Marc Duquesnoy

1.2k total citations · 1 hit paper
16 papers, 840 citations indexed

About

Marc Duquesnoy is a scholar working on Automotive Engineering, Electrical and Electronic Engineering and Materials Chemistry. According to data from OpenAlex, Marc Duquesnoy has authored 16 papers receiving a total of 840 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Automotive Engineering, 12 papers in Electrical and Electronic Engineering and 5 papers in Materials Chemistry. Recurrent topics in Marc Duquesnoy's work include Advanced Battery Technologies Research (14 papers), Advancements in Battery Materials (11 papers) and Machine Learning in Materials Science (5 papers). Marc Duquesnoy is often cited by papers focused on Advanced Battery Technologies Research (14 papers), Advancements in Battery Materials (11 papers) and Machine Learning in Materials Science (5 papers). Marc Duquesnoy collaborates with scholars based in France, United States and Belgium. Marc Duquesnoy's co-authors include Alejandro A. Franco, Teo Lombardo, Elixabete Ayerbe, Emiliano N. Primo, Mehdi Chouchane, Alexis Grimaud, Patrik Johansson, Peter Bjørn Jørgensen, Arnaud Demortière and Tejs Vegge and has published in prestigious journals such as Chemical Reviews, Journal of Power Sources and ACS Energy Letters.

In The Last Decade

Marc Duquesnoy

15 papers receiving 819 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

2021 339 citations Teo Lombardo, Marc Duquesnoy et al. Chemical Reviews profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Marc Duquesnoy France	12	626	522	183	164	46	16	840
Teo Lombardo France	16	999 1.6×	867 1.7×	215 1.2×	245 1.5×	54 1.2×	26	1.3k
Mohammad Shahjalal United Kingdom	10	606 1.0×	465 0.9×	42 0.2×	161 1.0×	75 1.6×	16	747
Ian Mathews Ireland	13	1.0k 1.7×	298 0.6×	281 1.5×	205 1.3×	29 0.6×	23	1.2k
Paul Gasper United States	14	792 1.3×	711 1.4×	124 0.7×	99 0.6×	80 1.7×	37	971
Fridolin Röder Germany	23	1.1k 1.8×	1.1k 2.0×	77 0.4×	146 0.9×	79 1.7×	41	1.3k
Subramanya Mayya Kolake South Korea	19	1.4k 2.3×	1.5k 2.9×	101 0.6×	119 0.7×	263 5.7×	35	1.8k
Guangyu Tian China	25	1.3k 2.0×	952 1.8×	102 0.6×	241 1.5×	182 4.0×	101	1.6k
Hossain Mansur Resalat Faruque Bangladesh	7	364 0.6×	290 0.6×	28 0.2×	120 0.7×	89 1.9×	11	546
Hongli Zhang China	16	660 1.1×	195 0.4×	108 0.6×	152 0.9×	148 3.2×	39	922

Countries citing papers authored by Marc Duquesnoy

Since Specialization

Citations

This map shows the geographic impact of Marc Duquesnoy's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marc Duquesnoy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc Duquesnoy more than expected).

Fields of papers citing papers by Marc Duquesnoy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marc Duquesnoy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marc Duquesnoy. The network helps show where Marc Duquesnoy may publish in the future.

Co-authorship network of co-authors of Marc Duquesnoy

This figure shows the co-authorship network connecting the top 25 collaborators of Marc Duquesnoy. A scholar is included among the top collaborators of Marc Duquesnoy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marc Duquesnoy. Marc Duquesnoy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Zanotto, Franco M., Marc Duquesnoy, Anna‐Katharina Hatz, et al.. (2023). Three-dimensional physical modeling of the wet manufacturing process of solid-state battery electrodes. Journal of Power Sources. 580. 233427–233427. 19 indexed citations

Duquesnoy, Marc, et al.. (2023). Toward high-performance energy and power battery cells with machine learning-based optimization of electrode manufacturing. Journal of Power Sources. 590. 233674–233674. 19 indexed citations

Duquesnoy, Marc, et al.. (2023). Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations. ECS Meeting Abstracts. MA2023-01(2). 704–704.

Yu, Jia, Marc Duquesnoy, Chaoyue Liu, & Alejandro A. Franco. (2023). Optimization of the microstructure of carbon felt electrodes by applying the lattice Boltzmann method and Bayesian optimizer. Journal of Power Sources. 575. 233182–233182. 5 indexed citations

Lombardo, Teo, et al.. (2022). The ARTISTIC Online Calculator: Exploring the Impact of Lithium‐Ion Battery Electrode Manufacturing Parameters Interactively Through Your Browser. Batteries & Supercaps. 5(3). 22 indexed citations

Duquesnoy, Marc, et al.. (2022). Electrochemistry Visualization Tool to Support the Electrochemical Analysis of Batteries. Batteries & Supercaps. 6(2). 5 indexed citations

Duquesnoy, Marc, et al.. (2022). Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics. npj Computational Materials. 8(1). 16 indexed citations

Duquesnoy, Marc, et al.. (2022). Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations. Energy storage materials. 56. 50–61. 76 indexed citations

Duquesnoy, Marc, et al.. (2021). Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity. Energy and AI. 5. 100090–100090. 43 indexed citations

10.

Lombardo, Teo, Emiliano N. Primo, Marc Duquesnoy, et al.. (2021). What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?. Batteries & Supercaps. 4(5). 689–689. 3 indexed citations

11.

Lombardo, Teo, Marc Duquesnoy, Fabian Årén, et al.. (2021). Artificial Intelligence Applied to Battery Research: Hype or Reality?. Chemical Reviews. 122(12). 10899–10969. 339 indexed citations breakdown →

12.

Lombardo, Teo, Emiliano N. Primo, Marc Duquesnoy, et al.. (2021). What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?. Batteries & Supercaps. 4(5). 758–766. 29 indexed citations

13.

Chen, Yu‐Ting, Marc Duquesnoy, Darren H. S. Tan, et al.. (2021). Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning. ACS Energy Letters. 1639–1648. 91 indexed citations

14.

Shodiev, Abbos, Marc Duquesnoy, Oier Arcelus, et al.. (2021). Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes. Journal of Power Sources. 511. 230384–230384. 33 indexed citations

15.

Lombardo, Teo, Jean‐Baptiste Hoock, Emiliano N. Primo, et al.. (2020). Accelerated Optimization Methods for Force‐Field Parametrization in Battery Electrode Manufacturing Modeling. Batteries & Supercaps. 3(8). 721–730. 46 indexed citations

16.

Duquesnoy, Marc, Teo Lombardo, Mehdi Chouchane, Emiliano N. Primo, & Alejandro A. Franco. (2020). Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning. Journal of Power Sources. 480. 229103–229103. 94 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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