Publications

A digital twin for parallel liquid-state nuclear magnetic resonance spectroscopy
He, M.; Faderl, D.; MacKinnon, N.; Cheng, Y.-T.; Buyens, D.; Jouda, M.; Luy, B.; Korvink, J. G.
2024. Communications Engineering, 3 (1), Art.-Nr.: 90. doi:10.1038/s44172-024-00233-0

Accelerated Screening of Protein–Ligand Interactions via Parallel T -Weighted F-MRI
Faderl, D.; Chenakkara, A.; Jouda, M.; MacKinnon, N.; Gossert, A. D.; Korvink, J. G.
2024. Analytical Chemistry, 96 (24), 9859–9865. doi:10.1021/acs.analchem.4c00333

Optimization of laser-induced OH Radical Generation in Water
Geiger, L. M.; Jouda, M.; Länge, K.; Rapp, M.; Voigt, A.; Howard, I.; MacKinnon, N.; Korvink, J.
2023, April 18. Experimental Nuclear Magnetic Resonance Conference (ENC 2023), Pacific Grove, CA, USA, April 16–21, 2023

Lenz Lenses in a Cryoprobe: Boosting NMR Sensitivity Toward Environmental Monitoring of Mass-Limited Samples
Bastawrous, M.; Ghosh Biswas, R.; Soong, R.; Jouda, M.; MacKinnon, N.; Mager, D.; Korvink, J. G.; Simpson, A. J.
2022. Analytical Chemistry, 95 (2), 1327–1334. doi:10.1021/acs.analchem.2c04203

Dataset to the publication ’Automatic Adaptive Gain for Magnetic Resonance Sensitivity Enhancement’
Jouda, M.
2022, January 31. doi:10.5445/IR/1000142540

Selective excitation enables encoding and measurement of multiple diffusion parameters in a single experiment
MacKinnon, N.; Alinaghian, M.; Silva, P.; Gloge, T.; Luy, B.; Jouda, M.; Korvink, J. G.
2021. Magnetic resonance, 2 (2), 835–842. doi:10.5194/mr-2-835-2021

Integrated impedance sensing of liquid sample plug flow enables automated high throughput NMR spectroscopy
Nassar, O.; Jouda, M.; Rapp, M.; Mager, D.; Korvink, J. G.; MacKinnon, N.
2021. Microsystems and Nanoengineering, 7 (1), Art.-Nr.: 30. doi:10.1038/s41378-021-00253-2

Integrated impedance sensors in a microfluidic system: Toward a fully automated high throughput nmr spectroscopy
Nassar, O.; Jouda, M.; Korvink, J.; Mager, D.; Mackinnon, N.
2020. 24th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2020; Virtual, Online; 4 through 9 October 2020, 723–724, Chemical and Biological Microsystems Society (CBMS)

Motion prediction enables simulated MR-imaging of freely moving model organisms
Reischl, M.; Jouda, M.; MacKinnon, N.; Fuhrer, E.; Bakhtina, N.; Bartschat, A.; Mikut, R.; Korvink, J. G.
2019. PLoS Computational Biology, 15 (12), e1006997. doi:10.1371/journal.pcbi.1006997

”Small is beautiful” in NMR
Korvink, J. G.; MacKinnon, N.; Badilita, V.; Jouda, M.
2019. Journal of magnetic resonance, 306, 112–117. doi:10.1016/j.jmr.2019.07.012

Broadband and multi-resonant sensors for NMR
Davoodi, H.; Jouda, M.; Korvink, J. G.; MacKinnon, N.; Badilita, V.
2019. Progress in nuclear magnetic resonance spectroscopy, 112-113, 34–54. doi:10.1016/j.pnmrs.2019.05.001

Automatic adaptive gain for magnetic resonance sensitivity enhancement
Jouda, M.; Fuhrer, E.; Silva, P.; Korvink, J. G.; MacKinnon, N.
2019. Analytical chemistry. doi:10.1021/acs.analchem.8b05148

Nuclear Magnetic Resonance Microscopy for In Vivo Metabolomics, Digitally Twinned by Computational Systems Biology, Needs a Sensitivity Boost
Korvink, J. G.; Badilita, V.; Bordonali, L.; Jouda, M.; Mager, D.; MacKinnon, N.
2018. Sensors and materials, 30 (2), 157–166. doi:10.18494/SAM.2018.1711