journal article·2026-07-17Open access

A Large Scale Multi‐Modal Workflow for Battery Characterization: From Concept to Implementation

François Cadiou, Cinthya Herrera, Duncan Atkins, Elixabete Ayerbe, Giorgio Baraldi, Stéphanie Belin, Anass Benayad, Didier Blanchard, Federico Capone, Ennio Capria, Isidora Cekic‐Laskovic, Robert Dominko, Kristina Edström, Ajay Gautam, Lukas Helfen, Antonella Iadecola, Quentin Jacquet, Gregor Kapun, Xinyu Li, Aleksandar Matic, Nataliia Mozhzhukhina, Andrew J. Naylor, Poul Norby, Chris O'Keefe, Alexandre Ponrouch, Jean‐Pascal Rueff, Elena Tchernykova, Deyana Tchitchekova, Israel Temprano, Nikita Vostrov, Marnix Wagemaker, Martin Winter, Christian Wölke, Tejs Vegge, Sandrine Lyonnard

Abstract

ABSTRACT The development of material acceleration platforms in battery research requires integrating complementary techniques and correlating heterogeneous experimental datasets. Here, this challenge is addressed through a large‐scale multimodal program involving fifteen European laboratories and facilities. Coordinated experiments on graphite/ Li‐ion full cells address two scientific questions: is the electrolyte composition impacting electrode properties, and how do electrode materials evolve when cells are cycled to their end‐of‐life? A fully standardized workflow is demonstrated, from sample production and delivery, to metadata and data handling, generating seventy‐five concatenated datasets shared among partners. Their integrated analysis reveals that scientific conclusions strongly depend on both the observable chosen to describe electrode properties, and the characterization technique employed. Beyond providing detailed insights into specific aspects as crystal structures, redox activity, surface processes or morphology, individual experiments can also act as binary diagnostic tool. Two‐dimensional observable‐technique patterns are introduced, where each pixel encodes a yes, no or uncertain answer to a scientific question. These multi‐property metaviews enable the classification of material behavior and technique suitability according to user demand and criteria, highlighting the interdependence between measurement, extracted parameters, and scientific interpretation. This work establishes a proof‐of‐concept toward integrated and holistic battery research workflows.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advancements in Battery Materials · Machine Learning in Materials Science · Advanced Battery Technologies Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

European Synchrotron Radiation Facility Grenoble France

CEA, Liten Université Grenoble Alpes Grenoble France

Institut Laue‐Langevin Grenoble France

CEA, IRIG‐MEM‐L_SIM Université Grenoble Alpes Grenoble France

CIDETEC, Basque Research and Technology Alliance (BRTA) Donostia‐San Sebastián Spain

Basquevolt, Alava Technology Park Vitoria‐Gasteiz Spain

Synchrotron SOLEIL, L'Orme des Merisiers Saint Aubin France

Sorbonne Université, CNRS, Physicochimie des Électrolytes et Nanosystèmes Interfaciaux Paris France

Réseau sur le Stockage Electrochimique de L'Energie (RS2E) Amiens France

Forschungszentrum Jülich GmbH, Helmholtz‐Institute Münster (IMD‐4) Münster Germany

Department of Materials Chemistry National institute of Chemistry Ljubljana Slovenia

Department of Chemistry – Ångström Laboratory Uppsala University Uppsala Sweden

Department of Radiation Science and Technology Delft University of Technology Delft The Netherlands

Univ. Grenoble Alpes, CEA, CNRS, GrenobleINP, IRIG, Symmes Grenoble France

Department of Energy Conversion and Storage Technical University of Denmark (DTU) Lyngby Denmark

Department of Physics Chalmers University of Technology Gothenburg Sweden

Department of Chemistry University of Cambridge Cambridge England

Institut de Ciència de Materials de Barcelona, ICMAB‐CSIC Bellaterra Spain

Funding

Horizon 2020 Framework Programme

References (100+)

