Advanced Sensor Research最新文献

Lateral flow assays (LFAs) are versatile detection devices widely used in various fields including healthcare, agriculture, and waste surveillance. Due to specific equipment needs, the fabrication of LFAs poses a large entry barrier for small laboratories wish to contribute to innovations in the field. To assist with the low-cost fabrication of LFAs, this study proposes a do-it-yourself (DIY) antibody dispenser built from commercial off-the-shelf and 3D-printed parts with a total cost of approximately 590 USD. The DIY antibody dispenser was designed based on the active flow of reagents using a pump. Detailed fabrication instructions, access to the design files, operating instructions, and troubleshooting parameters are provided to enable the construction of the dispenser. The DIY dispenser was evaluated by calibrating the line widths generated from flow rate and speed parameters, where a theoretical model was developed to explain the correlations observed. The LFAs constructed using the DIY dispenser were validated by comparing their analytical performance against LFAs made with a commercial counterpart. It was demonstrated that the new DIY dispenser is a viable avenue for the low-cost construction of reproducible LFAs.

The optical nature of ultra-broadband millimeter-wave–visible-light non-destructive inspection requires noise suppression of sensors and their appropriate operations for higher-intensity laser irradiation to flexibly choose photo-sources. While carbon nanotube (CNT) soft photo-thermoelectric sensors facilitate compact ultra-broadband monitoring, their conventional designs focus on noise. In short, efforts are insufficient for evaluating photo-response dynamic ranges and the fundamental behavior for higher-intensity irradiation. This work clarifies the surface adhesiveness of substrates dominantly controls response dynamic ranges of CNT sensors against higher-intensity photo-irradiation. The proposed CNT sensor design strategy experimentally demonstrates that higher-intensity irradiation transfers the inherent pn-junction photo-detection interface to undesired n-type channels. This work suggests adhesive chemicals of the device substrate surface function as unnecessary electron-donating groups on the entire pn-junction CNT film triggered by irradiation-induced heating. The device properly maintains the photo-detection interface for higher-intensity irradiation by appropriately designing the material composition and size for CNTs on non-adhesive substrates. This work realizes the associated CNT sensor operation at 10–100 mV response ranges customizable within diverse instruments. The optimized CNT sensor on non-adhesive substrates finally expands its photo-response upper-limit (112 mV) from that (9.98 mV) on adhesive tapes. Such approaches develop user-friendly CNT film photo-thermoelectric sensors and ubiquitous social non-destructive inspection.

Imaging biosensors take advantage of biosensing principles and imaging tools to visualize and map analytes in real specimens. They find applications in environmental monitoring, in situ biomarker detection, food safety, precision agriculture, and research. They offer valuable information about how analytes are distributed spatially, helping to uncover chemical phenomena in large-scale samples that were previously unnoticed. This perspective presents examples of imaging biosensors that utilize different detection techniques, including colorimetry, fluorescence, bioluminescence, ultrasound-assisted, electrochemical, and thermometric sensing. It discusses challenges and projects what still must be done to improve this technology and demonstrate new applications.

Accurate intraoperative identification of brain tumor margins remains a major challenge in neurosurgery. Tumors often differ from healthy brain tissue in their mechanical properties, such as stiffness and viscoelasticity, yet current imaging methods provide limited real-time mechanical feedback during surgery. In this study, the use of acoustic sensing based on surface acoustic wave (SAW) actuators to distinguish between non-neoplastic brain tissue, primary brain tumors, and metastatic tumors based on their acoustic properties is investigated. Tissue samples are measured ex vivo, and attenuation is analyzed as a function of mass and stiffness. Results showed clear, consistent trends, where non-neoplastic tissues exhibit increased acoustic attenuation, metastatic tumors exhibited intermediate attenuation, and primary tumors showed the lowest attenuation, reflecting increasing stiffness across these tissue types. These findings align with previously reported mechanical properties from techniques such as magnetic resonance elastography and microindentation, where acoustic/SAW based methodologies have significant potential advantages in throughput, cost-effectiveness and integrability with other techniques. Accordingly, this work demonstrates that SAW sensing enables reliable sensitivity to biomechanical differences between tissue types, supporting its potential as a real-time, non-invasive tool for intraoperative tumor detection.

Biofluid Sensors

The cover illustrates the selective sensing of a salivary biomarker for oral diseases — sialic acid, using an electrochemical molecularly imprinted polymer sensor. The authors introduce a disposable electrochemical sensor based on a molecularly imprinted polymer of aminophenylboronic acid electropolymerized directly on laser-induced graphene electrode, enabling selective detection of sialic acid. More details can be found in the Research Article by Alexander V. Shokurov, Max Nobre Supelnic, and Carlo Menon (DOI: 10.1002/adsr.202500156).

