Jens Eriksson

Associate Professor, Docent

Head of Applied sensor science unit

The SIO Grafen Graduate Network

Jens Eriksson, Associate Professor, IFM.

New national network for 2D-materials

In this video Jens Eriksson, Associate Professor at the Department for Physics, Chemistry and Biology, talks about the new graduate network 2D-materials. The purpose is to connect young researchers in 2D-materials on a national level, and to connect them to industry.

Publications

2026

Carlito S. Ponseca, Mohammed O. Musa, Feng Wang, Ignas Nevinskas, Andrius Arlauskas, Chiara Musumeci, Ali Shuaib, Fatemah Aldousari, Jens Eriksson, Kaibo Zheng, Feng Gao (2026)

Defect mediated pulse terahertz emission from thiocyanate-treated hybrid perovskite nanoparticles: role of the orientation of built-in surface electric field Scientific Reports, Vol. 16, Article 11542 (Article in journal)

https://dx.doi.org/10.1038/s41598-026-42017-w

Ivan I. Shtepliuk, Lingyin Meng, Christer Borgfeldt, Jens Eriksson, Donatella Puglisi (2026)

Biomarker‐Agnostic Detection of Ovarian Cancer from Blood Plasma Using a Machine Learning‐Driven Electronic Nose Advanced Intelligent Systems, Article e202500838 (Article in journal)

https://dx.doi.org/10.1002/aisy.202500838

2025

Donatella Puglisi, Jens Eriksson, Kerstin Montelius, Emanuela Tavaglione, Barbara Fabbri, Rebecca Stenberg, Ivan I. Shtepliuk (2025)

Machine Learning-Enhanced Odor Detection System asNext-Generation Forensic Technology AMA Proceedings, p. 120-121 (Conference paper)

https://dx.doi.org/10.5162/EUROSENSORS2025/T4.3.4

Donatella Puglisi, Ivan I. Shtepliuk, Jens Eriksson (2025)

Integration of Machine Learning for Next Generation Gas Sensor Technology Sensors and Microsystems: Proceedings of AISEM 2025, p. 399-403 (Conference paper)

https://dx.doi.org/10.1007/978-3-032-08271-8_63

Jens Eriksson, Donatella Puglisi, Filip Herbst, Arturas Dobilas, Ivan Shtepliuk, Ulrika Joneborg, Henrik Falconer, Angelique Floter Radestad, Christer Borgfeldt (2025)

Machine learning-enhanced gas sensor technology identifies ovarian and endometrial cancer of all stages through plasma volatile organic compound patterns eBioMedicine, Vol. 122, Article 106027 (Article in journal)

https://dx.doi.org/10.1016/j.ebiom.2025.106027

News

AI-boosted electronic nose detects ovarian cancer

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method is precise and, according to the LiU researchers behind the study, it could eventually be used to find many different cancers.