Search Results (3,705)

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Although optical fiber-based surface plasmon resonance (SPR) sensors have revolutionized real-time, label-free biosensing, conventional designs suffer from limited multi-analyte detection capabilities. This study utilizes the novel Pi (π)-configured dual SPR optical fiber sensor with two opposing side-polished surfaces, enabling plasmonic excitation for simultaneous multi-analyte detection. The proposed sensor leverages asymmetric metallic thin films such as Ag, Au, Cu, and hybrid configurations (metal + TiO₂) to generate two distinct resonance peaks, significantly enhancing detection versatility. Numerical simulations using the finite element method in COMSOL Multiphysics v6.3 demonstrate that the π-configuration achieves dual resonance dips at 982 nm and 1276 nm for Ag and Ag–TiO₂ films, 1040 nm and 1317 nm for Au and Au–TiO₂ films, and 977 nm and 1249 nm for Cu and Cu–TiO₂ films, respectively, for an analyte refractive index of 1.42. A peak spectral separation >125 nm was achieved for all the sensors for a refractive index range of 1.37–1.42, ensuring that the two dips are resolvable since the change in SPR wavelength is greater than or equal to the full width at half maximum, preserving dual-analyte capability and minimizing potential crosstalk. The results indicate that the π-configured dual SPR sensor utilizing silver and silver–TiO₂ sensing layers had the highest wavelength sensitivity of 12,600 nmRIU⁻¹ and 20,000 nmRIU⁻¹, respectively, slightly outperforming its gold and copper counterpart. The optimized metallic and hybrid nanostructured films ensure dual distinct peaks with high sensitivity, while maximizing refractive index resolution. This work presents the design of a π-configured SPR-based optical fiber sensor utilizing dielectric and multi-metallic thin films, thereby offering a breakthrough in multiplexed biosensing for applications in medical diagnostics, environmental monitoring, and chemical detection. Full article

Biosensing performance in graphene-derived field-effect transistors (BioFETs) is widely attributed to surface chemistry, yet the role of the underlying charge transport mechanism remains poorly understood. This work establishes a direct correlation between disorder-driven transport and biosensing transduction in vertical graphene (VG) and nanocrystalline graphite (NCG) FET devices. Temperature-dependent electrical characterization (15–500 K) reveals a hybrid transport regime: three-dimensional Mott variable-range hopping below 240 K, transitioning to thermally activated Arrhenius-type conduction above 240 K. The extracted VRH parameters characteristic temperature T₀, localization length ξ, and density of states N(E_F) quantify fundamentally distinct disorder landscapes: VG operates in a strongly localized, edge-dominated regime, while NCG forms a continuous percolative network with greater transport stability. Surface functionalization via PASE and amine-terminated ssDNA probes, followed by DNA hybridization across four nucleobase systems, demonstrates that the sequence-dependent electrical response is mechanistically interpretable within the VRH–transconductance framework. NCG transduces biomolecular binding through direct charge transfer and hopping pathway perturbation, whereas VG responds through interfacial electrostatic reorganization. These results introduce a unified VRH–transconductance–sensing framework, providing a rational physical basis for next-generation graphene BioFET design. Full article

In this work, we propose a hyperbolic metamaterials (HMMs)-based coreless fiber surface plasmon resonance (SPR) sensor. Leveraging the absence of a core in coreless fibers, the evanescent waves at the cladding–external solution interface couple more effectively into the solution, enabling surface plasmon resonance without any additional processing. To enhance sensitivity, we adopted a multimode–coreless–multimode (MCM) structure and grew layered hyperbolic metamaterials as the SPR-excitation-sensitive layer within the coreless region. Through finite element simulations, we optimized HMM parameters and fabricated high-performance HMM-SPR sensors. Test results demonstrate that the fabricated HMM-SPR sensor achieves an optimal refractive index sensitivity of 3703.33 nm/RIU, representing a 49.68% improvement over single-layer gold film SPR sensors. It successfully detects glucose solutions at varying concentrations with a sensitivity of 2671.25 nm/RIU. The high-sensitivity, structurally simple HMM-SPR sensor we proposed demonstrates broad application prospects in biosensing, environmental monitoring, food safety, and other fields. Full article

