This study presents a comprehensive kinetic investigation for describing the dynamic sensing process of semiconductor metal oxide (SMO)-based gas sensors, with a focus on the Eley-Rideal mechanism as a valid pathway. The modeling elucidates the direct interactions between volatile organic compounds (VOCs) and adsorbed oxygen on the material surface, providing insights into the temperature-dependent response characteristics of the sensor and addressing selectivity toward different VOCs fundamentally. By incorporating the effects of thermal modulation, the kinetic model was validated through theoretical fitting of experimental data obtained from a fabricated SnO nanoparticle gas sensor exposed to ethanol, n-propanol, toluene, and butanone at systematically varying operating temperatures. The results demonstrated that the model accurately captures response transient values, enabling a general framework for qualitatively discriminating specific gases based on their unique reaction kinetics. Furthermore, a machine learning procedure containing the above model and power laws of quantitative concentrations after identifying compounds was developed for the prediction of target VOCs. The qualitative accuracy was determined to be 99.4%, while quantifying with the mean absolute errors of 5.0, 7.5, 4.0, and 8.9 within the range of 25-200 ppm, respectively. The precise and straightforward strategy facilitates the response modeling of SMO-based gas sensors, offering a valuable platform for the designing model-driven algorithms applicable in gas analysis and monitoring.

Deep-red fluorescence was implemented in this fully injectable, nonenzymatic glucose biosensor design to allow for better light penetration through the skin, particularly for darker skin tones. In this work, a novel method was developed to synthesize Cy5.5 labeled mannose conjugates (Cy5.5-mannobiose, Cy5.5-mannotriose, and Cy5.5-mannotetraose) to act as the fluorescent competing ligand in a competitive binding assay with the protein Concanavalin A acting as the recognition molecule. Using fluorescence anisotropy (FA) data, a computational model was developed to determine optimal concentration ratios of the assay components to allow for sensitive glucose measurements within the physiological range. The model was experimentally validated by measuring the glucose response via FA of the three Cy5.5-labeled mannose conjugates synthesized with Cy5.5-mannotetraose demonstrating the most sensitive response to glucose across the physiological range. The developed method may be broadly applied to a vast range of commercially available fluorescent dyes and opens up opportunities for glucose measurements using nonenzymatic assays.

Detection of slight pH changes in skin interstitial fluid (ISF) is crucial yet challenging for studying pathological processes and understanding personal health conditions. In this work, we construct an i-motif DNA based fluorescent ratiometric microneedle sensing patch (IFR-pH MN patch) strategy that enables minimally invasive, high-resolution, and sensitive transdermal monitoring of small pH variations in ISF. The IFR-pH MN patch with advanced integration of both ISF sampling and pH sensing was fabricated from the cross-linking of gelatin methacryloyl and methacrylated hyaluronic acid, wrapping with pH-sensitive hairpin-containing i-motif DNA based fluorescent ratiometric probes in the matrix. Because it is mechanically robust for skin penetration and has high swelling ability, the IFR-pH MN patch could be quickly extracted as sufficient liquid from agarose gel (∼56.4 μL in 10 min). Benefiting from conformation changes of the hairpin-containing i-motif DNA under pH variation and ratiometric fluorescence signal readout, the IFR-pH MN patch could quantitate pH over a small range between pH 6.2 and 6.9 with an accuracy of 0.2 pH units in the mimic skin model. Furthermore, testing on wound and tumor mouse models indicated the ability of the biocompatible IFR-pH MN patch to penetrate the skin for obtaining transdermal pH values, demonstrating the potential applications in monitoring and intervention of pathological states.

Tendinopathy is one of the most prevalent musculoskeletal disorders, significantly affecting the quality of life of patients. Treatment outcomes can be improved with an early diagnosis and timely targeted interventions. Increasing evidence indicates that ROS and endoplasmic reticulum (ER) stress play key roles in modulating the differentiation processes of tendon-derived stem cells (TDSCs), thereby contributing to the initiation and progression of tendinopathy. However, the relationship between ONOO and the differentiation process, as well as the various stages of tendinopathy, remains unexplored. Herein, we developed two highly specific and sensitive fluorescent probes ( and ) for detecting ONOO in the ER. can detect basal levels of ONOO in the ER of TDSCs and measure ONOO levels in primary TDSCs stimulated by interleukin-1β over various durations, allowing for comparisons between chondrogenic and osteogenic differentiation and ER stress levels. Additionally, we examined ONOO variations in different stages of tendinopathy and treatment rat models in vivo and discussed the potential mechanisms. This research provides a robust tool for analyzing ONOO dynamics in the tenogenic and osteogenic differentiation of TDSCs, offering new insights into the pathophysiology and treatment of tendinopathy.

Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model's accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.