Show source-provided references
1. 10.1038/3510464410.1038/35104644 ↗
2. 10.1002/aenm.20210290410.1002/aenm.202102904 ↗
3. Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches10.1002/aenm.202100362 ↗
4. 10.1002/aenm.20210265210.1002/aenm.202102652 ↗
5. 10.1002/aenm.20210269410.1002/aenm.202102694 ↗
6. 10.1002/aenm.20210278510.1002/aenm.202102785 ↗
7. 10.1038/nmat477710.1038/nmat4777 ↗
8. 10.1016/j.joule.2020.11.00310.1016/j.joule.2020.11.003 ↗
9. 10.1021/acs.chemrev.4c0056610.1021/acs.chemrev.4c00566 ↗
10. Multimodal Nanoscale Tomographic Imaging for Battery Electrodes10.1002/aenm.201904119 ↗
11. 10.1039/D4EE00590B10.1039/D4EE00590B ↗
12. 10.1149/1945-7111/ac429810.1149/1945-7111/ac4298 ↗
13. 10.1002/aenm.20230010310.1002/aenm.202300103 ↗
14. B.Wolff P. P. M.Schleker H.Ditler et al. “In Operando EPR and NMR Cell for Correlative Characterisation of Battery Electrodes ” (2025).10.26434/chemrxiv-2025-g1sv5 ↗
15. Correlative AFM‐in‐SEM Characterization of Next‐Generation Batteries
16. S.Bai Z.Liu D.Cheng et al. “Guidelines for Correlative Imaging and Analysis of Reactive Lithium Metal Battery Materials ” (2024).
17. 10.1039/D4EE01497A10.1039/D4EE01497A ↗
18. 4D Imaging of Lithium Batteries Using Correlative Neutron and X‐Ray Tomography with a Virtual Unrolling Technique10.1038/s41467-019-13943-3 ↗
19. 10.1038/s41560-024-01634-310.1038/s41560-024-01634-3 ↗
20. 10.1038/s41467-023-35933-210.1038/s41467-023-35933-2 ↗
21. 10.1038/s41565-025-01921-410.1038/s41565-025-01921-4 ↗
22. 10.1002/cssc.20230033310.1002/cssc.202300333 ↗
23. 10.1088/2515-7655/addd4610.1088/2515-7655/addd46 ↗
24. 10.1002/adma.20240779110.1002/adma.202407791 ↗
25. 10.1002/aenm.20210267810.1002/aenm.202102678 ↗
26. 10.1016/j.matt.2023.07.01610.1016/j.matt.2023.07.016 ↗
27. 10.1002/aenm.20210263810.1002/aenm.202102638 ↗
28. 10.1002/aenm.20240326310.1002/aenm.202403263 ↗
29. The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers10.1002/aidi.202500067 ↗
30. 10.1002/adma.20180825610.1002/adma.201808256 ↗
31. 10.3389/fmats.2022.84064410.3389/fmats.2022.840644 ↗
32. SimStack: An Intuitive Workflow Framework10.3389/fmats.2022.877597 ↗
33. S. K.Steensen T. S.Thakur M.Dillenz et al. “The Interoperability Challenge in DFT Workflows across Implementations ” (2025).10.1002/aidi.202500232 ↗
34. Scientific Workflow Systems: Pipeline Pilot and KNIME10.1007/s10822-012-9577-7 ↗
35. 10.1039/D4DD00134F10.1039/D4DD00134F ↗
36. 10.1016/j.joule.2022.12.00410.1016/j.joule.2022.12.004 ↗
37. 10.1039/C9SC03766G10.1039/C9SC03766G ↗
38. 10.1002/aenm.20240496110.1002/aenm.202404961 ↗
39. 10.1038/s41586-023-06734-w10.1038/s41586-023-06734-w ↗
40. 10.1039/D4DD00129J10.1039/D4DD00129J ↗
41. IvoryOS: An Interoperable Web Interface for Orchestrating Python‐Based Self‐Driving Laboratories10.1038/s41467-025-60514-w ↗
42. 10.1016/j.device.2024.10056710.1016/j.device.2024.100567 ↗
43. 10.1002/batt.20250015510.1002/batt.202500155 ↗
44. 10.1126/science.adk922710.1126/science.adk9227 ↗
45. 10.1021/jacs.4c0955310.1021/jacs.4c09553 ↗
46. 10.1038/s43586-021-00015-410.1038/s43586-021-00015-4 ↗
47. 10.1038/s41565-022-01081-910.1038/s41565-022-01081-9 ↗
48. 10.1039/D1TA02262H10.1039/D1TA02262H ↗
49. 10.1146/annurev-matsci-070616-12395710.1146/annurev-matsci-070616-123957 ↗
50. 10.1016/j.joule.2023.10.01110.1016/j.joule.2023.10.011 ↗
51. 10.1016/j.jpowsour.2021.23081810.1016/j.jpowsour.2021.230818 ↗
52. 10.1002/aenm.20240425610.1002/aenm.202404256 ↗
53. 10.1021/acs.chemrev.4c0083110.1021/acs.chemrev.4c00831 ↗
54. 10.1016/j.jpowsour.2021.22990410.1016/j.jpowsour.2021.229904 ↗
55. 10.1149/1945-7111/ab975a10.1149/1945-7111/ab975a ↗
56. 10.1021/ja212192610.1021/ja2121926 ↗
57. Revealing How Internal Sensors in a Smart Battery Impact the Local Graphite Lithiation Mechanism10.1038/s41467-024-54656-6 ↗
58. 10.1016/j.joule.2023.11.00310.1016/j.joule.2023.11.003 ↗
59. 10.1016/S0378-7753(00)00493-610.1016/S0378-7753(00)00493-6 ↗
