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Feature Papers in Biosensors Section 2026
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Feature Papers in Biosensors Section 2026

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 950

Special Issue Editors

Prof. Dr. Alexander Star

E-Mail Website
Guest Editor
Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, PA 15260, USA
Interests: nanosensors; carbon nanotubes and graphene; nanoparticles; nanotoxicology; drug delivery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Patrick Wagner

E-Mail Website
Guest Editor
Soft-Matter Physics and Biophysics Section, Department of Physics and Astronomy, KU Leuven, Celestijnenlaan 200 D, 3001 Leuven, Belgium
Interests: label-free biosensing techniques (e.g. impedance spectroscopy, microgravimetry and thermal detection methods); synthetic receptors such as molecularly- and surface-imprinted polymers; functional surfaces and interfaces; medical diagnostics and biomedical engineering; environmental monitoring and food-safety analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Spyridon Kintzios

E-Mail Website
Guest Editor
Laboratory of Cell Technology, Faculty of Biotechnology, Agricultural University of Athens, Athens, Greece
Interests: biosensors; biotechnology; cell culture; cell technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of previous years, where we curated a collection of outstanding papers from our Editorial Board Members (EBMs) and leading scholars, we are pleased to invite you to contribute to this year’s Special Issue on Biosensing Technologies. This issue aims to highlight exceptional contributions in the rapidly advancing field of biosensors. We invite high-quality papers authored by our EBMs or recommended by them, focusing on the latest trends, innovations and cutting-edge developments in biosensor research.

The goal is to assemble a set of insightful, original and influential research articles and reviews that exemplify the most advanced and impactful work in the biosensors field. We encourage our EBMs to share their expertise and perspectives on topics including (but not limited to) the following:

  • Biosensors
  • Lab-on-a-chip technology
  • Optical biosensors
  • Plasmonic biosensors
  • Biosensors for cell analysis
  • Electrochemical biosensors
  • Enzymatic biosensors
  • Graphene-based biosensors
  • Carbon nanotube biosensors
  • Aptamer biosensors

Following the submission deadline, the accepted papers will be compiled into a printed edition and actively promoted to ensure broad readership and significant impact within the biosensors community.

Thank you for your valuable contributions to advancing research in biosensors.

For reference, you can view the Feature Papers from previous years as follows:

Feature Papers in Biosensors Section 2025
https://www.mdpi.com/journal/sensors/special_issues/45V4QUX0B7
Feature Papers in Biosensors Section 2024
https://www.mdpi.com/journal/sensors/special_issues/RLUGY0J367

Prof. Dr. Alexander Star
Prof. Dr. Patrick Wagner
Prof. Dr. Spyridon Kintzios
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • biosensors
  • lab-on-a-chip technology
  • optical biosensors
  • plasmonic biosensors
  • biosensors for cell analysis
  • electrochemical biosensors
  • enzymatic biosensors
  • graphene-based biosensors
  • carbon nanotube biosensors
  • aptamer biosensors

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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22 pages, 2279 KB  
Article
Virtual Mice, Real Errors: A Sensor-Aware Generative Framework for In Silico Ethology
by Reza Sayfoori, Goli Vaisi and Hung Cao
Sensors 2026, 26(10), 2977; https://doi.org/10.3390/s26102977 - 9 May 2026
Viewed by 157
Abstract
Long-duration animal trajectories are central to computational ethology, yet constructing large rodent cohorts remains costly, time-intensive, and constrained by animal-use considerations. We present a sensor-aware generative framework that separates latent behavioral dynamics from sensing-induced observation distortion to synthesize observed-domain trajectories that are behaviorally [...] Read more.
Long-duration animal trajectories are central to computational ethology, yet constructing large rodent cohorts remains costly, time-intensive, and constrained by animal-use considerations. We present a sensor-aware generative framework that separates latent behavioral dynamics from sensing-induced observation distortion to synthesize observed-domain trajectories that are behaviorally plausible while reproducing proxy-referenced observation distortions. The framework combines a run-level semi-Markov ethology model, occupancy calibration, and state-conditioned kinematic generation with a regime-dependent Ultra-Wideband observation channel that explicitly captures Line-of-Sight and Non-Line-of-Sight sensing conditions. Using four UWB sessions, this proof-of-concept study models three states—exploring, feeding, and burrowing—and evaluates realism through state occupancy, state-conditioned kinematic divergence, residual-domain agreement, and mean-squared displacement across time lags. We further assess whether sensor-aware conditioning improves robustness under LoS/NLoS domain shift in downstream trajectory classification. Sensor-aware conditioning yields stable mixed-domain performance with AUC = 0.995, whereas condition-agnostic baselines decline to AUC = 0.974 and AUC = 0.901. These results support the feasibility of sensor-aware in silico ethology as a proof-of-concept framework for controlled robustness studies and algorithm evaluation under proxy-referenced observation distortion. Because the present evaluation is based on four UWB sessions and uses a smoothed UWB-derived reference trajectory rather than independent ground truth, broader applications to synthetic-cohort generation, disease modeling, and statistical power-analysis workflows should be considered future directions requiring validation in larger datasets. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2026)
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Review

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30 pages, 2786 KB  
Review
Modelling Skin Pigmentation Using the Monte Carlo Technique: A Review
by Raghda Al-Halawani, Meha Qassem and Panicos A. Kyriacou
Sensors 2026, 26(8), 2337; https://doi.org/10.3390/s26082337 - 10 Apr 2026
Viewed by 439
Abstract
The impact of skin pigmentation on the accuracy of optical biomedical devices has gained increased attention since the COVID-19 pandemic, particularly following evidence of oximetry measurement bias in dark-skinned individuals. Meanwhile, many computational models utilising the Monte Carlo (MC) technique have been developed [...] Read more.
The impact of skin pigmentation on the accuracy of optical biomedical devices has gained increased attention since the COVID-19 pandemic, particularly following evidence of oximetry measurement bias in dark-skinned individuals. Meanwhile, many computational models utilising the Monte Carlo (MC) technique have been developed as a cost-effective and scalable method for investigating these effects. Hence, this review explores the application of the MC technique in modelling skin pigmentation, focusing specifically on how melanin in the epidermis is represented across different studies. First, the biological mechanisms of pigmentation and current stratification methods are outlined to contextualise the variability in skin tone, followed by the principles of MC modelling, including photon scattering, absorption, reflection, and detection. Following a screening and exclusion process, 50 studies were evaluated in terms of how melanin concentration and distribution are incorporated into MC models and their applications, revealing a range of approaches that include analytical equations, experimental optical property measurements, or hybrid methods. The benefits and limitations of each approach is discussed, in addition to emerging advancements such as heterogeneous melanin distribution and the relation between optical properties and skin colour classification scales. Overall, the review outlines the current methodological approaches utilised for skin pigmentation modelling and offers a reference framework for researchers seeking to improve the representation of skin pigmentation in MC-based optical simulations. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2026)
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