Three awards for Gdańsk Tech researchers and PhD students for research on sensors
Sensors for detecting viral RNA (ribonucleic acid) and glucose in urine, as well as a comparison of data processing methods in the determination of C-reactive protein (CRP) – scientists and PhD students from the Faculty of Electronics, Telecommunications and Informatics presented the results of their research during the Biosensors 2025 conference in Portugal and were awarded for the best poster presentations.
Sensors for detecting viral RNA (ribonucleic acid) and glucose in urine, as well as a comparison of data processing methods in the determination of C-reactive protein (CRP) – scientists and PhD students from the Faculty of Electronics, Telecommunications and Informatics presented the results of their research during the Biosensors 2025 conference in Portugal and were awarded for the best poster presentations.
Gdańsk Tech was represented by the OpticLab group from the Department of Metrology and Optoelectronics (Faculty of Electronics, Telecommunications and Informatics). The team won three out of ten awards granted during the conference (twice first place and once second place). The event was attended by 1180 participants from 54 countries.
Fiber optic sensor will help limit the spread of viruses
The poster “Biofunctionalized fiber-optic sensors for viral RNA detection,” authored by MSc Eng. Patryk Sokołowski, PhD Eng. Paweł Wityk, MSc Eng. Maria Babińska, PhD Joanna Raczak-Gutknecht (GUMed), DSc Eng. Michał Sobaszek, prof. Gdańsk Tech, MSc Pharm. Wiktoria Brzezińska, and prof. DSc Eng. Małgorzata Szczerska, received the award for the best presentation in Poster Session 1. The poster presentation was delivered by PhD student MSc Eng. Patryk Sokołowski.
The developed fiber optic sensor uses a microsphere at the fiber tip, coated with a layer of gold and oligonucleotide probes, enabling selective detection of viral RNA sequences. Thanks to optical interferometry, it can detect the presence of the virus within a minute at very low concentrations of genetic material. The technology can be easily adapted to other pathogens and represents a promising tool to help limit epidemics.
Traditional methods still more reliable than complex AI-based algorithms
First place in Poster Session 2 was awarded to a poster presented by MSc Eng. Kacper Cierpiak. The researchers compared spectral processing methods obtained from biophotonic sensors for the detection of CRP – a marker of inflammatory states. They analyzed classical machine learning algorithms, deep learning networks, and signal denoising techniques.
The project results showed that although advanced algorithms improved data quality in most spectral ranges, in some they introduced distortions. The best classification effectiveness was achieved by the classical model, indicating that for small, noisy datasets, traditional methods can be more stable and reliable than complex AI solutions.
Health monitoring – sensor enables glucose detection in urine
Second place in Poster Session 3 was won by the presentation of MSc Eng. Maria Babińska and MSc Eng. Adam Władziński, dedicated to a fiber optic optical sensor for non-invasive glucose detection in urine.
The device is based on a fiber terminated with a microsphere and utilizes optical interference. Machine learning algorithms enabled precise analysis of spectroscopic data and detection of even slight changes in glucose concentration. The technology may lead to the creation of an inexpensive, portable system for daily health monitoring of patients with diabetes and other metabolic disorders.