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Smart Wearable EEG Devices: A Review of Lightweight, Multi-Sensor Systems for Sleep and Everyday Neurophysiology
Wearable Wireless EMG Sensors for Monitoring Post-Error Neuromuscular Responses During a Sport-Specific Inhibitory Control Task

Journal Description

Biosensors

Biosensors is an international, peer-reviewed, open access journal on the technology and science of biosensors, published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Embase, CAPlus / SciFinder, Inspec, and other databases.
Journal Rank: JCR - Q1 (Instruments and Instrumentation) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.3 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Analysis and Sensing Technologies: Analytica, Biosensors, Chemosensors, Purification, Separations and Spectroscopy Journal.

Impact Factor: 6.2 (2025); 5-Year Impact Factor: 6.2 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2079-6374

Latest Articles

22 pages, 10596 KB

Open AccessArticle

Ag/AgCl Nanoparticle Incorporation into Epipremnum aureum for Electrothermal Signal Amplification and Machine-Learning-Based Temperature Prediction

by Marco Merino-Treviño, Ana Beatriz Morales-Cepeda, Hernán Peraza-Vázquez and Edgar Onofre-Bustamante

Biosensors 2026, 16(8), 450; https://doi.org/10.3390/bios16080450 - 19 Aug 2026

Recently, plant-based bioelectronic systems have been explored for environmental sensing applications. However, their intrinsically low electrical conductivity often limits signal sensitivity and measurement reliability. In this work, the electrothermal behavior of living Epipremnum aureum plants incorporating Ag/AgCl nanoparticles supported on nanocellulose was investigated. [...] Read more.

Recently, plant-based bioelectronic systems have been explored for environmental sensing applications. However, their intrinsically low electrical conductivity often limits signal sensitivity and measurement reliability. In this work, the electrothermal behavior of living Epipremnum aureum plants incorporating Ag/AgCl nanoparticles supported on nanocellulose was investigated. Electrical and thermal responses were simultaneously measured under controlled environmental conditions using external shunt resistances of 1, 10, 100, and 1000 Ω. Compared with the control without nanoparticle incorporation, the nanoparticle-incorporated plant exhibited stronger electrical responses and distinct electrothermal behavior over the studied temperature range. The measured signals showed nonlinear responses, temporal asymmetry, and resistance-dependent modulation, suggesting changes in charge transport within the plant tissues. Silver-enriched regions and the co-detection of chlorine within the nanoparticle-incorporated plant tissues were identified by environmental scanning electron microscopy and energy-dispersive X-ray spectroscopy. Five machine-learning regression models were trained to estimate temperature using the measured electrothermal voltage signals as predictors. The best-performing model, MLP FitRNet, achieved a mean absolute error of 0.598 °C, a root mean square error of 0.748 °C, and an

R^{2}

value of 0.974. These results demonstrate the potential of nanoparticle-incorporated biohybrid plant systems for electrothermal signal analysis and data-driven temperature estimation, while providing a foundation for future intelligent environmental monitoring applications. Full article

(This article belongs to the Special Issue Electrochemical (Bio)Sensors as Promising Analytical Tools in the Analysis of Soils, Plants and Environmental Monitoring (2nd Edition))

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30 pages, 2958 KB

Open AccessReview

Plasmonic Nanoarray Biosensors for Non-Invasive Cancer Diagnostics

by Se Eun Kim, Hye Kyu Choi and Jin-Ha Choi

Biosensors 2026, 16(8), 449; https://doi.org/10.3390/bios16080449 - 18 Aug 2026

Early cancer detection can expand treatment options and improve patient survival, but it requires tests that can be repeated with minimal patient burden. Urine and saliva can be collected non-invasively and may contain cancer-associated nucleic acids, proteins, and extracellular vesicles. Clinical analysis of [...] Read more.

Early cancer detection can expand treatment options and improve patient survival, but it requires tests that can be repeated with minimal patient burden. Urine and saliva can be collected non-invasively and may contain cancer-associated nucleic acids, proteins, and extracellular vesicles. Clinical analysis of these body fluids is complicated by low biomarker abundance, inter-individual variation, and matrix components that interfere with surface-based sensing. Plasmonic nanoarray biosensors address some of these analytical constraints by concentrating local electromagnetic fields, supporting multiplexed optical readout, and accommodating surface chemistry and microfluidic handling. This review examines nanoarray architectures, fabrication methods, surface functionalization, and signal generation for cancer-associated biomarkers in urine and saliva. Localized surface plasmon resonance, surface-enhanced Raman scattering, and metal-enhanced fluorescence are discussed together with applications to bladder, prostate, pancreatic, oral, and head-and-neck cancers. Remaining barriers include biofouling, pre-analytical variation, fabrication reproducibility, limited validation using authentic biofluids, and incomplete sample-to-answer integration. Addressing these challenges through standardized biofluid processing, scalable nanoarray fabrication, and integrated microfluidic platforms will be essential for translating plasmonic nanoarray biosensors into clinically applicable cancer screening tools. Full article

(This article belongs to the Special Issue Functional Materials for Biosensing Applications (2nd Edition))

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62 pages, 3911 KB

Open AccessReview

AI-Ready Multimodal Wearable Biosensors Beyond Glucose: Biofluid Sampling, Sensor Fusion, and Clinical Translation

by Ahmet Akif Kızılkurtlu and Ali Akpek

Biosensors 2026, 16(8), 448; https://doi.org/10.3390/bios16080448 - 18 Aug 2026

Continuous glucose monitoring has established that a molecular signal can be repeatedly measured in daily life and translated into clinically meaningful action. The next frontier is broader: wearable biosensors that monitor metabolites, electrolytes, hormones, drugs, nutrients, inflammatory markers, and tissue-state indicators beyond glucose. [...] Read more.

