Search Results (1,127)

The detection of glucose is a critical aspect of healthcare and biomedical research, particularly for the management of diabetes mellitus. Among various sensing technologies, surface plasmon resonance (SPR)-based optical fiber sensors have emerged as a promising platform due to their high sensitivity, real-time monitoring capabilities, and miniaturization potential. This paper explores the development and application of a molecularly imprinted polymer (MIP)-coated eccentric core optical fiber SPR sensor for glucose concentration detection. The integration of MIP technology with SPR sensing enables enhanced specificity and selectivity towards glucose molecules, while the eccentric core structure of the optical fiber contributes to improved light–matter interaction and sensitivity. The amplitude sensitivities are calculated as 0.88771 [mmol/mL]⁻¹ for the 3% glucose solution, 0.35161 [mmol/mL]⁻¹ for the 3.5% solution, 0.20425 [mmol/mL]⁻¹ for the 4% glucose solution, 0.89041 [mmol/mL]⁻¹ for the 5% solution, and 1.55825 [mmol/mL]⁻¹ for the 7% solution. The proposed sensor exhibits a simple geometry and presents itself as a promising candidate for glucose solution concentration detection. Full article

This research investigates the effects of electrochemical oxidation on surface properties and corrosion performance of the Ti-6Al-4V ELI alloy intended for biomedical applications. Electrochemical anodization is performed in 1 M H₂SO₄ and 1 M H₃PO₄ electrolytes at applied potentials of 200, 250, and 275 V for 1 min. Morphological characteristics and chemical constitution of the oxide films are investigated by SEM-EDS analysis, while surface roughness, wettability, and microhardness are evaluated using profilometry, contact angle measurements, and Vickers microhardness testing. Electrochemical behavior is assessed by monitoring free potential (OCP) and electrochemical impedance spectroscopy in Ringer solution and Ringer solution containing 40 g/L hydrogen peroxide. Among the investigated conditions, anodization at 200 V for 1 min provides the most favorable surface morphology, producing well-defined and uniformly distributed nanopores while maintaining the structural stability of the oxide layer. Oxidation in 1 M H₂SO₄ leads to a more homogeneous nanoporous structure, higher surface roughness, improved hydrophilicity, and increased microhardness compared to 1 M H₃PO₄ treatment. Electrochemical impedance spectroscopy analysis reveals superior corrosion resistance for all oxidized samples in comparison with the untreated alloy. The oxide layers obtained in sulfuric acid exhibit the highest polarization resistance and electrochemical stability in simulated physiological environments. Full article

Titanium alloys exhibit exceptional strength-to-density ratios, high hardness, and outstanding resistance to elevated temperatures, making them indispensable structural materials in aerospace engineering, marine construction, and biomedical applications. In aerospace systems specifically, fatigue failure represents the predominant failure mode for titanium alloy components. This review systematically examines prevalent surface treatment techniques for titanium alloys—including shot peening, ultrasonic rolling treatment, hot isostatic pressing (HIP), physical vapor deposition (PVD), micro-arc oxidation (MAO), and thermal spray processes—and critically evaluates their respective effects on fatigue performance. The underlying mechanisms of each technique are concisely outlined, with emphasis on stress state evolution, near-surface microstructural refinement, and interfacial integrity. Building upon the characteristic surface-dominated fatigue fracture behavior of titanium alloys, this work focuses on how coating composition, architecture (e.g., graded, multilayer, or nanocomposite designs), and interfacial bonding strength govern fatigue resistance. A unified analysis is presented on the distinct yet complementary roles of substrate deformation strengthening (e.g., residual compression, grain refinement) and coating-mediated protection (e.g., barrier function, crack deflection, stress redistribution) during fatigue crack initiation and propagation. Key determinants of fatigue performance, including residual stress distribution, coating/substrate adhesion, thermal mismatch, and environmental degradation susceptibility, are rigorously assessed. Finally, emerging research frontiers are identified, including intelligent process–structure–property mapping, in situ monitoring of fatigue damage at coated interfaces, and design of multifunctional gradient coatings that synergistically enhance strength, wear resistance, and fatigue endurance of titanium alloy components. Full article

