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

biomedical diagnostics biosensor liquid-phase diagnostics localized surface plasmon resonance nanosensors plasmonic sensor

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
28 Dec 2023
Historique:
received: 15 11 2023
revised: 15 12 2023
accepted: 25 12 2023
medline: 11 1 2024
pubmed: 11 1 2024
entrez: 11 1 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 38203048
pii: s24010186
doi: 10.3390/s24010186
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Sezin Sayin (S)

Department of Electrical and Computer Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA.

You Zhou (Y)

Department of Electrical and Computer Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA.

Sheng Wang (S)

Department of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA.

Andres Acosta Rodriguez (A)

3D Enviro, Barboursville, VA 22923, USA.

Mona Zaghloul (M)

Department of Electrical and Computer Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA.

Classifications MeSH