DIGOTA architectures have attracted growing interest as a means of addressing the problems that arose with the extreme miniaturization of the MOS transistor in analog design. Despite the increasing number of proposed architectures, the literature remains fragmented, with differences in design goals, structural choices, and evaluation criteria that make direct comparison difficult. This paper presents a comprehensive survey of DIGOTA architectures reported in the literature so far. This review study is organized according to key architectural characteristics, including biomedical applications, flexible electronics, and low-power amplifiers. Based on this analysis, the paper discusses major trends, common trade-offs, strengths, and limitations across current approaches. The survey also identifies open issues and promising directions for future research. By providing a structured overview of the field, this work serves as a useful reference for researchers seeking to understand, compare, and develop DIGOTA architectures. Full article

Bionic sensors are emerging as powerful analytical platforms driving the development of next-generation detection technologies, particularly for small molecule sensing in complex environmental and biological systems. However, accurate and selective detection of small molecules remains fundamentally challenging due to their low molecular weight, limited structural specificity, and strong interference from complex matrices. This review provides a comprehensive overview of recent advances in bionic sensor technologies, focusing on how the integration of synthetic biology, nanomaterials, and artificial intelligence (AI) addresses these limitations. Key biorecognition elements, including enzymes, antibodies, aptamers, and molecularly imprinted polymers, are examined for their suitability in small molecule sensing applications. Advances in nanomaterials such as graphene, carbon nanotubes, quantum dots, and MXenes are discussed in relation to signal transduction enhancement, sensitivity improvement, and device miniaturization. In parallel, the roles of AI and machine learning in signal denoising, adaptive calibration, and molecular fingerprinting for complex datasets are highlighted. Applications in wearable and implantable biosensors, environmental monitoring, and food safety are analyzed, emphasizing real-time detection of metabolites, pollutants, and toxins. Key challenges associated with AI-driven systems, including scalability, cost, data reliability, and ethical concerns, are also discussed. Emerging trends such as hybrid sensing platforms, self-powered biosensors, and secure data integration frameworks are presented as future directions. This review aims to provide a problem-driven perspective on how next-generation bionic sensors can overcome current limitations and enable robust small molecule detection in real-world applications. Full article

Achieving in situ and time-resolved monitoring of microbial metabolites without disrupting the microbial growth environment remains a key challenge in electrochemical biosensing. Herein, we propose a self-healing bilayer hydrogel-based solid-state electrochemical sensing platform for the in situ, time-resolved analysis of purine metabolites produced by Escherichia coli (E. coli). This platform integrates an upper Agar culture module and a lower borax-crosslinked poly(vinyl alcohol) (PVA) detection module, forming a contiguous structure that allows metabolites (e.g., guanine, xanthine, hypoxanthine) to migrate across the solid–solid interface for sensitive electrochemical detection. The detection layer exhibits excellent ionic conductivity; when coupled with its robust structural self-healing capacity, the platform achieved a detection limit of 0.05 µM for guanine. For E. coli detection, a linear response range of 1.1 × 10⁶ to 9.5 × 10⁶ CFU·mL⁻¹ (R² = 0.9974) was obtained, and relative standard deviations (RSDs) of less than 2.34% even after two weeks of storage. Leveraging this integrated design, the platform enables continuous, label-free tracking of bacterial metabolic dynamics throughout all growth phases. Notably, it detects metabolic transition points earlier than traditional plate counting methods and accurately evaluates antibiotic inhibition trends, with results consistent with colony-forming unit (CFU) analysis. This integrated culture–detection architecture thus provides a versatile strategy for functional microbial analysis and rapid antimicrobial susceptibility testing. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Hydrogel-based optical sensors have emerged as a versatile class of analytical materials that combine soft-matter processability, tunable network chemistry, and compatibility with luminescent, colorimetric, photonic, and hybrid transduction strategies. Progress in the field is driven not by a single sensing mechanism, but by the convergence of key advances in material functionalization, embedded selectivity, operation across diverse sample matrices, mechanical and analytical robustness, and usability beyond the laboratory. Current systems include framework-integrated, nanoparticle-doped, probe-functionalized, photonic-crystal, enzyme-immobilized, and device-coupled hydrogels, reflecting growing architectural diversity and application-oriented engineering. Selectivity has likewise advanced from basic interferent screening to recognition-specific, imprinted, and pattern-discriminative formats suited to complex environmental, food, biological, and wearable settings. Evidence of stability, reusability, and deformation tolerance further suggests that many platforms are moving beyond proof-of-concept demonstrations toward credible real-world operation. At the same time, translational priorities such as portability, smartphone readout, implantable and epidermal formats, and multifunctionality spanning antimicrobial action, adsorption, anti-counterfeiting, and device integration are becoming increasingly prominent. Together, these trends show that hydrogel-based optical sensing is maturing into a materially rich, application-responsive domain. The key challenge ahead is to unify materials design, selectivity control, durability, and deployability in standardized, reproducible, and clinically or environmentally credible sensing platforms. Full article