In this paper, a novel multiband microwave resonator is proposed and investigated for non-invasive glucose sensing applications. The structure is based on a compact, planar spiral-like geometry fed by a Coplanar waveguide (CPW) transmission line, designed to support multiple resonant modes through nested concentric rings. A full electromagnetic model was developed to predict the resonance behavior analytically, achieving excellent agreement with Computer Simulated Technology (CST) simulations across four resonant frequencies (2.7, 6.44, 8.0, and 12.8 GHz). The sensor demonstrated high glucose sensitivity at multiple frequencies, with peak values reaching 0.05 dB/mg/dL and 0.038 dB/mg/dL at 10.1 GHz and 6.22 GHz, respectively. To enhance conformability and skin contact, the antenna was further transformed into a semi-cylindrical flexible form suitable for finger-wrapping. Despite the mechanical deformation, the structure preserved its resonance while offering enhanced near-field interaction with biological tissues. The folded sensor achieved a sensitivity of 0.032 dB/mg/dL at 5.25 GHz and a peak gain of 6.05 dB, validating its robustness for wearable deployment. The clear correlation between reflection magnitude and glucose level (with R > 0.99) confirms the sensor’s potential as a passive, multiband, and non-invasive glucose monitoring platform. The physics-informed residual deep learning framework significantly enhances prediction accuracy, achieving an RMSE of 0.28 mg/dL, MARD of 0.13%, and confining 100% of both training and holdout predictions within the <5% ISO-like risk region, thereby ensuring robust and clinically reliable non-invasive glucose estimation. Full article

This study proposes a unified multimodal temporal motion state perception framework for optical imaging-oriented biomedical applications, integrating visual skeleton sequences, inertial measurement unit (IMU) signals, and surface electromyography (EMG) signals. The framework utilizes modality-specific encoders and a cross-modal temporal alignment attention mechanism to explicitly model temporal offsets from heterogeneous sensing streams. A multimodal temporal Transformer backbone is introduced to capture long-range motion dependencies and cross-modal interactions, while an uncertainty-aware fusion module dynamically allocates weights based on modality confidence. Experimental results demonstrate that the proposed approach achieves an accuracy of 94.37%, an F1-score of 93.95%, and a mean average precision of 96.02%, outperforming mainstream baseline models. Robustness evaluations further confirm stable performance under visual occlusion and sensor noise. These results indicate that the framework provides a highly accurate and robust solution for rehabilitation assessment, sports training monitoring, and wearable intelligent interaction systems. Full article

This study details the design and development of an ultra-sensitive microwave sensor for non-invasive blood glucose monitoring, achieved by analyzing variations in the response of a split-ring resonator (SRR) through advanced engineering methodologies. There were three design phases in the development process. In the first phase, a standard SRR design was used. It had a resonant frequency of 2.975 GHz in S₂₁ and a sensitivity of only 0.0032 dB/(mg/dL). In the second phase, an interdigital capacitor (IDC) was added to the SRR structure. This made it work better and made it more sensitive, with a sensitivity of 0.015 dB/(mg/dL) at 4.1 GHz. The third phase was to use a fourth-order Moore fractal geometry to improve the resonance properties of the design a lot. From the obtained S₁₁, the maximum sensitivity was 0.042 dB/(mg/dL), which was a huge improvement in sensing efficiency compared to earlier designs. Several resonant frequencies were recorded between 4.84 and 7.56 GHz. The addition of the fractal structure made the electromagnetic field stronger in the resonant space and made the waves interact more with small changes in the biological medium, all without changing the sensor’s size (80 mm × 40 mm). These results show that fractal architecture is a promising way to create non-invasive, accurate, and easily integrated sensors in biological systems that can continuously measure blood glucose levels. Full article

