Advanced Sensor Research最新文献

Traumatic brain injuries (TBIs) sustained during sports activity represent a complex and heterogeneous spectrum of neuropathological conditions that remain underdiagnosed and often poorly managed, particularly in the amateur athletic populations. Traditional diagnostic paradigms, heavily reliant on subjective symptom reporting and clinical observation, lack the sensitivity and specificity required for early and accurate detection of mild and sub-concussive injuries. This review fills a critical gap by synthesizing recent advances in precision diagnostic tools, including AI-enhanced neuroimaging, blood-based biomarkers, and wearable biosensors, which are reshaping the detection and monitoring of sports-related TBIs. Despite significant research, diagnostic inconsistency persists, particularly in youth and amateur athletes. By integrating these converging technologies, a unified framework for earlier and more accurate detection as well as longitudinal monitoring, is proposed. Through a systems biology framework, the study evaluates the translational relevance of these tools in stratifying injury severity, monitoring recovery trajectories, and informing return-to-play decisions. Furthermore, the review addresses inherent challenges, including inter-individual variability, lack of consensus on diagnostic thresholds, ethical considerations in youth, and collegiate sports and the need for large-scale, sport-specific normative datasets. Looking ahead, the synergistic application of AI and digital diagnostics offers a transformative shift in sports neurology and public health surveillance.

Clean, disinfected surfaces and medical instruments are critical to maintaining a hygienic environment, especially in healthcare settings. Current methods for disinfection validation and training require either a long evaluation time or do not distinguish between physical (dilution) and chemical (disintegration) disinfection procedures. However, to achieve effective disinfection, both effects, dilution and disintegration, are required for many commonly used disinfectants (e.g., alcohol, sodium hypochlorite, quaternary ammonium compounds). In this study, a method is established for the real-time monitoring of surface disinfection using fluorescence-labeled DNA and lipid nanoparticles (LNP) encapsulating such DNA. It is shown that the spatial separation of quencher-modified DNA and fluorophore-modified complementary DNA by LNPs can be disrupted by ethanolic disinfectants, facilitating the disintegration of LNPs. The resulting quenching of fluorescence can immediately be detected using a manual setup comprising a hand-held laser, a color filter, and a smartphone camera. To demonstrate a potential application of this novel disinfection detection technology, disinfection of a commonly used medical instrument, a scalpel, is validated using the qualitative change in fluorescence upon disintegration of LNPs, enabling distinction between physical dilution and chemical disintegration. Therefore, LNPs spatially separating quencher and fluorophore offer real-time, qualitative monitoring of surface disinfection.

Raman Microscopy

The resonance structure of 4-nitrophenol undergoes a distinct change upon interaction with surface-modified porous silica, as revealed by Raman microscopy. The silica surface is functionalized with a complex mixture comprising covalently attached acetylated mannoside via a linker, precipitated N-acylurea, and urea. The quinone-type resonance form can be monitored in situ without the need for alkaline treatment. More details can be found in the Research Article by Osamu Kanie and co-workers (DOI: 10.1002/adsr.202500093).

The deployment of graphene flexible sensor arrays is hindered by two major limitations—difficulty in achieving high spatial resolution with existing fabrication methods and the lack of system-level integration for practical applications. To address these challenges, a fully integrated platform based on an ultrathin graphene-based strain sensor array is presented. The array is fabricated on a 5 µm-thick polyimide substrate using CVD-grown graphene and top-down microfabrication techniques. With a 4 × 4 layout and 1 mm unit pitch, a device density of ≈64units cm⁻² is achieved, enabling millimeter-scale spatial resolution. The platform integrates the full development pipeline, including sensor array fabrication, flexible circuit design, signal control, and data acquisition. The durability test reveals stable performance over 5000 bending cycles. Strain sensitivity measurements show a maximum gauge factor of 144 under 0.8% strain, while dynamic tests yield rapid response and relaxation times of 0.2 and 0.16 s, respectively. The platform reliably resolves localized pressure, monitors arterial pulse waveforms, and distinguishes surface curvatures, showcasing its multifunctional sensing capabilities. These results establish the practical viability of the proposed platform for applications in wearable health monitoring, soft robotics, and next-generation flexible electronics.

Gas sensors play a critical role in safety assurance, environmental monitoring, and health diagnostics, requiring high sensitivity, fast response, and low power consumption—especially in portable applications. This study presents graphene-based chemiresistive gas sensors fabricated on MEMS microheaters and functionalized with atomically thin layers of vanadium pentoxide or copper-manganese oxide. In these heterostructures, the metal oxide serves as the gas receptor while graphene functions as the transducer. Operated in a pulsed heating mode (115–205 °C for 0.05–1 s every 10 s), the sensors demonstrated ultra-low power consumption ranging from 13 to 520 µW. Ammonia (NH₃), a hazardous industrial gas and a biomarker in exhaled breath, is used as the target analyte. Transient conductance profiles at 4–32 ppm NH₃ are analyzed using machine learning. Feature extraction via discrete Fourier transform and prediction using a compact neural network enables NH₃ concentration estimation within 10–20 s, achieving a mean absolute error below 1% (or below 0.1 ppm at low concentrations). Despite the raw signal's sensitivity to relative humidity (RH), the model accurately predicts NH₃ concentrations without RH data. The highest accuracy and humidity robustness are achieved using signals from two sensors with different oxide coatings.

