2025
T. Le-Dinh, H. Schlenz, N.H. Menzler, A.A. Franco, O. Guillon, Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells, Energy and AI (2025) in preparation.
T. Le-Dinh, M. Alabdali, F.M. Zanotto, H. Schlenz, N.H. Menzler, O. Guillon, A.A. Franco, Coarse-Grained Physics-based Modelling for Tape Casting of Fuel-Electrode Supports in Solid Oxide Cells, ChemRixv (2025) https://doi.org/10.26434/chemrxiv-2025-wb05t
H. Schlenz, T. Bronger, M. Selzer, B. Nestler, S. Enahoro, L. Riem, Research Data Management - A Practical Introduction, Book, Zeonodo (2025), https://doi.org/10.5281/zenodo.16870779
H. Schlenz, T. Bronger, M. Selzer, B. Nestler, S. Enahoro, L. Riem, Forschungsdatenmanagement - Eine praxisorientierte Einführung, Buch, Zeonodo (2025), https://doi.org/10.5281/zenodo.16870414
F. Zeng, K. Ran, C. Dellen, H. Schlenz, J. Mayer, R. Schwaiger, W.A. Meulenberg, S. Baumann, A novel mixed-conducting network in all-oxide composites: overcoming traditional percolation constraints, J. Mater. Chem. A, 13 (2025) 14940-14956, DOI: 10.1039/d4ta06889k
2024
P. Agharezaei, N.G. Tomohiro, H. Kobayashi, H. Schlenz, Y. Miho, K.K. Ghuman, Unraveling the Enhanced N2 Activity on CuNi Alloy Catalysts for Ammonia Production: Experiments, DFT, and Statistical Analysis, J. Phys. Chem. C128 (2024) 3703-3717.
2023
W. Zhou, F. Zeng, J. Malzbender, H. Schlenz, W. Deibert, D. Sergeev, I. Povstugar, R. Schwaiger, A. Nijmeijer, M. Müller, O. Guillon, W.A. Meulenberg, Promoting densification and grain growth of BaCe0.65Zr0.2Y0.15O3-δ, Journal of materials research and technology 27 (2023) 3531-3538.
2022
J. Leys, Y. Ji, M. Klinkenberg, P. Kowalski, H. Schlenz, S. Neumeier, D. Bosbach, G. Deissmann, Monazite-type SmPO4 as potential nuclear waste form: Insights into radiation effects from ion-beam irradiation and atomistic simulations, Materials 15 (2022) 3434, https://doi.org/10.3390/ma15103434
T. Bronger, M. Flemming, H. Schlenz, M. Selzer, SciMesh: RDF graphs for scientific knowlege, Zenodo (2022), DOI: 10.5281/zenodo.5878990
H. Schlenz, S. Sandfeld, Applications of Machine Learning to the Study of Crystalline Materials, Crystals 12(2022) 1070, https://doi.org/10.3390/cryst12081070
H. Schlenz, S. Baumann, W.A. Meulenberg, O. Guillon, The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning, Crystals 12 (2022)947, https://doi.org/10.3390/cryst12070947
B. Nestler, P.F. Pelz, R.H. Schmitt, M. Berger, H. Dierend, B. Farnbacher, B. Flemisch, D. Gläser, I. Heine, N. Hoppe, G. Jagusch, R. Lachmayer, J. Lemmer, J. Linxweiler, A.I. Metzmacher, I. Mozgova, N. Preuß, M. Richter, S. Roski, H. Schlenz, M. Selzer, C. Stemmer, Nationale Forschungsdateninfrastruktur für die Ingenieurwissenschaften (NFDI4Ing), Publiziert in: Vincent Heuveline, Nina Bisheh (Hg.): E-Science-Tage 2021, Share Your Research Data. Heidelberg: heiBOOKS, 2022, https://doi.org/10.11588/heibooks.979.c13739
2021
S.I. Ecker, J. Dornseiffer, J. Werner, H. Schlenz, Y.J. Sohn, F.S. Sauerwein, S. Baumann, H.J.M. Bouwmeester, O. Guillon, T.E. Weirich, W.A. Meulenberg, Novel low-temperature lean NOx storage materials based on La0.5Sr0.5Fe1-xMxO3-δ/Al2O3 infiltration composites (M = Ti, Zr, Nb), Applied Catalysis B, 286 (2021), DOI: 10.1016/j.apcatb.2021.119919