The management of complex acute and chronic wounds remains a formidable challenge in modern medicine, underscoring the urgent need for advanced therapeutic strategies that accelerate healing, prevent infection, and promote functional tissue regeneration. Electrospun nanofibers have attracted considerable attention in the biomedical field due to their extracellular matrix-like architecture, high surface area, interconnected porosity, and tunable physicochemical composition, which drive advances in wound regeneration, tissue engineering, and biopolymer-based therapeutics. In wound healing, nanofibrous dressings composed of natural polymers such as chitosan, gelatin, collagen, and cellulose promote cell attachment and proliferation, support angiogenesis, and enable infection control while delivering bioactive agents, thereby addressing significant challenges related to inflammation, biocompatibility, and antimicrobial resistance. In tissue engineering, aligned and hierarchically organized scaffolds fabricated from biopolymers such as collagen, gelatin, chitosan, and cellulose enhance the guided orientation of cells, differentiation, and functional regeneration of neural, musculoskeletal, vascular, and skin tissues. In addition to their conventional regenerative applications, recent studies have demonstrated that electrospun biopolymer nanofibers can be used in multifunctional biomedical platforms, including smart and stimuli-responsive systems for drug delivery, biosensing, regenerative interfaces, and wearable medical technologies. The integrated constructs that incorporate diagnostic or therapeutic functionalities, hybrid fabrication approaches that combine 3D printing with electrospinning, and intelligent biopolymer frameworks that enable telemedicine, real-time physiological monitoring, and personalized regenerative therapies offer new opportunities for developing improved biomedical systems. Overall, these advances position electrospun nanofiber systems as promising biomaterials for next-generation biomedical innovation. This review summarizes recent progress in tissue-engineered scaffolds, wound dressings, fabrication strategies for integrative therapeutics, and wearable devices with transformative potential for biomedical applications. Finally, the review addresses significant challenges related to scalability and clinical translation. It offers perspectives on future directions, including the integration of artificial intelligence and the regeneration of complex skin appendages, which will shape the next generation of nanofiber-based wound-healing therapies. Full article

Closed-loop vasopressor systems integrate real-time blood pressure monitoring with automated decision logic to support hemodynamic stability in perioperative and critical care environments. These technologies sit at the intersection of biomedical sensing, signal processing, and clinician-supervised automation: the quality, latency, and failure behavior of the monitoring input can directly shape controller performance, safety margins, and clinical usability. In this comprehensive review, we synthesize the major closed-loop vasopressor architectures reported in the literature, examine how sensor modality and signal integrity influence algorithm behavior, and summarize recurrent reliability vulnerabilities spanning sensors, control logic, and device integration. We organize the field through an end-to-end information pipeline—monitoring input, signal conditioning and quality assessment, decision and control strategy, actuation via infusion technology, and supervisory safety layers—highlighting common performance metrics used to benchmark control quality. We then discuss clinical validation patterns across settings, emphasizing practical considerations for deployment and the evidence gaps that remain most relevant to high-risk populations. Finally, we propose reporting and validation priorities for future studies, with a focus on sensor robustness, transparency of algorithm design, integration safeguards, and standardized documentation of failures and overrides. Full article

Responsive photonic crystals, as a class of intelligent photonic materials that can generate optical signals in response to external stimuli including temperature, pH, humidity, electric fields, magnetic fields, and specific molecules, exhibit great application potential in sensing and detection, drug delivery, environmental monitoring, and other fields. This is attributed to their unique tunable photonic band gaps and visual sensing characteristics. In this paper, we systematically review the main preparation methods, including the sol–gel method, photolithography, electrochemical deposition, self-assembly, and 3D printing, and compare the advantages and limitations of each method in terms of key performance indicators, cost, applicable material systems, and scalability for large-scale production. The design strategies of photonic crystals based on different response mechanisms are discussed in detail, revealing the structure–activity relationship between external stimuli and the modulation of photonic band gaps. Meanwhile, the typical applications of responsive photonic crystals in the sensing field are comprehensively summarized, with an emphasis on the latest advances in biomedical sensing, environmental monitoring, and intelligent detection. Finally, in view of the current challenges facing responsive photonic crystals, future development directions are prospected, which provides a reference for promoting the translation of responsive photonic crystals from laboratory research to industrial applications. Full article