Continuous glucose monitoring has established that a molecular signal can be repeatedly measured in daily life and translated into clinically meaningful action. The next frontier is broader: wearable biosensors that monitor metabolites, electrolytes, hormones, drugs, nutrients, inflammatory markers, and tissue-state indicators beyond glucose. This review proposes an artificial intelligence (AI)-ready framework for multimodal wearable biochemical monitoring. We organize the field as a complete measurement chain that links biofluid access, sampling chronology, flexible biointerfaces, molecular recognition, signal conditioning, metadata capture, sensor fusion, clinical validation, and lifecycle governance. The central argument is that the clinically useful variable is rarely a raw current, potential, optical intensity, or spectrum; it is a quality-controlled, context-aware, and uncertainty-aware digital biomarker. We compare sweat, interstitial fluid, saliva, tears, wound exudate, and breath condensate; evaluate enzymatic, ion-selective, affinity, transistor, optical, and spectroscopic sensing strategies; synthesize representative high-impact studies; and define minimum metadata, validation metrics, and translation gates. The review highlights recurring gaps in biofluid validity, real-world robustness, reference-comparator alignment, subgroup evidence, and algorithmic governance. We conclude with practical design rules for converting flexible biochemical wearables from attractive prototypes into clinically credible intelligent biosensing systems. Full article

(This article belongs to the Special Issue Advances in Flexible Bioelectronics and Intelligent Biosensing Systems—2nd Edition)

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11 pages, 5849 KB

Open AccessArticle

Bystander Effect Induced by Electrical Stimulation Promotes N2a Differentiation Through Interleukin-6

by Daniel Martín, Luis Orta, Akaitz Dorronsoro, Diego Ruano, Alberto Yúfera and Paula Daza

Biosensors 2026, 16(8), 447; https://doi.org/10.3390/bios16080447 - 18 Aug 2026

The bystander effect describes the induction of responses in non-targeted cells through cell signaling by directly stimulated cells. While this phenomenon has been extensively studied in the context of ionizing radiation, its occurrence following electrical stimulation (ES) remains poorly understood. Conditioned medium from [...] Read more.

The bystander effect describes the induction of responses in non-targeted cells through cell signaling by directly stimulated cells. While this phenomenon has been extensively studied in the context of ionizing radiation, its occurrence following electrical stimulation (ES) remains poorly understood. Conditioned medium from N2a neuroblastoma cells exposed to voltage-controlled biphasic pulses at 500 mV/mm and 100 Hz induced neuronal differentiation in non-stimulated cells through an ES bystander effect. Bystander medium promoted morphological changes associated with neuronal differentiation, including increased neurite outgrowth and a reduction in the proliferation marker KI-67, indicating that the effects of ES extend to neighboring non-targeted cells. Molecular analysis revealed increased expression and secretion of interleukin-6 (IL-6) following ES, while neutralization of the IL-6 receptor inhibited the effects of ES, highlighting the role of IL-6 as a key mediator of this effect. We provide the first evidence that ES promotes a differentiation-associated bystander effect mediated by IL-6. Full article

(This article belongs to the Section Biosensor and Bioelectronic Devices)

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20 pages, 4529 KB

Open AccessArticle

Functional Activity of TDP-43: A Direct Biomarker for ALS

by Kirti Shila Sonkar, Vito Levi D’Ancona, Jade Cramp, Hannah Shilling, Ellie Giles, Tyler Howell-Bray, Becky Fillingham, Merit E. Cudkowicz, Avindra Nath, Jeffrey D. Rothstein, Robert Bowser, Barbara Borroni, James D. Berry, Ghazaleh Sadri-Vakili, Emanuele Buratti and Ian P. Thrippleton

Biosensors 2026, 16(8), 446; https://doi.org/10.3390/bios16080446 - 17 Aug 2026

TDP-43 dysfunction is a defining feature of amyotrophic lateral sclerosis (ALS), yet no biofluid biomarker directly measures its functional activity. We developed a serum-based homogeneous time-resolved FRET (hTR-FRET) assay that quantifies TDP-43 RNA binding activity using synthetic UU-rich RNA probes. We analyzed 1080 [...] Read more.

TDP-43 dysfunction is a defining feature of amyotrophic lateral sclerosis (ALS), yet no biofluid biomarker directly measures its functional activity. We developed a serum-based homogeneous time-resolved FRET (hTR-FRET) assay that quantifies TDP-43 RNA binding activity using synthetic UU-rich RNA probes. We analyzed 1080 serum samples from controls, sporadic ALS, and genetic subgroups (C9orf72, SOD1) across multiple biorepositories. Cross-sectionally, TDP-43 functional activity was elevated in ALS (mean 390 a.u.) versus controls (302 a.u.), yielding AUC = 0.79. Genotype means were 392 a.u. (sporadic), 382 a.u. (C9orf72), and 323 a.u. (SOD1); a 366 a.u. threshold achieved 95% specificity against controls. Longitudinally, Target ALS showed a modest but significant inverse correlation between TDP-43 activity and ALSFRS-R, while other cohorts exhibited similar non-significant trends. Elevated signal in serum likely reflects increased extracellular release of probe-competent TDP-43 species during cell death and exosomal shedding, rather than restored intracellular nuclear splicing function. This assay provides a proof-of-concept platform for the direct functional measurement of probe-competent TDP-43 species in serum. While it demonstrates moderate group-level discrimination, individual diagnostic performance requires prospective validation. The assay may support exploratory applications in genotype stratification and progression monitoring in future clinical studies. Full article

