All publications can be found on Zenodo on the SERSing community: SERSing
- Plasmonic MOF Thin Films with Raman Internal Standard for Fast and Ultrasensitive SERS Detection of Chemical Warfare Agents in Ambient Air.
- Marta Lafuente, Sarah De Marchi, Miguel Urbiztondo, Isabel Pastoriza-Santos, Ignacio Pérez-Juste, Jesús Santamaría, Reyes Mallada, and María Pina, ACS Sensors 2021 6 (6), 2241-2251. DOI: 10.1021/acssensors.1c00178
- Raman spectrum matching with contrastive representation learning.
- Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Analyst 2022 10. http://dx.doi.org/10.1039/d2an00403h
- On-chip monitoring of toxic gases: capture and label-free SERS detection with plasmonic mesoporous sorbents.
- Lafuente, Marta & Almazán, Fernando & Bernad, Eduardo & Florea, Ileana & Arenal, Raul & Urbiztondo, Miguel & Mallada, Reyes. (2023). On-chip monitoring of toxic gases: capture and label-free SERS detection with plasmonic mesoporous sorbents. Lab on a chip. (https://doi.org/10.1039/D3LC00136A)
- Visualizing undyed microplastic particles and fibers with plasmon-enhanced fluorescence.
- Wei, X-F., Rindzevicius, T., Wu, K., Bohlen, M., Hedenqvist, M., Boisen, A., & Hakonen, A. (2022). Visualizing undyed microplastic particles and fibers with plasmon-enhanced fluorescence. Chemical Engineering Journal, 442, Article 136117. https://doi.org/10.1016/j.cej.2022.136117
- Plasmonic metal-organic frameworks.
- Zheng G, Pastoriza-Santos I, Pérez-Juste J, Liz-Marzán LM. Plasmonic metal-organic frameworks. SmartMat. 2021; 2: 446–465. https://doi.org/10.1002/smm2.1047
- Nitroaromatic explosives’ detection and quantification using an attention-based transformer on surface-enhanced Raman spectroscopy maps.
- Li, B., Zappala, G., Dumont, E., Boisen, A., Rindzevicius, T., Schmidt, M. N., & Alstrom, T. S. (2023). Nitroaromatic explosives’ detection and quantification using an attention-based transformer on surface-enhanced Raman spectroscopy maps. Analyst, 148, 4787-4798. https://doi.org/10.1039/d3an00446e
- On the effectiveness of partial variance reduction in federated learning with heterogeneous data.
- Li, B., Schmidt, M. N., Alstrom, T. S., & Stich, S. U. (2023). On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. In Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3964-3973). IEEE. https://doi.org/10.1109/CVPR52729.2023.00386
- Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity.
- Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U Stich. Review on openreview: https://openreview.net/forum?id=c5o4HUypq
- An improved analysis of per-sample and per-update clipping in federated learning.
- Li, Bo; Jiang, Xiaowen; Schmidt, Mikkel N; Alstrøm, Tommy Sonne; Stich, Sebastian U; ” The Twelfth International Conference on Learning Representations,,,,2024,
- Exploring the surface-enhanced Raman scattering (SERS) activity of golf nanostructures embedded around nanogaps at wafer scale: Simulations and experiments.
- Lafuente, Marta & Muñoz, Pablo & Berenschot, J.W. & Tiggelaar, Roald & Susarrey-Arce, Arturo & Rodrigo, Sergio & Kooijman, Lucas & García-Blanco, Sonia & Mar, A & Mallada, Reyes & Pina, María & Tas, Niels. (2023). Exploring the surface-enhanced Raman scattering (SERS) activity of gold nanostructures embedded around nanogaps at wafer scale: Simulations and experiments. Applied Materials Today. 35. 101929. Doi:10.1016/j.apmt.2023.101929.