Raman spectroscopy offers unique molecular fingerprinting capability for cancer diagnosis and monitoring, yet its biomedical application is fundamentally limited by weak intrinsic signals and complex biological backgrounds. Composite Raman probes, particularly surface-enhanced Raman scattering (SERS)—based systems, overcome these limitations through synergistic electromagnetic and chemical enhancement combined with functional integration. By engineering plasmonic nanostructures, interfacial electronic states, and molecular architectures, composite Raman probes achieve synergistic electromagnetic and chemical enhancement while incorporating biorecognition units, reporter molecules, and protective coatings to improve stability, specificity, and biocompatibility. In recent years, these probes have evolved from simple signal tags into multifunctional platforms capable of ultrasensitive tumor biomarker detection, high-contrast imaging, surgical guidance, therapy monitoring, and dynamic analysis of the tumor microenvironment (TME). This review systematically summarizes recent advances in composite Raman probes for oncological applications, with an emphasis on material design strategies, enhancement mechanisms, and stimulus-responsive regulation. Representative applications at both molecular and tissue levels are highlighted, including nucleic acid, protein, and exosome detection, as well as in vivo imaging and microenvironmental sensing. Finally, current challenges and future perspectives toward clinical translation are discussed, aiming to provide guidance for the rational design of next-generation Raman probes for precision oncology. Full article

Biomaterials are engineered to interact with biological systems for therapeutic or diagnostic purposes. Among them, natural biomaterials offer important advantages over many synthetic polymers, including intrinsic biocompatibility, non-toxicity and biodegradability. Silk fibroin, a fibrous protein derived mainly from Bombyx mori cocoons, has re-emerged as a particularly versatile platform because it combines favourable mechanical, thermal, electrical and optical properties with aqueous processing and tuneable degradation. In this review, we first summarise the key structural, physicochemical and functional properties of regenerated silk fibroin, including its mechanical behaviour, thermal stability, dielectric and piezoelectric response, optical transparency and low autofluorescence. We then describe how extraction and regeneration protocols are used to produce defined material formats—fibres and nanofibrous mats, porous 3D scaffolds and hydrogels, sub-micron particles, thin films and microstructured devices—and outline major functionalisation strategies, ranging from physical blending and encapsulation to covalent chemistry, genetic engineering of recombinant silk variants, and enzyme-mediated conjugation approaches. Building on this foundation, we critically examine biomedical applications of silk fibroin with a particular emphasis on (i) hybrid silk–fluorophore systems for bioimaging and biosensing (nanodiamonds, quantum dots and organic dyes), (ii) optical fibre, wearable and edible sensors for health and food monitoring, (iii) wound dressings and wound-sensing platforms, and (iv) tissue engineering scaffolds and drug-delivery depots. Finally, we discuss current limitations, including process variability, the trade-offs introduced by blending and cross-linking, and the challenges posed by non-degradable inorganic fillers and clinical translation. Together, these perspectives highlight silk fibroin’s potential and constraints as a multifunctional biomaterial for next-generation biomedical devices and theranostic systems. Full article

Polarized side-scattering techniques are widely used in aerosol detection, oceanographic optics, and biomedical sensing due to their high sensitivity to weak optical signals in low-scattering coefficient media. Conventional polarized Monte Carlo methods face significant challenges in such regimes due to geometric mismatch, where photon exit positions deviate substantially from the detector plane. This study addresses the geometric mismatch issue in polarized Monte Carlo simulations for side scattering in low-scattering media (scattering coefficient ${\mu }_s=$ 1 cm⁻¹), where photon exit positions often deviate from the detector plane. We propose a novel algorithm incorporating backward ray tracing with geometric projection correction to enhance simulation accuracy. Experimental validation was conducted using 532 nm laser illumination on both 500 nm polystyrene microspheres (${\mu }_s=$ 0.21 cm⁻¹) and 5 nm TiO₂ nanoparticles (${\mu }_s=$ 1.06 × 10⁻⁶–1.06 × 10⁻⁵ cm⁻¹). The results demonstrate excellent agreement between simulations and experiments, confirming the algorithm’s capability to accurately capture the polarization characteristics of side-scattered light. This work provides a high-fidelity simulation tool for designing optical sensors in low-scattering media and holds direct applicability in nanoparticle concentration sensing and aerosol monitoring. Full article