Facing global problems such as the energy crisis and climate change, in recent years, the bioelectrochemical system represented by plant microbial fuel cell (PMFC) has been widely studied. It is a frontier bioelectrochemical technology that combines plant photosynthesis, rhizosphere microbial metabolism, and electrochemical energy conversion. This paper focuses on the linkage application of PMFC and intelligent sensing technology in the paddy-field environment, systematically expounds the basic composition, working principle, and integration mode of this technology with paddy field ecology, and emphatically analyzes its realization path and application potential in self-powered external sensor deployment, rhizosphere biosensor, and multi-node sensor network integration. The analysis shows that PMFC has the unique advantage of in situ and continuous micro-power generation in flooded rice fields. Its output not only supports the intermittent operation of low-power sensors, but the output electrical signals can also reflect plant stress and environmental conditions, thereby possessing biosensing potential. However, the current system still faces key bottlenecks, such as low power density, easily disturbed electrical signals, and high cost of high-performance electrode materials, which restrict the actual deployment of rice fields. Through the collaborative optimization of electrode interface engineering, microbial community directional control, and low-power sensing fusion strategy, it is expected to promote the transformation of PMFC from principle verification to field intelligent monitoring application. Full article

Three-dimensional (3D) cell culture models more faithfully reproduce native tissue organization and function than conventional two-dimensional systems, yet many existing bioprinting methods depend on scaffolds, complex instrumentation, or limited control over cell positioning. This review examines magnetic bioinks as a versatile platform for contactless 3D cell manipulation and biofabrication. It first outlines the fundamentals of magnetophoresis and defines magnetic bioinks as combinations of magnetic agents, including magnetic nanoparticles or paramagnetic salts, with biological components such as cells, proteins, or fluids. The review then compares label-based strategies, in which cells are magnetized and guided by positive magnetophoresis, with label-free approaches that exploit magnetic susceptibility differences to position diamagnetic cells through negative magnetophoresis. Across these methods, magnetic bioinks have enabled single-cell sorting, spatial patterning, spheroid and co-culture assembly, multilayer tissue formation, and hydrogel-integrated printing. These capabilities support applications in disease modeling, drug screening, biosensing, regenerative medicine, and emerging biofabrication under microgravity conditions. The paper also highlights key limitations, including nanoparticle biocompatibility, paramagnetic salt toxicity, osmotic stress, and the need for better assay standardization and translational validation. Overall, magnetic bioinks represent a promising scaffold-free approach for rapidly producing physiologically relevant 3D biological constructs for research and clinical innovation. Full article

We report a cross-station replication of endogenous circadian rhythms in plant bioelectric voltage, recorded continuously for 42 days at three independent sensor stations within a public science exhibition (Phänomena, Dietikon, Switzerland; March–April 2026). Three primrose (Primula vulgaris) stations were equipped with custom Biolingo bioelectric sensors (ESP32 + AD8232) and recorded autonomously through approximately 21,000 visitor interactions. We extracted DC-invariant spectral features from 5–10 s voltage windows (n = 78,431 quality-filtered files) and fitted two-stage cosinor models with bootstrap 95% confidence intervals. All three stations show a robust 24 h rhythm in the 1–5 Hz band power (bp_1–5), with peak-to-trough amplitudes between 0.35× and 1.19× of mesor (R²_med 0.72–0.87). Acrophase varies across stations from 05:00 to 11:00 local time. Critically, the rhythm survives an overnight-only restriction (18:00–09:00, no visitors) at all three stations, ruling out visitor presence as the rhythm driver. The most visitor-intensive station (faces of museum visitors triggering an emotion-recognition installation) additionally shows a sharp daytime amplitude collapse coincident with the exhibition opening at 09:00, during the hours of sustained visitor presence. This temporal coincidence is consistent with—though not by itself proof of—the cardiovascular-mechanosensory coupling characterized at single-subject resolution in a companion study. We argue that bp_1–5—the spectral band most directly related to plant action-potential activity—carries an endogenous circadian signal in Primula vulgaris and that this station-level signal co-varies with sustained nearby human presence in a manner consistent with frequency-selective mechanosensory coupling, although the observational design cannot establish this mechanism. From a biomimetic perspective, this suggests that the plant’s evolved bioelectric sensing apparatus might be leveraged as a live ambient biosensor for nearby human activity, complementing the more common biomimetic approach of replicating plant sensing in synthetic devices. Full article