(This article belongs to the Special Issue Biosensors for Disease Analysis)

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20 pages, 3534 KB

Open AccessArticle

Deep Learning-Assisted Accuracy Improvement in Bladder Cancer Staging of Spectrum-Aided Visual Enhanced Cystoscopy Images

by Kuan-Hsun Huang, Yu-You Liu, Chia-Chien Wu, Chia-Ling Chen, Jie-Lun Hsieh, Lung-Hsiang Chuo and Hsiang-Chen Wang

Biosensors 2026, 16(8), 445; https://doi.org/10.3390/bios16080445 - 16 Aug 2026

Recent statistics reported by the World Health Organization and the International Agency for Research on Cancer indicate that the global incidence of bladder cancer has continued to increase in recent years, particularly in industrialized countries. Therefore, the timely diagnosis of early-stage bladder cancer [...] Read more.

Recent statistics reported by the World Health Organization and the International Agency for Research on Cancer indicate that the global incidence of bladder cancer has continued to increase in recent years, particularly in industrialized countries. Therefore, the timely diagnosis of early-stage bladder cancer is of great clinical importance for improving patient prognosis and treatment outcomes. In this context, computational optical sensing frameworks that integrate Spectrum-Aided Visual Enhancer (SAVE) technology with cystoscopy have attracted significant attention to overcome the limitations of conventional visual data interpretation. In this study, an AI-driven optical biosensing framework was evaluated using 1372 white-light cystoscopy (WLC) images of bladder cancer (RGB-WLC) collected in collaboration with Chung Shan Medical University Hospital. Hyperspectral conversion technology was applied to extract precise spectral information from the white-light images. Subsequently, dimensionality reduction was performed based on the characteristic wavelengths of narrow-band imaging cystoscopy at 415 nm and 540 nm to generate hyperspectral reconstructed narrow-band images. The images were categorized into Ta stage (Ta), above T1 stage (Above T1), and four additional classes. The dataset was divided into training and testing sets to establish both a standard white-light cystoscopy model (RGB-WLC) and an advanced hyperspectral biosensing model utilizing the YOLOv8 architecture for enhanced pattern recognition. Model performance was evaluated using sensitivity, F1-score, and overall accuracy. The standard RGB-WLC model achieved an accuracy of 0.852, whereas the SAVE-based biosensing model achieved an accuracy of 0.948, representing an improvement of approximately 11.27%. The results demonstrate that combining algorithmic hyperspectral reconstruction with deep learning architectures effectively addresses the challenges of clinical data interpretation and significantly enhances the detection and staging performance of bladder cancer imaging. Full article

(This article belongs to the Special Issue AI-Enabled Biosensor Technologies for Boosting Medical Applications)

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29 pages, 3870 KB

Open AccessReview

From Device-Level Implementation to In-Sensor Computing in Memristive-Device-Based Biosensors: A Review

by Hyunwook Ryu, Won-Chul Lee and Jongwon Lee

Biosensors 2026, 16(8), 444; https://doi.org/10.3390/bios16080444 - 16 Aug 2026

Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors [...] Read more.

Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors from device-level transduction to system-level integration. At the device level, sensing strategies have evolved from direct sensing toward indirect sensing architectures, improving stability and reusability. At the system level, conventional off-chip implementations have progressively shifted toward fully integrated on-chip implementations. Furthermore, this review highlights the emerging paradigm of in-sensor computing, in which sensing, memory, and computation are co-located within a single physical platform. This approach enables reduced data movement and supports energy-efficient operation for point-of-care applications. Finally, key challenges—including CMOS compatibility, device variability, and reliable multi-threshold sensing operation—are discussed as critical factors for the practical realization of memristive-device-based electrochemical biosensing systems. Full article

(This article belongs to the Special Issue AI-Based Biosensors and Biomedical Imaging)

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17 pages, 10623 KB

Open AccessArticle

Reversal Nanoimprinted 3D Plasmonic Sensor Around Microposts for Cell and DNA Detection

by Yijun Cheng and Stella W. Pang

Biosensors 2026, 16(8), 443; https://doi.org/10.3390/bios16080443 - 16 Aug 2026

Localized surface plasmon resonance biosensors are promising devices for label-free detection of live cells and biomolecules. However, typical plasmonic sensors have limited surface area, planar electromagnetic fields, and poor compatibility with three-dimensional (3D) interactions with cells or biomolecules. In this study, a 3D [...] Read more.

