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Simultaneous PIV–LIF measurements using RuPhen and a color camera
Shah, J., Mucignat, C., Lunati, I., & Rösgen, T. (2024). Simultaneous PIV–LIF measurements using RuPhen and a color camera. Experiments in Fluids, 65, 3 (15 pp.). https://doi.org/10.1007/s00348-023-03742-4
Amorphous matters: heterogeneity and defects of nanopore silica surfaces enhance CO<sub>2</sub> adsorption
Turchi, M., Galmarini, S., & Lunati, I. (2024). Amorphous matters: heterogeneity and defects of nanopore silica surfaces enhance CO2 adsorption. Journal of Non-Crystalline Solids, 624, 122709 (11 pp.). https://doi.org/10.1016/j.jnoncrysol.2023.122709
A machine learning approach for computation of cardiovascular intrinsic frequencies
Alavi, R., Wang, Q., Gorji, H., & Pahlevan, N. M. (2023). A machine learning approach for computation of cardiovascular intrinsic frequencies. PLoS One, 18(10), e0285228 (20 pp.). https://doi.org/10.1371/journal.pone.0285228
Data-driven stochastic particle scheme for collisional plasma simulations
Chung, K., Fei, F., Gorji, M. H., & Jenny, P. (2023). Data-driven stochastic particle scheme for collisional plasma simulations. Journal of Computational Physics, 492, 112400 (15 pp.). https://doi.org/10.1016/j.jcp.2023.112400
Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January-March 2022)
Gorji, H., Stauffer, N., Lunati, I., Caduff, A., Bühler, M., Engel, D., … Renz, H. (2023). Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January-March 2022). Epidemics, 43, 100680 (9 pp.). https://doi.org/10.1016/j.epidem.2023.100680
Superhydrophobic and flexible aerogels and xerogels derived from organosilane precursors
Kanamori, K., Stojanovic, A., Pajonk, G. M., Nadargi, D. Y., Rao, A. V., Nakanishi, K., & Koebel, M. M. (2023). Superhydrophobic and flexible aerogels and xerogels derived from organosilane precursors. In M. A. Aegerter, N. Leventis, M. Koebel, & S. A. Steiner III (Eds.), Springer handbooks. Springer handbook of aerogels (pp. 367-391). https://doi.org/10.1007/978-3-030-27322-4_15
Critical assessment of various particle Fokker-Planck models for monatomic rarefied gas flows
Kim, S., Gorji, H., & Jun, E. (2023). Critical assessment of various particle Fokker-Planck models for monatomic rarefied gas flows. Physics of Fluids, 35(4), 046117 (23 pp.). https://doi.org/10.1063/5.0143195
A lightweight neural network designed for fluid velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2023). A lightweight neural network designed for fluid velocimetry. Experiments in Fluids, 64(10), 161 (17 pp.). https://doi.org/10.1007/s00348-023-03695-8
Prediction of the surface chemistry of calcium aluminosilicate glasses
Miri Ramsheh, S., Turchi, M., Perera, S., Schade, A. M., Okhrimenko, D. V., Stipp, S. L. S., … Andersson, M. P. (2023). Prediction of the surface chemistry of calcium aluminosilicate glasses. Journal of Non-Crystalline Solids, 620, 122597 (7 pp.). https://doi.org/10.1016/j.jnoncrysol.2023.122597
A lightweight convolutional neural network to reconstruct deformation in BOS recordings
Mucignat, C., Manickathan, L., Shah, J., Rösgen, T., & Lunati, I. (2023). A lightweight convolutional neural network to reconstruct deformation in BOS recordings. Experiments in Fluids, 64(4), 72 (16 pp.). https://doi.org/10.1007/s00348-023-03618-7
Atomistic simulations of calcium aluminosilicate interfaced with liquid water
Vuković, F., Garcia, N. A., Perera, S., Turchi, M., Andersson, M. P., Solvang, M., … Walsh, T. R. (2023). Atomistic simulations of calcium aluminosilicate interfaced with liquid water. Journal of Chemical Physics, 159(10), 104704 (11 pp.). https://doi.org/10.1063/5.0164817
Wicking dynamics in yarns
Fischer, R., Schlepütz, C. M., Zhao, J., Boillat, P., Hegemann, D., Rossi, R. M., … Carmeliet, J. (2022). Wicking dynamics in yarns. Journal of Colloid and Interface Science, 625, 1-11. https://doi.org/10.1016/j.jcis.2022.04.060
Wicking fingering in electrospun membranes
Fischer, R., Schoeller, J., Rossi, R. M., Derome, D., & Carmeliet, J. (2022). Wicking fingering in electrospun membranes. Soft Matter, 18(30), 5662-5675. https://doi.org/10.1039/d2sm00472k
Wicking through complex interfaces at interlacing yarns
Fischer, R., Schlepütz, C. M., Rossi, R. M., Derome, D., & Carmeliet, J. (2022). Wicking through complex interfaces at interlacing yarns. Journal of Colloid and Interface Science, 626, 416-425. https://doi.org/10.1016/j.jcis.2022.06.103
Results from Canton Grisons of Switzerland suggest repetitive testing reduces SARS-CoV-2 incidence (February-March 2021)
Gorji, H., Lunati, I., Rudolf, F., Vidondo, B., Hardt, W. D., Jenny, P., … Caduff, A. (2022). Results from Canton Grisons of Switzerland suggest repetitive testing reduces SARS-CoV-2 incidence (February-March 2021). Scientific Reports, 12, 19538 (7 pp.). https://doi.org/10.1038/s41598-022-23986-0
Competing phases in the room-temperature M<sub>2</sub>(2,6-ndc)<sub>2</sub>(dabco) metal-organic framework thin film synthesis
Hamon, L., Andrusenko, I., Borzì, A., Stiefel, M., Carl, S., Frison, R., … Collings, I. E. (2022). Competing phases in the room-temperature M2(2,6-ndc)2(dabco) metal-organic framework thin film synthesis. Materials Advances, 3(17), 6869 (9 pp.). https://doi.org/10.1039/d2ma00389a
Infection risk in cable cars and other enclosed spaces
Lunati, I., & Mucignat, C. (2022). Infection risk in cable cars and other enclosed spaces. Indoor Air, 32(8), e13094 (14 pp.). https://doi.org/10.1111/ina.13094
A study on diurnal microclimate hysteresis and plant morphology of a <em>Buxus sempervirens</em> using PIV, infrared thermography, and X-ray imaging
Manickathan, L., Defraeye, T., Carl, S., Richter, H., Allegrini, J., Derome, D., & Carmeliet, J. (2022). A study on diurnal microclimate hysteresis and plant morphology of a Buxus sempervirens using PIV, infrared thermography, and X-ray imaging. Agricultural and Forest Meteorology, 313, 108722 (16 pp.). https://doi.org/10.1016/j.agrformet.2021.108722
Higher-order accurate neural network for real-time fluid velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2022). Higher-order accurate neural network for real-time fluid velocimetry. In Proceedings of the 20th international symposium on the application of laser and imaging techniques to fluid mechanics 2022 (p. (13 pp.). Sine nomine.
Kinematic training of convolutional neural networks for particle image velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2022). Kinematic training of convolutional neural networks for particle image velocimetry. Measurement Science and Technology, 33(12), 124006 (16 pp.). https://doi.org/10.1088/1361-6501/ac8fae
 

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