| Artificial intelligence applied to battery research: hype or reality? T Lombardo, M Duquesnoy, H El-Bouysidy, F Årén, A Gallo-Bueno, ... Chemical reviews 122 (12), 10899-10969, 2021 | 228 | 2021 |
| Investigating electrode calendering and its impact on electrochemical performance by means of a new discrete element method model: Towards a digital twin of Li-Ion battery … AC Ngandjong, T Lombardo, EN Primo, M Chouchane, A Shodiev, ... Journal of Power Sources 485, 229320, 2021 | 159 | 2021 |
| Artificial intelligence investigation of NMC cathode manufacturing parameters interdependencies RP Cunha, T Lombardo, EN Primo, AA Franco Batteries & Supercaps 3 (1), 60-67, 2020 | 127 | 2020 |
| Lithium ion battery electrodes predicted from manufacturing simulations: Assessing the impact of the carbon-binder spatial location on the electrochemical performance M Chouchane, A Rucci, T Lombardo, AC Ngandjong, AA Franco Journal of Power Sources 444, 227285, 2019 | 109 | 2019 |
| Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning M Duquesnoy, T Lombardo, M Chouchane, EN Primo, AA Franco Journal of Power Sources 480, 229103, 2020 | 95 | 2020 |
| 4D-resolved physical model for Electrochemical Impedance Spectroscopy of Li (Ni1-x-yMnxCoy) O2-based cathodes in symmetric cells: Consequences in tortuosity calculations A Shodiev, EN Primo, M Chouchane, T Lombardo, AC Ngandjong, ... Journal of Power Sources 454, 227871, 2020 | 77 | 2020 |
| Carbon-binder migration: A three-dimensional drying model for lithium-ion battery electrodes T Lombardo, AC Ngandjong, A Belhcen, AA Franco Energy Storage Materials 43, 337-347, 2021 | 67 | 2021 |
| Accelerated optimization methods for force‐field parametrization in battery electrode manufacturing modeling T Lombardo, JB Hoock, EN Primo, AC Ngandjong, M Duquesnoy, ... Batteries & Supercaps 3 (8), 721-730, 2020 | 51 | 2020 |
| What can text mining tell us about lithium‐ion battery researchers’ habits? H El‐Bousiydy, T Lombardo, EN Primo, M Duquesnoy, M Morcrette, ... Batteries & Supercaps 4 (5), 758-766, 2021 | 31 | 2021 |
| An experimentally-validated 3D electrochemical model revealing electrode manufacturing parameters’ effects on battery performance C Liu, T Lombardo, J Xu, AC Ngandjong, AA Franco Energy Storage Materials 54, 156-163, 2023 | 22 | 2023 |
| The ARTISTIC online calculator: exploring the impact of lithium‐ion battery electrode manufacturing parameters interactively through your browser T Lombardo, F Caro, AC Ngandjong, JB Hoock, M Duquesnoy, ... Batteries & Supercaps 5 (3), e202100324, 2022 | 21 | 2022 |
| Towards a 3D-resolved model of Si/Graphite composite electrodes from manufacturing simulations C Liu, O Arcelus, T Lombardo, H Oularbi, AA Franco Journal of Power Sources 512, 230486, 2021 | 20 | 2021 |
| ToF-SIMS in battery research: Advantages, limitations, and best practices T Lombardo, F Walther, C Kern, Y Moryson, T Weintraut, A Henss, ... Journal of Vacuum Science & Technology A 41 (5), 2023 | 12 | 2023 |
| Experimentally Validated Three‐Dimensional Modeling of Organic‐Based Sodium‐Ion Battery Electrode Manufacturing T Lombardo, F Lambert, R Russo, FM Zanotto, C Frayret, G Toussaint, ... Batteries & Supercaps 5 (8), e202200116, 2022 | 12 | 2022 |
| Electrochemical detection of droplets in microfluidic devices: Simultaneous determination of velocity, size and content T Lombardo, L Lancellotti, C Souprayen, C Sella, L Thouin Electroanalysis 31 (11), 2103-2111, 2019 | 12 | 2019 |
| Accelerating battery manufacturing optimization by combining experiments M Duquesnoy, T Lombardo, M Chouchane, EN Primo, AA Franco Silico Electrodes Generation and Machine Learning, ChemRxiv. Prepr, 1-33, 2020 | 10 | 2020 |
| Electrochemical assessments of droplet contents in microfluidic channels. Application to the titration of heterogeneous droplets T Delahaye, T Lombardo, C Sella, L Thouin Analytica Chimica Acta 1155, 338344, 2021 | 7 | 2021 |
| Artificial intelligence investigation of NMC cathode manufacturing parameters interdependencies, Batter. Supercaps. 3 (2020) 60–67 RP Cunha, T Lombardo, EN Primo, AA Franco | 7 | |
| Identification of lithium compounds on surfaces of lithium metal anode with machine-learning-assisted analysis of ToF-SIMS spectra Y Zhao, SK Otto, T Lombardo, A Henss, A Koeppe, M Selzer, J Janek, ... ACS Applied Materials & Interfaces 15 (43), 50469-50478, 2023 | 6 | 2023 |
| Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics M Duquesnoy, T Lombardo, F Caro, F Haudiquez, AC Ngandjong, J Xu, ... npj Computational Materials 8 (1), 161, 2022 | 6 | 2022 |