Localized surface plasmon resonance biosensors are promising devices for label-free detection of live cells and biomolecules. However, typical plasmonic sensors have limited surface area, planar electromagnetic fields, and poor compatibility with three-dimensional (3D) interactions with cells or biomolecules. In this study, a 3D plasmonic sensor around microposts was developed using reversal nanoimprint lithography for highly sensitive cell and DNA detection. Au nanopillars were conformally integrated onto the bottom, sidewall, and top of microposts, forming additional sensing surface area along the sidewall of microposts for plasmonic sensing. The 3D plasmonic sensors exhibited tunable resonance peaks and refractive index (RI) sensitivities by varying the micropost height. The highest sensitivity of 1306 nm per RI unit was obtained from the sensor with 10 μm-tall microposts at a resonance wavelength of 1315 nm, which was significantly higher than that of typical planar plasmonic sensors. The platform was applied to live MC3T3-E1 cell detection, showing a resonance peak shift of 71 ± 11.6 nm at a cell concentration of 10⁶ cells/mL with a cell concentration ranging from 10² to 10⁶ cells/mL. In addition, DNA hybridization detection was demonstrated over a concentration range of 10⁻¹⁵–10⁻⁷ M complementary target DNA, with a resonance shift of 68 ± 2.5 nm observed at 10⁻⁷ M target DNA concentration. The 3D plasmonic sensor provides a scalable device for additional plasmonic biointerfaces with enhanced analyte accessibility and light–matter interactions. This platform offers high-sensitivity biosensing involving live cells, nucleic acids, and other biological targets. Full article

(This article belongs to the Special Issue Fluorescent Materials with Excellent Biocompatibility and Their Application in Bio-Sensing, Bio-Imaging (3rd Edition))

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34 pages, 6523 KB

Open AccessArticle

A Blockchain-Enabled Federated Neuro-Symbolic Framework for Secure Wearable Biosensor-Based Health Monitoring

by Khulud Salem Alshudukhi and Noshina Tariq

Biosensors 2026, 16(8), 442; https://doi.org/10.3390/bios16080442 - 16 Aug 2026

Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model [...] Read more.

Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge–Fog–Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96–99.47% for accuracy and 99.08–99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments. Full article

(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)

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15 pages, 934 KB

Open AccessArticle

A Ready-to-Use Recombinant Yeast Two-Hybrid Assay for Thyroxine Detection

by Marius Danhausen, Sebastian Buchinger, Shimshon Belkin and Thomas Andreas Ternes

Biosensors 2026, 16(8), 441; https://doi.org/10.3390/bios16080441 - 15 Aug 2026

We report a freeze-dried ready-to-use yeast thyroid screen (YTS), preserving the general dose–response characteristics of the freshly prepared counterpart. This field-deployable method reduces the assay time of the overall procedure from several days to 5 h with no requirement for sterile conditions, thus [...] Read more.

We report a freeze-dried ready-to-use yeast thyroid screen (YTS), preserving the general dose–response characteristics of the freshly prepared counterpart. This field-deployable method reduces the assay time of the overall procedure from several days to 5 h with no requirement for sterile conditions, thus fulfilling key requirements for on-site implementation in a biosensor array. The effects of cell density and concentration of the cryoprotectant trehalose on median effective concentrations (EC₅₀), limit of detection (LOD) and biosensor induction (IF) were determined and monitored over a storage period of 5 months. In addition, the impact of these parameters was monitored on the biosensor survival rate during freeze-drying and the subsequent storage process. Throughout the 5-month study, the freeze-dried recombinant yeast assay retained comparable dose–response characteristics to those of the freshly prepared counterpart, displaying median values of EC₅₀ in the range of 350 nM to 550 nM and LODs in the range of 20 nM to 45 nM of the reference compound thyroxine (T4). Long-term stabilization is demonstrated using spiked (T4, 2 µM) river water and extracted wastewater effluent. After 5 months of storage, the T4-equivalent activities were 96 ± 38% and 112 ± 15% for river water and wastewater, respectively. In summary, we have successfully demonstrated a proof of principle of a field-deployable yeast thyroid screen (YTS) by using freeze-dried cells and trehalose as a cryoprotectant to achieve storability for up to 5 months at 4 °C. Full article

(This article belongs to the Section Environmental, Agricultural, and Food Biosensors)

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13 pages, 2237 KB

Open AccessArticle

PCDA–EDA Colorimetric Nanofiber Sensor for Rapid Visual GHB Screening: Linker Reassignment and Scalable Fabrication

by Seunghye Yang, Jeongwook Lee, Om Darlami and Dongyun Shin

Biosensors 2026, 16(8), 440; https://doi.org/10.3390/bios16080440 - 14 Aug 2026

γ-Hydroxybutyric acid (GHB), a colorless and odorless central nervous system depressant associated with drug-facilitated sexual assault, demands rapid on-site detection. Polydiacetylene (PDA) colorimetric sensors derived from 10,12-pentacosadiynoic acid (PCDA) conjugates are a promising platform, but the molecular origin of GHB recognition in PCDA–gabazine [...] Read more.

γ-Hydroxybutyric acid (GHB), a colorless and odorless central nervous system depressant associated with drug-facilitated sexual assault, demands rapid on-site detection. Polydiacetylene (PDA) colorimetric sensors derived from 10,12-pentacosadiynoic acid (PCDA) conjugates are a promising platform, but the molecular origin of GHB recognition in PCDA–gabazine systems has remained unresolved. Here, we compare a series of structurally related PCDA conjugates to examine how the chemical state of the EDA-derived unit affects the GHB-induced colorimetric response. A side-by-side substituent screen of three PCDA derivatives showed that PCDA–EDA produced the largest colorimetric response (ΔR = 53) within 30 s, the hydrazide analogue gave a moderate response (ΔR = 34), and a simple amide was negligible (ΔR = 12). By contrast, a PCDA–gabazine mat prepared by the same protocol showed only a subtle, barely discernible color shift after several hours and remained predominantly blue even after approximately 24 h, without a visually appreciable blue-to-red transition. This difference suggests that the accessible free primary amine of PCDA–EDA is an important factor contributing to its faster and stronger response. Building on this mechanistic finding, we replaced the previously used PVDF–HFP/PEO matrix with a cellulose/PVDF–HFP formulation processed from DMF and adopted multi-nozzle electrospinning, reducing the fabrication time from approximately 120 to 30 min per sheet, corresponding to a 75% reduction in processing time. The sensor mat showed a clearly distinguishable, dose-dependent visual response across 0.5–3% w/v GHB within 30 s, covering the forensically relevant concentration window. These findings reposition linker architecture as a central design parameter for PDA-based forensic colorimetric sensors. Full article

