Magee, C. W.; Lutz, N.; Renelle, D.; Baptiste, G.; Simon, B.: Technical note: Investigation into the mechanism of chemical abrasion using SHRIMP, Raman spectroscopy and atom probe tomography. EGUSphere, pp. 1 - 23 (2025)
Lee, C.-G.; Nallathambi, V.; Kang, T.; Aota, L. S.; Reichenberger, S.; El-Zoka, A.; Choi, P.-P.; Gault, B.; Kim, S.-H.: Magnetocaloric effect of Fe47.5Ni37.5Mn15 bulk and nanoparticles: A cost-efficient alloy for room temperature magnetic refrigeration. arXiv (2024)
Kim, S.-H.; Yoo, Su, S.-H.; Aota, L. S.; El-Zoka, A.; Kang, P. W.; Lee, Y.; Gault, B.: B dopant evolution in Pd catalysts after H evolution/oxidation reaction in alkaline environment. arXiv (2023)
Douglas, J. O.; Conroy, M. A.; Giuliani, F.; Gault, B.: In-situ sputtering from the micromanipulator to enable cryogenic preparation of specimens for atom probe tomography by focused-ion beam. arXiv (2022)
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.