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Nanotechnology Applications in Sensors Development

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 632

Special Issue Editors


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Guest Editor
Dipartimento di Medicina Sperimentale, Università della Campania “Luigi Vanvitelli”, 80138 Napoli, Italy
Interests: fluorescence optical methods; vibrational spectroscopies; enzymatic optical biosensing; two-photon microscopy; optical properties of turbid media; biophotonics medical applications.
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Dipartimento di Scienze Ecologiche e Biologiche, Università degli Studi della Tuscia, I-01100 Viterbo, Italy
Interests: optical spectroscopy and microscopy; Raman and SERS techniques; light scattering methods; optical biosensing; optical sensing approaches; diagnosis and disease follow-up and study of ionizing radiation on biosystems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nanotechnologies have established new scenarios for the use of optical methods in sensing applications. The miniaturization of matter provides interesting and unique optical properties, enabling the development of new sensing schemes and devices characterized by an increased specificity and sensitivity, reliability, and adaptability, as required for innovative applicative approaches within the qualitative and quantitative determination of analytes of interest across many fields of application. These include pharmaceutical research, medical diagnostics, environmental monitoring, agriculture, industry, and food safety and security. This Special Issue aims to offer an overview of the recent advances in the use of nanotechnologies for the development of optical sensors and their applications. Original research papers and review articles working within this realm are welcomed, showcasing the variety of recent advancements in various fields and their extensive distribution. We encourage you to contact us if you have any questions or would like to discuss your potential contribution in advance.

We look forward to and welcome your participation in this Special Issue.

Dr. Maria Lepore
Dr. Ines Delfino
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nanoparticles, multilayer, core–shell-type nanoparticles for sensing applications
  • nanopatterning of surfaces, surface functionalization, and the self-assembling of structures
  • organization of nanoparticles into periodic or aperiodic functional structures for nanoscale probes, sensors, and devices
  • semiconductor quantum dot technology for sensor development
  • metallic nanoparticles and nanorods for biosensing
  • up-converting nanophores
  • nanosensors for in vitro bioanalysis
  • nanotechnologies for Raman, SERS, and SEIRA sensing
  • fluorescence-based nanosensors
  • DNA-based nanosensors
  • multimodal nanosensors
  • plasmonic nanosensors

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Published Papers (1 paper)

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Research

14 pages, 2474 KiB  
Article
Exploiting Temporal Features in Calculating Automated Morphological Properties of Spiky Nanoparticles Using Deep Learning
by Muhammad Aasim Rafique
Sensors 2024, 24(20), 6541; https://doi.org/10.3390/s24206541 - 10 Oct 2024
Viewed by 274
Abstract
Object segmentation in images is typically spatial and focuses on the spatial coherence of pixels. Nanoparticles in electron microscopy images are also segmented frame by frame, with subsequent morphological analysis. However, morphological analysis is inherently sequential, and a temporal regularity is evident in [...] Read more.
Object segmentation in images is typically spatial and focuses on the spatial coherence of pixels. Nanoparticles in electron microscopy images are also segmented frame by frame, with subsequent morphological analysis. However, morphological analysis is inherently sequential, and a temporal regularity is evident in the process. In this study, we extend the spatially focused morphological analysis by incorporating a fusion of hard and soft inductive bias from sequential machine learning techniques to account for temporal relationships. Previously, spiky Au nanoparticles (Au-SNPs) in electron microscopy images were analyzed, and their morphological properties were automatically generated using a hourglass convolutional neural network architecture. In this study, recurrent layers are integrated to capture the natural, sequential growth of the particles. The network is trained with a spike-focused loss function. Continuous segmentation of the images explores the regressive relationships among natural growth features, generating morphological statistics of the nanoparticles. This study comprehensively evaluates the proposed approach by comparing the results of segmentation and morphological properties analysis, demonstrating its superiority over earlier methods. Full article
(This article belongs to the Special Issue Nanotechnology Applications in Sensors Development)
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