Real-time physiological state awareness is central to next-generation wearable computing, yet most existing electrophysiological signal acquisition platforms remain limited to single-modality sensing, high component cost, or bulky form factors that hinder everyday deployment. Here, we present a compact, low-cost wearable platform for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) acquisition. The system integrates an analog front-end, a microcontroller, and a Bluetooth wireless link on a compact single-board platform (5.6 × 3.8 cm, approximately 12.8 g with the selected lithium-polymer battery installed), with an estimated bill-of-materials cost of 67.40 USD. Experimental validation across three healthy subjects, with the ECG channel additionally benchmarked against a commercial clinical-grade ambulatory ECG recorder, demonstrates that the platform captures ECG waveforms with recognizable P-QRS-T morphology under controlled recording conditions, supports reliable R-peak detection and heart rate estimation, records stable resting-state EEG spectral features, and distinguishes EMG activation from resting baseline in both time-domain amplitude and time-frequency structure. Leveraging the real-time wireless data link between the wearable hardware and a PC-hosted MATLAB environment, we further explore application-oriented signal processing scenarios. As an offline algorithm-pipeline compatibility demonstration, a CNN-based seizure detection pipeline is applied to the Bonn EEG benchmark for five-class epileptic state classification, achieving 86.60% mean classification accuracy. The proposed system offers a scalable and affordable foundation for wearable human-state-aware interaction, with potential applications in clinical monitoring, rehabilitation, and brain–computer interfaces. Full article

Effective wound management remains a critical challenge in modern medicine, requiring a delicate balance among infection control, hemostasis, and tissue regeneration. Biopolymer-based hydrogels have emerged as leading candidates for medical use due to their biocompatibility, moisture-retention capabilities, and structural similarity to the natural ECM. This review provides a comprehensive overview of the transition from passive dressings to intelligent, multifunctional hydrogel scaffolds. We first examine the biological mechanisms of wound healing and the fundamental roles of hydrogels in maintaining an optimal microenvironment. Central to this discussion is the integration of conductive materials (including conductive polymers, carbon-based nanomaterials, and metal nanoparticles), which empower hydrogels with bio-sensing and electromechanical stimulation capabilities. Furthermore, we explore how 3D printing technologies enable the fabrication of personalized, high-precision scaffolds. The review also discusses the emerging role of integrated monitoring systems and machine learning algorithms in enhancing diagnostic accuracy. By synthesizing current research, this review identifies critical engineering hurdles and outlines the future trajectory toward automated, closed-loop wound-care systems in clinical practice. Ultimately, while these advanced electronic scaffolds offer revolutionary therapeutic paradigms, this review underscores that balancing electroconductivity with chronic cytocompatibility, refining multi-modal biosensor calibration, and navigating complex regulatory evaluation pathways remain critical prerequisites. Overcoming these fundamental translational bottlenecks is essential to realizing the next generation of automated clinical wound care. Full article

Traditional petroleum-based packaging suffers from pollution and functional limits, making it unsuitable for next-generation food systems. In contrast, bio-based smart packaging—combining renewable substrates with responsive components—transforms packaging from a passive shell into an active quality monitor and supply chain information node through three interconnected pillars: renewability, real-time responsiveness to freshness markers, and digital traceability. Market figures confirm this shift, with the smart food packaging sector projected to reach USD 48.97 billion by 2028 (CAGR 4.49% from 2023). This review covers recent progress in natural polymers (cellulose, chitosan, alginate, gelatin) and bio-based polyesters (PLA, PHA). Their multiscale structures enable tunable mechanical and barrier properties while serving as hosts for intelligent functions. Two functional directions stand out: active preservation (antimicrobial, antioxidant, gas-regulating, stimulus-controlled release) and intelligent sensing (colorimetric indicators, bio-based sensors, nano-amplified signals for real-time freshness monitoring). Beyond material functions, digital tools such as IoT and blockchain turn packaging into interactive data nodes, linking material intelligence with full traceability to enhance food safety and supply chain efficiency. Key challenges remain with long-term operational stability, production costs, scalable manufacturing, and life cycle assessments. Nevertheless, bio-based smart packaging is expected to evolve through biomimetic design, process innovation, and system-level integration toward adaptability, multifunctionality, and intelligence, ultimately supporting safer, more transparent, efficient, and sustainable food systems. Full article