60. A Fundamental Correlative Spectroscopic Study on Li1−xNiO2${\rm Li}1-x{\rm NiO}_2$ and NaNiO2${\rm NaNiO}_2$10.1002/aenm.202401413 ↗
61. 10.1002/aenm.20240408010.1002/aenm.202404080 ↗
62. Innovative Data Management in Advanced Characterization: Implications for Materials Design10.1016/j.mtcomm.2019.100541 ↗
63. 10.1038/sdata.2016.1810.1038/sdata.2016.18 ↗
64. 10.1016/j.est.2023.11040910.1016/j.est.2023.110409 ↗
65. There and Back Again—The Journey of LiNiO2${\rm LiNiO}_2$ as a Cathode Active Material10.1002/anie.201812472 ↗
66. 10.1002/er.376210.1002/er.3762 ↗
67. A New Solvent Mixture for Use of LiTDI as Electrolyte Salt in Li‐Ion Batteries10.1016/j.electacta.2019.02.097 ↗
68. 10.1016/j.jpowsour.2011.06.03010.1016/j.jpowsour.2011.06.030 ↗
69. Determination of the Electrochemical Performance and Stability of the Lithium Salt Lithium 4,5‐Dicyano‐2‐(Trifluoromethyl) Imidazolide with Various Anodes in Li‐Ion Cells10.1016/j.jpowsour.2015.08.102 ↗
70. Power Capability of LiTDI‐Based Electrolytes for Lithium‐Ion Batteries10.1016/j.jpowsour.2015.06.073 ↗
71. 10.1002/aenm.20210380610.1002/aenm.202103806 ↗
72. 10.1021/acs.chemmater.6b0524710.1021/acs.chemmater.6b05247 ↗
73. F.Cadiou C.Herrera D.Blanchard et al. “D5.3 – Demonstrating the Capacity to Run Coordinated Multi‐Technique Multi‐Site and Multi‐Scale Experiments ”BIG‐MAP Project Technical Report(2023).
74. BIG‐MAP “BIG‐MAP App Store ” (2024) https://big‐map.github.io/big‐map‐registry/.
75. V.Granata F.Liot X.Wang et al. “Implementing a Scalable Redeployable and Multitiered Repository for FAIR and Secure Scientific Data Sharing: The BIG‐MAP Archive ” (2025).
76. 10.1002/batt.20210011710.1002/batt.202100117 ↗
77. Phase Behavior During Electrochemical Cycling of Ni‐Rich Cathode Materials for Lithium‐Ion Batteries10.1002/aenm.202003404 ↗
78. 10.1039/D3EE04115H10.1039/D3EE04115H ↗
79. 10.1149/2.008306jes10.1149/2.008306jes ↗
80. 10.1016/j.chempr.2021.02.02510.1016/j.chempr.2021.02.025 ↗
81. 10.1002/aenm.20220330710.1002/aenm.202203307 ↗
82. 10.1016/j.elspec.2018.05.00510.1016/j.elspec.2018.05.005 ↗
83. 10.1039/D2TA03242B10.1039/D2TA03242B ↗
84. C. N.Herrera F.Capone R.Fantin et al. “Standardization of Post‐Mortem Photoelectron Spectroscopy Studies of Battery Interphases: From Cell Assembly to Data Analysis ”SSRN(2025).10.2139/ssrn.5508856 ↗
85. Engineering Electrode Microstructures for Advanced Lithium‐Ion Batteries10.20517/microstructures.2023.89 ↗
86. 10.1021/acsnano.4c1641910.1021/acsnano.4c16419 ↗
87. 10.1016/j.ensm.2024.10378510.1016/j.ensm.2024.103785 ↗
88. V.Vanpeene O.Stamati F.Cadiou Q.Jacquet J.Villanova andS.Lyonnard “4D Operando X‐Ray Nano‐Holo‐Tomography Reveals Multiscale Chemomechanics in Silicon‐Graphite Anodes ” (2025).
89. 10.1038/s41586-019-1481-z10.1038/s41586-019-1481-z ↗
90. 10.1002/aenm.20250203210.1002/aenm.202502032 ↗
91. In Situ and Operando Raman Spectroscopy of Layered Transition Metal Oxides for Li‐Ion Battery Cathodes10.3389/fenrg.2018.00082 ↗
92. Imaging Inter‐ and Intra‐Particle Features in Crystalline Cathode Materials for Li‐Ion Batteries Using Nano‐Focused Beam Techniques at Fourth‐Generation Synchrotron Sources10.20517/microstructures.2024.19 ↗
93. Defects and Nanostrain Gradients Control Phase Transition Mechanisms in Single‐Crystal High‐Voltage Lithium Spinel10.1038/s41467-023-42285-4 ↗
94. 10.1021/acs.chemrev.5c0039410.1021/acs.chemrev.5c00394 ↗
95. 10.1016/j.joule.2021.12.00710.1016/j.joule.2021.12.007 ↗
96. 10.1016/j.jpowsour.2013.06.15810.1016/j.jpowsour.2013.06.158 ↗
97. 10.1002/aenm.20220342610.1002/aenm.202203426 ↗
98. Operando Monitoring of the Lithium Spatial Distribution in Lithium Metal Anodes10.1038/s41467-018-04394-3 ↗
99. Probing Jahn–Teller Distortions and Antisite Defects in LiNiO2${\rm LiNiO}_2$ with 7Li$7{\rm Li}$ NMR Spectroscopy and Density Functional Theory10.1021/acs.chemmater.3c03103 ↗
100. New Insights into Lithium Hopping and Ordering in LiNiO2${\rm LiNiO}_2$ Cathodes During Li (De)Intercalation10.1021/acs.chemmater.1c02680 ↗