Chitooligosaccharides (COS) are short-chain chitosan derivatives with a wide range of biomedical, agricultural, and environmental applications, including antimicrobial therapy, wound healing, and pollutant removal. Reliable quantification of COS is essential but currently relies on high-performance liquid chromatography, mass spectrometry, or capillary electrophoresis, which require costly equipment, complex sample preparation, and are unsuitable for routine or on-site applications. This study reports a rapid, solvent-free, colorimetric assay for COS based on the reaction of 5% aqueous ninhydrin with free amino groups in McIlvaine buffer. The assay was optimized using glucosamine as a model analyte, yielding maximal sensitivity at pH 7.0. The chromophore generated (Ruhemann’s purple) remained stable for over 120 min after reaction, allowing measurements to be taken without strict time constraints. Calibration was linear from 0.4 to 2.2 mM (R² = 0.9926), with low limits of detection (0.006 mM) and quantification (0.018 mM). Increasing absorbance with COS polymerization degree (DP1–DP6) demonstrates specificity for free amino groups, while N-acetyl glucosamine showed a negligible response. Furthermore, the assay was successfully adapted for solid-phase detection on ninhydrin-pretreated filter paper and nitrocellulose, with enhanced sensitivity. This simple, efficient, and low-cost method provides an accessible alternative to instrumental techniques, supporting COS monitoring in laboratory workflows and enabling portable applications in biomedicine, agriculture, and environmental diagnostics. Full article

Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, residual stresses, distortions, microstructural variability, and the complexity of the coupled process-parameter space. This review examines how artificial intelligence (AI)—including machine learning, deep learning, and optimization algorithms—is being applied to address these challenges across the MAM workflow. A structured literature review was conducted covering studies published between 2015 and 2025, identified through searches in Scopus, Web of Science, and IEEE Xplore. The selected literature is analyzed according to key functional domains of metal additive manufacturing: design for additive manufacturing (DfAM), process modeling and simulation, in situ monitoring and control, and microstructure and property prediction. AI approaches are further categorized by learning paradigm, including supervised learning, deep learning, reinforcement learning, and hybrid physics–machine learning models. The review highlights recent advances in AI-assisted parameter optimization, defect detection, and digital-twin frameworks for process supervision. At the same time, it identifies persistent challenges, particularly the scarcity and heterogeneity of datasets, limited transferability across machines and materials, and the need for uncertainty-aware models capable of supporting validation and certification. Overall, the analysis indicates that the integration of multi-sensor monitoring with hybrid physics-informed AI models represents the most promising near-term pathway to improve process reliability, reduce trial-and-error experimentation, and accelerate industrial qualification in metal additive manufacturing. Full article

This paper presents a novel dual-mode memristor-based ring oscillator designed for energy-efficient, wireless biomedical signal conditioning systems. The proposed architecture leverages a compact DTMOS memristor emulator, consisting of only two transistors and one capacitor, to replace the conventional NMOS pull-down devices in a three-stage PMOS ring oscillator. This integration enables two distinct operating modes within a single compact core: a fixed-frequency mode for stable clock generation and carrier synthesis, and a programmable chirp mode for frequency-modulated signal generation. The fixed-frequency mode achieves continuous tuning from 3.142 GHz to 4.017 GHz via varactor control, with an ultra-low power consumption of only 111 µW at 4.017 GHz. The chirp mode generates linear frequency sweeps starting from 0.8 GHz, with the sweep range independently controllable through the state capacitor value and the pulse width of the control signal (SW_Chirp). Designed in a standard 0.18 µm CMOS process, the oscillator exhibits a low phase noise of −87.82 dBc/Hz at a 1 MHz offset for the three-stage configuration, improving to −94.3 dBc/Hz for the five-stage design. The overall frequency coverage spans 0.8–4.017 GHz, representing a 133.6% fractional range. The calculated figure of merit (FoM) is −169.45 dBc/Hz. Experimental validation using a discrete CD4007 prototype confirms the oscillation principle, while comprehensive simulations demonstrate robust performance across process corners and temperature variations. With its zero-static-power memristor core, wide tunability, and dual-mode reconfigurability, the proposed oscillator is ideally suited for multi-standard wireless biomedical applications, including implantable telemetry, neural stimulation, ultra-wideband (UWB) transmitters, and non-contact vital sign monitoring. Full article