Khazaei, M., Ranjbar, A., Kang, Y., Liang, Y., Khaledialidusti, R., Bae, S., Raebiger, H., Wang, V., Han, M. J., Mizoguchi, H., Bahramy, M. S., Kühne, T., Belosludov, R. V., Ohno, K., &; Hosono, H. (2022). Electronic Structures of Group III–V Element Haeckelite Compounds: A Novel Family of Semiconductors, Dirac Semimetals, and Topological Insulators. Advanced Functional Materials, 32(20), Article 2110930.https://doi.org/10.1002/adfm.202110930 

Schulze Lammers, B., López-Salas, N., Stein Siena, J., Mirhosseini, H., Yesilpinar, D., Heske, J. J., Kühne, T., Fuchs, H., Antonietti, M., &; Mönig, H. (2022). Real-Space Identification of Non-Noble Single Atomic Catalytic Sites within Metal-Coordinated Supramolecular Networks. ACS Nano, 16(9), 14284–14296.https://doi.org/10.1021/acsnano.2c04439 

Henao Aristizabal, A., Gohar, Y., Whilhelm, R., &; Kühne, T. (2022). On the Role of Hydrogen Bond Strength and Charge Transfer of a Diels-Alder Reaction On-Water: Semiempirical and Free Energy Calculations. American Chemical Society (ACS).

Khajehpasha, E. R., Finkler, J. A., Kühne, T., &; Ghasemi, A. (2022). CEN: Improved charge equilibration via neural network technique. Physical Review B, 105(14), Article 144106.https://doi.org/10.1103/physrevb.105.144106 

Elizabeth, A., Sahoo, S. K., Phirke, H., Kodalle, T., Kühne, T., Audinot, J.-N., Wirtz, T., Redinger, A., Kaufmann, C. A., Mirhosseini, H., &; Mönig, H. (2022). Surface Passivation and Detrimental Heat-Induced Diffusion Effects in RbF-Treated Cu(In,Ga)Se<sub>2</sub> Solar Cell Absorbers. ACS Applied Materials &;amp; Interfaces, 14(29), 34101–34112.https://doi.org/10.1021/acsami.2c08257 

Raghuwanshi, M., Chugh, M., Sozzi, G., Kanevce, A., Kühne, T., Mirhosseini, H., Wuerz, R., &; CojocaruMirédin, O. (2022). Fingerprints Indicating Superior Properties of Internal Interfaces in Cu(In,Ga)Se            <sub>2</sub>            ThinFilm Solar Cells. Advanced Materials, 34(37), Article 2203954.https://doi.org/10.1002/adma.202203954 

Ibaceta-Jaña, J., Chugh, M., Novikov, A. S., Mirhosseini, H., Kühne, T., Szyszka, B., Wagner, M. R., &; Muydinov, R. (2022). Do Lead Halide Hybrid Perovskites Have Hydrogen Bonds? The Journal of Physical Chemistry C, 126(38), 16215–16226.https://doi.org/10.1021/acs.jpcc.2c02984 

Lepre, E., Heske, J. J., Nowakowski, M., Scoppola, E., Zizak, I., Heil, T., Kühne, T., Antonietti, M., López-Salas, N., &; Albero, J. (2022). Ni-based electrocatalysts for unconventional CO2 reduction reaction to formic acid. Nano Energy, 97, Article 107191.https://doi.org/10.1016/j.nanoen.2022.107191 

Balos, V., Kaliannan, N. K., Elgabarty, H., Wolf, M., Kühne, T., &; Sajadi, M. (2022). Time resolved THz-Raman spectroscopy reveals that cations and anions distinctly modify intermolecular interactions of water. LibreCat University.https://doi.org/10.5281/ZENODO.6514905 

Hoffmann, C., Hoppe, J. A., &; Ziemann, N. (2022). Who has the future in mind? Gender, time perspectives, and pro-environmental behaviour. Environmental Research Letters, 17(10), Article 104026.https://doi.org/10.1088/1748-9326/ac9296 

Papenkordt, J., Gabriel, S., Thommes, K., &; Dumitrescu, R. (2022). Künstliche Intelligenz in der industriellen Arbeitswelt - Studie zum Status Quo in der Region OstWestfalenLippe. Kompetenzzentrum Arbeitswelt.Plus.https://doi.org/10.55594/tmao3234 

Kharatyan, A., Humpert, L., Anacker, H., Dumitrescu, R., Wäschle, M., Albers, A., &; Horstmeyer, S. (2022). Künstliche Intelligenz im Engineering. Zeitschrift Für Wirtschaftlichen Fabrikbetrieb, 117(6), 427–431.https://doi.org/10.1515/zwf-2022-1074 

Meyer, M., Wiederkehr, I., Koldewey, C., &; Dumitrescu, R. (2022). Planning the Analysis of Use Phase Data in Product Planning. Proceedings of the Design Society, 2, 753–762.https://doi.org/10.1017/pds.2022.77 

Meyer, M., Fichtler, T., Koldewey, C., &; Dumitrescu, R. (2022). How can Data Analytics Results be Exploited in the Early Phase of Product Development? 13 Design Principles for Data-Driven Product Planning.  AMCIS 2022 Proceedings. AMCIS 2022, Minneapolis.

Meyer, M., Panzner, M., Koldewey, C., &; Dumitrescu, R. (2022). 17 Use Cases for Analyzing Use Phase Data in Product Planning of Manufacturing Companies. Procedia CIRP, 107, 1053–1058.https://doi.org/10.1016/j.procir.2022.05.107 

Wiecher, C., Mandel, C., Günther, M., Fischbach, J., Greenyer, J., Greinert, M., Wolff, C., Dumitrescu, R., Mendez, D., &; Albers, A. (2022). Model-based Analysis and Specification of Functional Requirements and Tests for Complex Automotive Systems. ArXiv Preprint.

Joppen, R., Kühn, A., Förster, M., &; Dumitrescu, R. (2022). Evaluation of Industry 4.0 Applications in Production. Journal of the Knowledge Economy.https://doi.org/10.1007/s13132-022-00959-2 

Japs, S., Schmidt, S., Kargl, F., Kaiser, L., Kharatyan, A., &; Dumitrescu, R. (2022). Collaborative Modeling of Use Case &;amp; Damage Scenarios in Online Workshops Using a 3D Environment. Proceedings of the Design Society, 2, 1599–1608.https://doi.org/10.1017/pds.2022.162 

Japs, S., Kargl, F., Anacker, H., &; Dumitrescu, R. (2022). Why make it hard? - Usage of aggregated statistical data for risk assessment of damage scenarios in the context of ISO/SAE 21434. Procedia CIRP, 109, 293–298.https://doi.org/10.1016/j.procir.2022.05.252 

Mager, T., Jürgenhake, C., &; Dumitrescu, R. (2022). Efficient method for determining substrate parameters of additive manufactured spatial circuit carriers. 2022 14th German Microwave Conference (GeMiC), 224–227.