Plasmonic Sensing Studies of a Gas-Phase Cystic Fibrosis Marker in Moisture Laden Air

Development of Liquid-Phase Plasmonic Sensor Platforms for Prospective Biomedical Applications

Localized Surface Plasmon Resonance (LSPR) is an optical method for detecting changes in refractive index by the interaction between incident light and delocalized electrons within specific metal thin films' localized "hot spots". LSPR-based sensors possess advantages, including their compact size, enhanced sensitivity, cost-effectiveness, and suitability for point-of-care applications. This research focuses on the development of LSPR-based nanohole arrays (NHAs) as a platform for monitoring probe/target binding events in real time without labeling, for low-level biomolecular target detection in biomedical diagnostics. To achieve this objective, this study involves creating a liquid-phase setup for capturing target molecules. Finite-difference time-domain simulations revealed that a 75 nm thickness of gold (Au) is ideal for NHA structures, which were visually examined using scanning electron microscopy. To illustrate the functionality of the liquid-phase sensor, a PDMS microfluidic channel was fabricated using a 3D-printed mold with a glass slide base and a top glass cover slip, enabling reflectance-mode measurements from each of four device sectors. This study shows the design, fabrication, and assessment of NHA-based LSPR sensor platforms within a PDMS microfluidic channel, confirming the sensor's functionality and reproducibility in a liquid-phase environment.

Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring

Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO₂ nanopillars covered with a 15 nm metal-organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5-80 μmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.