(This article belongs to the Section Biosensor Materials)

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21 pages, 10909 KB

Open AccessArticle

Ultra-Broadband Metasurface Absorber Enabled by a Central-Bar-Coupled Split-Disk Dimer

by Carlotta Panciera, Giuseppe Brunetti, Caterina Ciminelli and Muhammad A. Butt

Biosensors 2026, 16(8), 439; https://doi.org/10.3390/bios16080439 - 14 Aug 2026

A hybrid metasurface absorber (MSA) based on a central-bar-coupled split-disk dimer is proposed and numerically investigated for high-resolution refractive-index sensing in the near-infrared spectral region. The metasurface consists of silicon nitride dielectric resonators integrated with a gold plasmonic layer, enabling strong electromagnetic confinement, [...] Read more.

A hybrid metasurface absorber (MSA) based on a central-bar-coupled split-disk dimer is proposed and numerically investigated for high-resolution refractive-index sensing in the near-infrared spectral region. The metasurface consists of silicon nitride dielectric resonators integrated with a gold plasmonic layer, enabling strong electromagnetic confinement, enhanced light–matter interaction, and ultra-narrow resonant features within the 1000–1400 nm wavelength range. The optimized structure supports multiple resonant modes under both x- and y-polarized excitation, producing sharp reflection dips with full-width-at-half-maximum values as low as 0.58 nm and quality factors reaching 2007. Refractive-index sensing performance was evaluated by varying the aqueous superstrate refractive index from 1.33 to 1.35, resulting in bulk sensitivities up to 860 nm/RIU under normal incidence. The angular response was further analyzed for incidence angles up to 5°, revealing polarization-dependent resonance splitting and the emergence of additional high-Q resonant branches under oblique excitation. Several angularly induced resonances exhibit narrower linewidths than those observed at normal incidence while preserving high refractive-index sensitivity up to 870 nm/RIU. Electric-field distributions confirm strong field localization near the dielectric boundaries and coupling regions, validating the hybrid resonant mechanism responsible for the enhanced spectral selectivity and sensing performance. The proposed MSA provides a promising platform for compact and ultrasensitive biosensing applications. Full article

(This article belongs to the Section Optical and Photonic Biosensors)

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23 pages, 6448 KB

Open AccessReview

Chemiresistive Gas Sensors for the Detection of Listeria monocytogenes Metabolite: Recent Progress and Challenges

by Bingxi Feng and Jing Wei

Biosensors 2026, 16(8), 438; https://doi.org/10.3390/bios16080438 - 13 Aug 2026

Listeria monocytogenes (LM), one of the most virulent foodborne pathogens, poses a serious threat to public health due to its strong environmental adaptability and high pathogenicity. Rapid, sensitive, and real-time detection of LM is of great importance. Chemiresistive gas sensors have attracted enormous [...] Read more.

Listeria monocytogenes (LM), one of the most virulent foodborne pathogens, poses a serious threat to public health due to its strong environmental adaptability and high pathogenicity. Rapid, sensitive, and real-time detection of LM is of great importance. Chemiresistive gas sensors have attracted enormous attention in LM detection owing to their advantages of low cost, simple structure, fast response, and easy miniaturization, which can achieve indirect detection of LM by recognizing its specific metabolic volatile organic compounds. This review summarizes the recent progress in chemiresistive gas sensors for the detection of LM metabolites. First, the metabolic characteristics of LM and the typical volatile organic compound (3-hydroxy-2-butanone) as its characteristic biomarker are introduced. Then, the performance and sensing mechanisms of different types of chemiresistive gas sensors for LM metabolite detection are summarized and elaborated systematically. The application of chemiresistive gas sensors for the detection of actual samples and the progress in the design of related detection devices are introduced. Finally, the current challenges faced by chemiresistive gas sensors in LM metabolite detection and their future development prospects are discussed. This review provides a comprehensive reference for the research and practical application of chemiresistive gas sensors in Listeria monocytogenes detection. Full article

(This article belongs to the Special Issue Biosensors for Environmental Monitoring and Food Safety—2nd Edition)

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21 pages, 4250 KB

Open AccessArticle

Deep Learning for Ear-EEG-Based Brain–Computer Interface: A Systematic Comparison and Design Insights

by Ji-Seung Kim, Soo-In Choi, Han-Jeong Hwang and Chang-Hee Han

Biosensors 2026, 16(8), 437; https://doi.org/10.3390/bios16080437 - 12 Aug 2026

Electroencephalography (EEG) measured inside or around ears, called ear-EEG, provides a practical measurement modality for daily brain–computer interface (BCI) applications. However, reliable decoding of mental imagery remains challenging due to the limited number of channels, low signal-to-noise ratio (SNR), and substantial inter- and [...] Read more.

