Items where Greenwich Author is "Le Houx, James"
Dark-Field X-ray Microscopy (DFXM), chemo-mechanics, strain evolution, single particle analysis, defect nucleation
Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Mistry, Jamini Jessica, Li, Yaozhu, Lesage, Louis, Staeck, Steffen, Bird, Robert and Spencer-Jolly, Dominic
(2026)
The topographic origin of chemomechanical failure in high-nickel cathodes revealed by operando darkfield X-ray microscopy.
[Working Paper]
(doi:10.26434/chemrxiv.15002900/v1)
Enceladus, fluid inclusions, Natron, astrobiology, correlative X-ray tomography and diffraction, In-situ freezing
Perera, Liam, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Leonardi, Alberto, Day, Sarah and Thompson, Stephen
(2026)
Fluid inclusions in natron: a window into the interior of Enceladus.
[Working Paper]
(doi:10.21203/rs.3.rs-8406896/v1)
Swiss Nanoscience Institute Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung. Grant Number: Sinergia CRSII5_202296 Rutherford Appleton Laboratory Faraday Institution alloy interlayer, all-solid-state battery, image subtraction method, Li metal, operando XCT, zero-excess lithium anode
Xu, Linfeng, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Kachkanov, Vyacheslav, Zhang, Jinsong, Norbert Wullich, Robin, Fankhauser, Matthias, Löffel, Kaspar, Schmidt, Thomas J. and El Kazzi, Mario
(2026)
Operando X‐Ray computed tomography reveals the role of interfacial nucleation nanolayers in suppressing mechanical failure in zero‐excess lithium all‐solid‐state batteries.
Small, 22 (10):e12284.
ISSN 1613-6810 (Print), 1613-6829 (Online)
(doi:10.1002/smll.202512284)
XCT, fracture mechanics
Wright, Conor, Ramsdale, Emily, McKay Fletcher, Daniel, Williams, Katherine, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673 and Ruiz, Siul
(2026)
Understanding root biomechanics in high-strength environments- assessing the feasibility of penetration and fracture FE models with SRXCT.
In: EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026.
EGU26
(7232).
Copernicus GmbH, Göttingen, Germany.
(doi:10.5194/egusphere-egu26-7232)
autonomous experimentation, large-scale user facilities, AI for science, taxonomy, Operational Design Domains (ODD), sim-to-real transfer
Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673
(2026)
Benchmarking autonomy in scientific experiments: a hierarchical taxonomy for autonomous large-scale facilities.
[Working Paper]
(doi:10.48550/arXiv.2601.06978)
autonomous experimentation, operando characterisation, battery degradation, scientific AI, physics,. informed machine learning
Lu, Emily, Perez, Gabriel, Baker, Peter, Irving, Daniel, Kumar, Santosh, Celorrio, Veronica, Britto, Sylvia, Headen, Thomas F., Gomez-Gonzalez, Miguel, Wright, Connor, Green, Calum, Young, Robert Scott, Kirichek, Oleg, Mortazavi, Ali, Day, Sarah, Antony, Isabel, Wright, Zoe, Wood, Thomas, Snow, Tim, Thiyagalingam, Jeyan, Quinn, Paul, Jones, Martin Owen, David, William and Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673
(2026)
Autonomous battery research: principles of heuristic operando experimentation.
[Working Paper]
(doi:10.48550/arxiv.2601.00851)
operando X-ray diffraction, lithium-ion batteries, impedance-based modelling, bilayer electrodes, reaction heterogeneity, depth profiling
Tan, Hwee Jien, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Grey, Clare P and De Volder, Michael
(2026)
Particle size-graded bilayer electrodes for enhanced transport in
layered transition metal oxide cathodes.
Journal of The Electrochemical Society (JES).
ISSN 0013-4651 (Print), 1945-7111 (Online)
(doi:10.1149/1945-7111/ae6f4e)
quantum machine learning, quantum kernel, support vector machine, trabecular bone, micro-CT, dimensionality reduction, UMAP
Florez, Isabella, Farhat, Ahmed, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Altamura, Edoardo and Tozzi, Gianluca
(2026)
Quantum kernel support vector machines for trabecular bone classification: comparing feature reduction strategies on synthetic micro-CT data.
[Working Paper]
(doi:10.64898/2026.05.04.722627)
strain, bone, X-ray computed tomography, digital volume correlation, deep learning, convolutional neural network, data-driven image mechanics
Valijonov, Jon, Soar, Peter ORCID: https://orcid.org/0000-0003-1745-9443, Le Houx, James
ORCID: https://orcid.org/0000-0002-1576-0673 and Tozzi, Gianluca
(2026)
Evaluation of direct strain field prediction in bone with data-driven image mechanics (D2IM-Strain).
[Working Paper]
(doi:10.64898/2026.03.31.715417)
Up a level