, R Kumar, ... Computer Aided Chemical Engineering 52, 1451-1456, 2023 | 1 | 2023 |
| Hybrid Modeling for Electrochemical Systems LA Briceno-Mena | 1 | 2023 |
| Introduction to Formulation Optimization A Schmidt, L Briceno-Mena, S Rajagopalan, K Ma, B Reiner, B Braun The Digital Transformation of Product Formulation, 207-235, 2025 | | 2025 |
| Synergizing data-driven and knowledge-based hybrid models for ionic separations T Olayiwola, L Briceno-Mena, C Arges, J Romagnoli | | 2024 |
| Optimization and Machine Learning in Chemical Manufacturing L Briceno-Mena, S Iyer 2024 Spring Meeting & 20th Global Congress on Process Safety, 2024 | | 2024 |
| A Practitioner’s Guide to Machine Learning Applications in Chemical Engineering L Briceno-Mena, J Romagnoli 2024 Spring Meeting & 20th Global Congress on Process Safety, 2024 | | 2024 |
| A microfluidic approach to study variations in Chlamydomonas reinhardtii alkaline phosphatase activity in response to phosphate availability A Rahnama, M Vaithiyanathan, L Briceno-Mena, TM Dugas, KL Yates, ... Analyst, 2024 | | 2024 |
| Automated Synthesis of Hybrid Models for Ionic Separations T Olayiwola, L Briceno-Mena, T Kulkarni, C Arges, R Kumar, ... 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Determining ion activity coefficients in ion-exchange membranes with machine learning and molecular dynamics HKG Dona, T Olayiwola, LA Briceno-Mena, CG Arges, R Kumar, ... | | 2023 |
| Process Monitoring Optimization Via Unsupervised Clustering Metrics L Briceno-Mena, Z Webb, JA Romagnoli 2022 AIChE Annual Meeting, 2022 | | 2022 |
José A. RomagnoliCain Endowed Chair & Professor, Department of Chemical Engineering, Louisiana State University在 lsu.edu 的电子邮件经过验证