Electroencephalography (EEG) measured inside or around ears, called ear-EEG, provides a practical measurement modality for daily brain–computer interface (BCI) applications. However, reliable decoding of mental imagery remains challenging due to the limited number of channels, low signal-to-noise ratio (SNR), and substantial inter- and intra-subject variability inherent to ear-EEG. Addressing these constraints requires advanced decoding strategies specifically optimized for this signal domain. In this study, we retrospectively analyze the ear-EEG dataset of a previous study in which a real-time endogenous BCI was evaluated using conventional machine learning. Specifically, we present an offline benchmark of 23 deep neural network architectures originally developed for scalp-EEG, which were adapted to ear-EEG and evaluated under an identical validation framework. To the best of our knowledge, this is the first systematic comparison of this breadth for ear-EEG-based mental-task classification. Beyond conventional performance comparison, we identify the optimal architecture by jointly considering statistical significance and a performance–cost trade-off, incorporating classification accuracy, parameter count, and measured computational cost. Our results demonstrate that FBLightConvNet achieves the highest classification accuracy among all evaluated models and outperforms common spatial pattern-linear discriminant analysis (CSP-LDA), a widely adopted and robust conventional baseline, on all three recording days, with the difference reaching statistical significance on Days 2 and 3. Notably, many state-of-the-art scalp-EEG models fail to generalize effectively to ear-EEG, highlighting the importance of architecture selection in this domain. These findings identify the best-performing architecture in this setting and indicate which architectural characteristics support effective ear-EEG decoding. Ultimately, this study offers practical design insights and a reproducible benchmarking framework for developing lightweight and high-performance deep learning models, which we hope will support future efforts toward real-world ear-EEG-based BCI systems. Full article

(This article belongs to the Special Issue Wearable Sensors and Biosensors for Physiological Signals Measurement)

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17 pages, 3799 KB

Open AccessFeature PaperArticle

Novel Optical Sensor-Based Organic Chromophore for the Detection of Copper Ions

by Reham Ali, Majd K. Almotiri, Sabri Messaoudi, Ibrahim A. I. Ali, Azizah Algreiby and Sayed M. Saleh

Biosensors 2026, 16(8), 436; https://doi.org/10.3390/bios16080436 - 12 Aug 2026

During this research, a distinctive optical sensor film was devised and designed to detect Cu(II). The 3-acetyl-4-hydroxyquinolin-2(1H)-one (AHQ) sensor probe is effectively synthesized. This organic probe of the sensor film exhibits exceptional sensitivity to Cu(II) ions and a “turn-off” state. This [...] Read more.

During this research, a distinctive optical sensor film was devised and designed to detect Cu(II). The 3-acetyl-4-hydroxyquinolin-2(1H)-one (AHQ) sensor probe is effectively synthesized. This organic probe of the sensor film exhibits exceptional sensitivity to Cu(II) ions and a “turn-off” state. This innovative fluorescent chemosensor is distinguished by its unique optical characteristics, which include a significant Stokes shift of approximately 91 nm. The binding of Cu(II) with AHQ organic probe introduces a 1:2 (metal:ligand) complex, accompanied by the quenching of the maximum emission peak at 455. Furthermore, AHQ exhibits exceptional selectivity for Cu(II). The quenching of the complex fluorescence is attributed to internal charge transfer (ICT), as indicated by the mechanism. The AHQ sensing molecule for Cu(II) ions is attributed to chelation-quenched fluorescence. Density functional theory (DFT) and time-dependent DFT (TDDFT) were employed to study the binding of Cu(II)–AHQ structures and related electronic characteristics in solutions. The results reveal that the luminescence quenching of this complex is caused by ICT. The influences of the interference ions were investigated using a solution that contained multiple metal ions. This AHQ molecule exhibits exceptional selectivity and sensitivity and a low LOD of 10.8 nM, and is administered in a physiological pH medium (pH = 7.4) with a relative standard deviation (RSD_r) (1%, n = 3). Also, the AHQ shows good binding behaviour towards Cu(II), and the binding constant was determined to be 3.8 × 10⁶ M⁻¹. As a result, these unique characteristics allow it to identify Cu(II) within a controlled dynamic range of 0.019–2.4 μM Cu(II). The reversibility of the chemosensor was established by using EDTA as a strong chelating agent. As a highlight, we present an important optical chemosensor dependent on the AHQ molecule. Full article

(This article belongs to the Section Optical and Photonic Biosensors)

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28 pages, 8745 KB

Open AccessReview

Recent Progress in Nanoparticle-Based Biosensors for Monitoring Shigella spp. in Food Safety: A Critical Review

by Sumeyra Savas and Seyed Mohammad Taghi Gharibzahedi

Biosensors 2026, 16(8), 435; https://doi.org/10.3390/bios16080435 - 11 Aug 2026

Shigella is a foodborne bacterial pathogen with a low infectious dose and significant public health impact. Culture-based and molecular techniques provide reliable identification but are time-consuming. Nanoparticle-based biosensors offer sensitive, selective, and compact alternatives. Recent advances in nanoparticle-based biosensors for Shigella spp. ( [...] Read more.

