Johansen, M.; Singh, M. P.; Gault, B.; Liu, F.: Suppressing Lithium Migration in a Carbon Fiber Negative Electrode During Atom Probe Tomography Analysis. Microscopy and Microanalysis 30 (6), pp. 1066 - 1073 (2024)
Johansen, M.; Singh, M. P.; Xu, J.; Asp, L. E.; Gault, B.; Liu, F.: Unravelling lithium distribution in carbon fibre electrodes for structural batteries with atom probe tomography. Carbon 225, 119091 (2024)
Woods, E.; Singh, M. P.; Kim, S.-H.; Schwarz, T.; Douglas, J. O.; El-Zoka, A.; Giulani, F.; Gault, B.: A versatile and reproducible cryo-sample preparation methodology for atom probe studies. Microscopy and Microanalysis, ozad120 29 (6), pp. 1992 - 2003 (2023)
Singh, M. P.; Woods, E.; Kim, S.-H.; Jung, C.; Aota, L. S.; Gault, B.: Facilitating the Systematic Nanoscale Study of Battery Materials by Atom Probe Tomography through in-situ Metal Coating. Batteries & Supercaps 7 (2), e202300403 (2023)
Woods, E.; Aota, L. S.; Schwarz, T.; Kim, S.-H.; Douglas, J. O.; Singh, M. P.; Gault, B.: In-situ cryogenic protective layers and metal coatings in cryogenic FIB. IMC20 - 20th International Microscopy Congress - Pre-congress workshop, Cryogenic Atom Probe Tomography, Busan, South Korea (2023)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.