Fluorescent sensor arrays provide pattern‑based, multidimensional optical fingerprints for detecting chemically and biologically diverse analytes across complex matrices. By leveraging orthogonal readouts (intensity, ratiometric channels, lifetime, and excitation–emission matrices) from cross-reactive and target-specific elements, fluorescent sensor arrays achieve sensitive, rapid measurements suitable for environmental, biomedical, and food‑safety applications. The data richness of fluorescent sensor arrays, however, exceeds the capabilities of traditional analytical approaches. Classical chemometrics, exemplified by principal component analysis for exploratory visualisation and linear discriminant analysis for baseline classification, assumes linear structure and homoscedasticity, and therefore struggles with non‑linear photophysical responses, multicollinearity, and mixture quantification. This review surveys machine‑learning methods that address these limitations for both discrimination and quantification, including support vector machines and k‑nearest neighbours, tree ensembles, Gaussian process and support‑vector regression, and neural/deep‑learning models tailored for spectra, excitation–emission matrices, and images. Practical guidance is provided on acquisition and pre‑processing, rigorous validation (nested cross‑validation, external tests), uncertainty quantification, and interpretability to inform array design and deployment. Case studies demonstrate improved sensitivity, selectivity, robustness, and calibration transfer. Remaining challenges, dataset size, drift, and matrix effects, are discussed alongside opportunities in excitation‑multiplexed “virtual arrays”, active learning, and explainable AI for next‑generation, data‑driven fluorescent sensing.

Fluorescent sensor arrays provide pattern‑based, multidimensional optical fingerprints for detecting chemically and biologically diverse analytes across complex matrices. By leveraging orthogonal readouts (intensity, ratiometric channels, lifetime, and excitation–emission matrices) from cross-reactive and target-specific elements, fluorescent sensor arrays achieve sensitive, rapid measurements suitable for environmental, biomedical, and food‑safety applications. The data richness of fluorescent sensor arrays, however, exceeds the capabilities of traditional analytical approaches. Classical chemometrics, exemplified by principal component analysis for exploratory visualisation and linear discriminant analysis for baseline classification, assumes linear structure and homoscedasticity, and therefore struggles with non‑linear photophysical responses, multicollinearity, and mixture quantification. This review surveys machine‑learning methods that address these limitations for both discrimination and quantification, including support vector machines and k‑nearest neighbours, tree ensembles, Gaussian process and support‑vector regression, and neural/deep‑learning models tailored for spectra, excitation–emission matrices, and images. Practical guidance is provided on acquisition and pre‑processing, rigorous validation (nested cross‑validation, external tests), uncertainty quantification, and interpretability to inform array design and deployment. Case studies demonstrate improved sensitivity, selectivity, robustness, and calibration transfer. Remaining challenges, dataset size, drift, and matrix effects, are discussed alongside opportunities in excitation‑multiplexed “virtual arrays”, active learning, and explainable AI for next‑generation, data‑driven fluorescent sensing.

Accurate intraoperative identification of brain tumor margins remains a major challenge in neurosurgery. Tumors often differ from healthy brain tissue in their mechanical properties, such as stiffness and viscoelasticity, yet current imaging methods provide limited real-time mechanical feedback during surgery. In this study, the use of acoustic sensing based on surface acoustic wave (SAW) actuators to distinguish between non-neoplastic brain tissue, primary brain tumors, and metastatic tumors based on their acoustic properties is investigated. Tissue samples are measured ex vivo, and attenuation is analyzed as a function of mass and stiffness. Results showed clear, consistent trends, where non-neoplastic tissues exhibit increased acoustic attenuation, metastatic tumors exhibited intermediate attenuation, and primary tumors showed the lowest attenuation, reflecting increasing stiffness across these tissue types. These findings align with previously reported mechanical properties from techniques such as magnetic resonance elastography and microindentation, where acoustic/SAW based methodologies have significant potential advantages in throughput, cost-effectiveness and integrability with other techniques. Accordingly, this work demonstrates that SAW sensing enables reliable sensitivity to biomechanical differences between tissue types, supporting its potential as a real-time, non-invasive tool for intraoperative tumor detection.

Electrochemical Immunosensor

Glycoprotein 88 (GP88) is a secreted biomarker that is overexpressed in various cancers, as well as in neurological and inflammatory diseases. In the Research Article (DOI: 10.1002/adsr.202500122), Anna Ignaszak and co-workers introduce the first disposable electrochemical immunosensor built on a screen-printed electrode for the detection of a circulating GP88.