Shigella is a foodborne bacterial pathogen with a low infectious dose and significant public health impact. Culture-based and molecular techniques provide reliable identification but are time-consuming. Nanoparticle-based biosensors offer sensitive, selective, and compact alternatives. Recent advances in nanoparticle-based biosensors for Shigella spp. (S. flexneri, S. sonnei, S. dysenteriae, and S. boydii) detection have been reviewed in terms of signal amplification, biorecognition, biological targets, sensor types, and performance in real food matrices. Detection strategies rely on gene-level and whole-cell recognition. Targeting virulence genes, invasion plasmid antigen H (ipaH), provides stable genus-level identification, whereas whole-cell recognition facilitates rapid detection without extensive sample preparation. Optical biosensors, including fluorescence-based methods, surface-enhanced Raman spectroscopy (SERS), and localized surface plasmon resonance (LSPR), achieve low detection limits with strong tolerance to complex food matrices. Electrochemical biosensors offer operational simplicity, portability, and suitability for food screening. Lateral flow and hybrid systems provide rapid detection through simplified assay formats and visual readout, with performance influenced by the balance between speed and sensitivity. Validation in real food matrices shows acceptable recoveries, minimal cross-reactivity, and agreement with reference methods. This overview provides a design-oriented framework for nanoparticle-based biosensor selection in food safety by integrating nanomaterial function, biosensor design, and performance characteristics. Full article

(This article belongs to the Special Issue Advanced Biosensors for Food and Agriculture Safety)

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23 pages, 4458 KB

Open AccessArticle

MSA-CNN: A Multi-Scale Attention Convolutional Neural Network for fNIRS-Based Emotion Recognition

by Deping Huang, Xiu Zhang, Ye Li, Jingfu Wu and Youzhi Yue

Biosensors 2026, 16(8), 434; https://doi.org/10.3390/bios16080434 - 9 Aug 2026

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in affective brain–computer interface research due to its non-invasive nature, portability, and robustness to motion artifacts. However, substantial inter-subject variability in neural responses remains a major challenge for subject-independent emotion recognition. To address this issue, [...] Read more.

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in affective brain–computer interface research due to its non-invasive nature, portability, and robustness to motion artifacts. However, substantial inter-subject variability in neural responses remains a major challenge for subject-independent emotion recognition. To address this issue, this work presents an effective integration of multi-scale temporal convolution and dual-attention mechanisms for subject-independent fNIRS emotion recognition evaluated under the leave-one-subject-out protocol within a single dataset. The proposed framework employs multi-scale temporal convolutions to capture hemodynamic characteristics at different temporal resolutions and incorporates channel and temporal attention mechanisms to adaptively emphasize informative brain regions and critical temporal segments. Experiments were conducted on both a self-collected fNIRS emotion dataset and the publicly available ENTER dataset using the Leave-One-Subject-Out (LOSO) evaluation protocol. On the self-collected dataset, MSA-CNN achieved an accuracy of 65.06 ± 7.10% with an F1-score of 0.605. On the ENTER dataset, the proposed model obtained an accuracy of 68.91% and an F1-score of 0.621, outperforming conventional machine learning approaches and several representative deep learning baselines. Ablation studies further demonstrated the positive contributions of both the multi-scale convolutional structure and the dual-attention mechanism. Experimental results on both the self-collected and ENTER datasets demonstrate that the proposed MSA-CNN achieves competitive emotion recognition performance under the LOSO protocol. Class-wise evaluation using precision, recall, and the F1-score further provides a comprehensive assessment of the model’s classification behavior. These results indicate the effectiveness of the proposed framework for cross-subject fNIRS-based emotion recognition under the current experimental settings. The results indicate that multi-scale temporal feature learning combined with attention mechanisms can effectively enhance fNIRS-based emotion recognition performance and provides a promising framework for within-dataset cross-subject evaluation in fNIRS-based emotion recognition. Future work will focus on expanding the subject population, conducting cross-dataset train–test evaluations, and incorporating multimodal neural signals to further improve robustness and generalization. Full article

(This article belongs to the Special Issue Applications of AI in Non-Invasive Biosensing Technologies)

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15 pages, 4655 KB

Open AccessCommunication

Development of a Visual Rapid Assay for Novel Goose Astrovirus Detection Based on RT-MIRA -PfAgo

by Dongdong Yin, Xinjun Chen, Zhixing Cheng, Yu Liu, Yin Dai, Xuehuai Shen and Xiaocheng Pan

Biosensors 2026, 16(8), 433; https://doi.org/10.3390/bios16080433 - 8 Aug 2026

Goose astrovirus genotype 2 (GAstV-2) is an important pathogen associated with gosling gout, and rapid detection is useful for early diagnosis and field surveillance. In this study, a visual assay for GAstV-2 detection was developed by combining one-step reverse transcription multienzyme isothermal rapid [...] Read more.