Flexible capacitive pressure sensors (CPSs) have attracted a lot of interest due to their potential applications in wearable electronics. Manufacturing flexible CPSs involves multiple steps to fabricate sensitive layers, electrode layers, and various sensitivity-amplifying microstructures. Despite significant advancements, there remains a critical need to simplify the fabrication processes, enhance the sensitivity, and create more compact pressure sensors. In this study, a single-step fabrication of a flexible CPSs in a gelatin matrix mixed with silver nitrate is reported. A seamless monolithic design is achieved by using a femtosecond laser-based direct writing technique to pattern silver electrodes in gelatin. This approach offers high-precision 3D fabrication of metallic silver in a single step. To increase sensitivity, microholes are fabricated in the gelatin layer using an amplified laser beam with the same fabrication setup. The microholes filled with air instigate a large variation in dielectric permittivity of a medium between two electrodes under applied pressure, resulting in enhanced sensitivity ≈2.44 kPa⁻¹ in the range of 0–0.5 kPa. This work highlights the potential of femtosecond laser-based direct writing of flexible sensors in next-generation wearable technologies, providing a pathway for high-performance solutions and a streamlined fabrication approach.

Prolonged storage of red blood cells (RBCs) induces morphological degradation that can compromise transfusion efficacy. Traditional quality assessment methods are often labor-intensive and time-consuming, limiting their utility in real-time settings. Although deep learning has been applied to RBC imaging, most approaches require large datasets and complex architectures, making them impractical for efficient deployment. This study introduces a holographic sensor-integrated deep learning framework for noninvasive RBC quality assessment using small datasets. A diffusion model is employed to synthetically generate phase images and segmentation masks, augmenting limited data. Self-supervised learning with pre-trained models further enhances classification performance while maintaining a streamlined model architecture. Compared to conventional segmentation methods, the proposed framework achieves higher accuracy and significantly faster inference. It also enables reliable detection of storage-induced morphological changes, providing proportional indicators of transfusion viability. Experimental results validate its effectiveness as a practical tool for real-time, sensor-driven monitoring of RBC quality.

Smart garments offer good potential to monitor and assess dynamic upper body movements, particularly from the shoulder joint with its 3 Degrees of Freedom (DOF). In this paper, a customizable lightweight smart shirt is proposed, which is designed and manufactured using Computer Numerical Control (CNC) knitting technology. This automated textile manufacturing method enables the seamless fabrication of multiple piezoresistive sensors at designated positions and at a high resolution within the garment. The paper presents a proof-of-concept system that can record both shoulder and elbow joint motions in a non-obtrusive and non-invasive manner. This system consists of: i) a fully CNC knitted smart crop top with multi-material stretchable strain sensors and interconnects; ii) a regression model, trained on several full Range of Motion (ROM) upper body actions, to convert these sensor signals into shoulder and elbow joint angles; iii) an assessment using an ergonomics software to translate these converted joint angles into muscle fatigue markers. To validate the workflow, the subject performed a pick-and-hold activity outside of the regression model's training dataset. Subsequently, the joint angles of the subject performing this pick-and-hold activity, converted from the smart garment, are compared with joint angle data from the Vicon motion capture system. Comparing the ergonomics software outputs from both datasets shows good agreement with overall low root mean square errors, the highest being 0.787% for maximum voluntary contraction (%MVC) and 1.896% for duty cycle limit (%DC).

Musculoskeletal physiotherapy is evolving with the integration of wearable technologies that enable continuous monitoring and personalized rehabilitation. This study presents a multimodal, wireless, and cost-effective wearable system designed to assess and classify joint motion using magneto-inertial sensing. The system incorporates a flexible PDMS-NdFeB magnetic skin patch and a compact sensor module with a 9-degree-of-freedom IMU. Real-time joint kinematics are wirelessly transmitted to a custom mobile application, enabling interactive visualization and feedback. Biomechanical modeling is employed to evaluate muscle contributions and joint dynamics across the wrist, elbow, and knee. Processed magnetic signals are used to classify range of motion (ROM) into three categories—Limited, Normal, and Hypermobility—through traditional machine learning and deep learning models. A 1D convolutional neural network (1D-CNN) achieves the highest classification accuracy (95.3%). The proposed system demonstrates strong potential for enhancing musculoskeletal rehabilitation by providing accurate, real-time assessments and individualized treatment feedback.