Goose astrovirus genotype 2 (GAstV-2) is an important pathogen associated with gosling gout, and rapid detection is useful for early diagnosis and field surveillance. In this study, a visual assay for GAstV-2 detection was developed by combining one-step reverse transcription multienzyme isothermal rapid amplification (MIRA) with the nucleic acid cleavage activity of Pyrococcus furiosus Argonaute (PfAgo). MIRA primers and specific guide DNAs were designed based on a conserved region of the GAstV-2 ORF1b gene, and the PfAgo reaction conditions were optimized. The optimal reaction contained 1.0 μM gDNA, 0.6 μM PfAgo, and 1.0 mM MnCl₂. Using recombinant pUC57-ORF1b plasmid DNA as the template, the lowest detectable plasmid concentration under the tested conditions was 1.0 × 10⁰ copies/μL. In the specificity assay, only GAstV-2 produced a positive signal, with no cross-reaction observed with GAstV-1, Tembusu virus, H9-subtype avian influenza virus, goose circovirus, fowl adenovirus serotype 4, or goose parvovirus. The assay was further tested with 23 clinical samples suspected of GAstV-2 infection. In a preliminary evaluation of 23 clinical samples, the RT-MIRA-PfAgo results were concordant with those obtained by conventional RT-PCR and RT-qPCR. Overall, the RT-MIRA-PfAgo assay provided sensitive and specific GAstV detection within a short time, without requiring programmed thermal cycling or an expensive real-time PCR instrument for routine endpoint detection. This method may be useful for GAstV-2 detection in basic laboratories and field settings. Full article

(This article belongs to the Special Issue Biosensor Applications in Agriculture, Aquaculture and Animal Husbandry)

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20 pages, 4441 KB

Open AccessArticle

Asymmetric Sensitivity of Hepatic Venous Pressure Gradient to Portal and Sinusoidal Resistance: An In Vitro Experimental Study

by Jiyang Zhang, Lingjun Liu, Xin Yu, Xiao Li, Zhongyou Li, Taoping Bai and Wentao Jiang

Biosensors 2026, 16(8), 432; https://doi.org/10.3390/bios16080432 - 8 Aug 2026

Hepatic venous pressure gradient (HVPG) is the clinical gold standard for assessing portal hypertension, but its dependence on different vascular resistance sources remains unclear. This study evaluated the respective effects of sinusoidal resistance (SR) and portal venous resistance (PVR) on HVPG using an [...] Read more.

Hepatic venous pressure gradient (HVPG) is the clinical gold standard for assessing portal hypertension, but its dependence on different vascular resistance sources remains unclear. This study evaluated the respective effects of sinusoidal resistance (SR) and portal venous resistance (PVR) on HVPG using an in vitro hemodynamic platform. A mock circulatory loop with dual hepatic blood supply was constructed and calibrated to near-physiological conditions. SR and PVR were independently adjusted to reproduce different portal hypertension states, and HVPG was measured by balloon wedging. A perturbation index (PI) was introduced to quantify the systemic effect of wedging, and Sobol global sensitivity analysis was used to compare resistance contributions. HVPG increased markedly with SR, ranging from 3.69 to 12.25 mmHg, but showed only limited changes with PVR, ranging from 1.03 to 6.56 mmHg. Sobol analysis confirmed the dominant contribution of SR over PVR (S₁: 0.856 vs. 0.034). Balloon wedging also induced measurable systemic perturbations, particularly under high-resistance conditions. Overall, HVPG is highly sensitive to SR but relatively insensitive to PVR, indicating a systematic underestimation risk in presinusoidal portal hypertension. Moreover, the hemodynamic perturbation induced by balloon wedging should not be neglected in severe portal hypertension, suggesting that this effect should be incorporated into virtual HVPG models to improve their predictive accuracy. Full article

(This article belongs to the Special Issue Biosensing Technologies in Medical Diagnosis—2nd Edition)

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33 pages, 2538 KB

Open AccessReview

Carbon Nanotube-Based Biosensors for Non-Invasive Biofluid Analysis

by Samriddha Dutta and Ashok Mulchandani

Biosensors 2026, 16(8), 431; https://doi.org/10.3390/bios16080431 - 7 Aug 2026

Carbon nanotube (CNT)-based biosensors have emerged as promising platforms for non-invasive biofluid analysis because of their high electrical conductivity, large surface area, tunable optical properties, and versatile surface chemistry, enabling miniaturized, flexible sensing devices. Sweat, saliva, tears, and urine are increasingly recognized as [...] Read more.

Carbon nanotube (CNT)-based biosensors have emerged as promising platforms for non-invasive biofluid analysis because of their high electrical conductivity, large surface area, tunable optical properties, and versatile surface chemistry, enabling miniaturized, flexible sensing devices. Sweat, saliva, tears, and urine are increasingly recognized as attractive alternatives to blood for point-of-care diagnostics because they enable repeated, non-invasive sampling while containing clinically relevant metabolites, electrolytes, proteins, hormones, nucleic acids, pathogens, and other biomarkers. However, the low abundance of many analytes, matrix complexity, biofouling, and biofluid-specific variability present significant analytical challenges. This review critically examines the different CNT-based sensor architectures, and their recent advances in non-invasive analysis of sweat, saliva, tears, and urine. It integrates sensor architecture, biofluid-specific analytical challenges, sample-validation level, and translational readiness within a single comparative framework. Representative applications are discussed for metabolic monitoring, renal health assessment, infectious disease testing, and other clinically relevant uses. Beyond clinical diagnostics, emerging non-clinical applications, including drug-of-abuse detection, forensic body-fluid identification, and occupational or environmental exposure assessment, are also highlighted. Finally, we discuss key barriers limiting real-world translation of CNT biosensors, including material reproducibility issues, biofouling, physiological interpretation of biofluid biomarkers, scalable manufacturing, and long-term operational stability, and outline future strategies to advance these platforms toward robust, reliable, and widely deployable biosensing technologies. Full article

(This article belongs to the Special Issue Fundamental Innovation and Device Engineering of Biosensors Driven by Advanced Functional Materials)

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