Optical spectroscopy is a valuable tool for online monitoring of a variety of processes. Ultraviolet-visible (UV-vis) spectroscopy can monitor the concentration of analytes as well as identify the speciation and oxidation state. However, it can be difficult or impossible to employ UV-vis-based sensors in chemical systems that are very dark (i.e., have a high optical density), requiring exceedingly short path lengths (for transmission approaches) or an effective means of backscattering (for reflectance approaches). Examples of processes that produce highly absorbing solutions and that would benefit significantly from the diagnostic potential of optical sensors include used nuclear fuel recycling and molten salt systems with high concentrations of dissolved uranium. Utilizing an attenuated total reflectance (ATR) UV-vis approach can overcome these challenges and allow for the measurement of solutions orders of magnitude more concentrated than transmission UV-vis. However, determining ideal sensor specifications for various processes can be time-consuming and expensive. Here, we evaluate the ability of a novel ATR-UV-vis probe to measure very concentrated solutions of Co(II) and Ni(II) nitrate as well as organic dyes (methylene blue, acid red 1, and crystal violet). This sensor design provides a modular method for exploring possible "path lengths" by altering the length of the ATR fiber that was submerged within solution during spectral measurements. Measurements within the ATR sensor cell were compared to measurements gathered by transmission UV-vis of samples within a commercially available 1 cm optical cuvette. The ATR-UV-vis probe was capable of measuring absorbance of solutions with a chromophore concentration 600 times greater than that in the 1 cm cuvette. Advanced data analysis in the form of multivariate curve resolution (MCR) was used to analyze the speciation of methylene blue over a large concentration range. The application of this novel ATR-UV-vis probe to the investigation of dark solutions is a promising avenue for use in online monitoring of nuclear processes.

The successful application of fluorescence immunoassays for clinical diagnosis requires stable photoluminescent materials and highly efficient signal amplification strategies. In this work, the magnetism-functionalized lanthanide MOF-on-MOF (FeO@SiO@MOF-on-MOF) was synthesized through intermolecular (van der Waals) interaction-assisted growth and further homogeneous epitaxial growth, which significantly improved the fluorescence performances and uncovered the underlying mechanism. The quantum chemical theory calculation and experimental studies revealed that the introduced magnetic FeO@SiO not only endowed magnetic separation capability but also promoted fluorescence performances, which increased the energy transfer of the intersystem crossing process and suppressed the luminescence of ligands and aggregation-induced quenching. Furthermore, the plasmonic Ag/Au nanocages were developed as highly efficient fluorescence quenchers to improve the sensitivity of the fluorescence immunoassay. On the basis of the proposed differential signal amplification (DSA) strategy, the immunoassay displayed superior detection ability, with a limit of detection of 0.13 pg·mL for severe acute respiratory syndrome coronavirus 2 nucleocapsid protein. The designed magnetic lanthanide MOF-on-MOF and proposed DSA strategy give new insights into ultrasensitive fluorescence immunoassays.

The design of high-performance and low-power formaldehyde (HCHO) gas sensors is of great interest to researchers for environmental monitoring and human health. Herein, InO/CsPbBr composites were successfully synthesized through an electrospinning and self-assembly approach, and their ultraviolet-activated (UV-activated) HCHO gas-sensing properties were investigated. The measurement data indicated that the InO/CsPbBr sensor possesses an excellent selectivity toward HCHO. The response of the InO/CsPbBr sensor to 2 ppm of HCHO was 31.4, which was almost 11 times larger than that of InO alone. Besides, the InO/CsPbBr sensor also displayed extraordinary linearity ( = 0.9696), stable reversibility, and ideal humidity resistance. Interestingly, the gas-sensing properties of the InO/CsPbBr sensor were further improved (/ = 54.8) under UV light irradiation. Meanwhile, the response/recovery time was shortened to 7/9 s. The improvement of HCHO-sensing properties might be ascribed to the distinctive structure of InO nanofibers, the adsorption capacity of cesium lead bromide quantum dots (CsPbBr QDs) for UV light, and the synergistic effect of heterostructures between the components. Density functional theory (DFT) was implemented to discuss the adsorption ability and electronic characteristics of HCHO at the surface of InO/CsPbBr composites. Especially, this research points out new constructive thoughts for the exploitation of UV light improved gas-sensing materials.

This study presents a novel method for the early detection of non-small-cell lung cancer (NSCLC) by employing target-induced on-protein clustering of metal-peptide complexes to facilitate low overpotential water splitting. The approach utilizes a designed peptide molecular probe composed of an EGFR-targeting motif and a copper-chelating tetrapeptide. Upon interaction with the epidermal growth factor receptor (EGFR) and divalent copper ions, the peptide probe forms a stable complex that undergoes on-protein clustering. This clustering significantly amplifies the electrochemical signal through enhanced dityrosine cross-linking and subsequent water splitting, achieving low overpotential for detection. The method was validated using clinical tissue samples and demonstrated improved sensitivity and specificity compared with traditional detection methods. This technique holds promise for earlier and more accurate diagnosis of NSCLC, leveraging the unique properties of metal-peptide interactions and electrochemical signal amplification.

Pathogen, prevalent in both natural and human environments, cause approximately 4.95 million deaths annually, ranking them among the top contributors to global mortality. Traditional pathogen detection methods, reliant on microscopy and cultivation, are slow and labor-intensive and often produce subjective results. While nucleic acid amplification techniques such as polymerase chain reaction offer genetic accuracy, they necessitate costly laboratory equipment and skilled personnel. Consequently, isothermal amplification methods like recombinase polymerase amplification (RPA) have attracted interest for their rapid and straightforward operations. However, these methods face challenges in specificity and automated sample processing. In this study, we introduce a self-interferometric digital optofluidic platform incorporating asymmetric direct solid-phase RPA for real-time, label-free, and automated pathogen genotyping. By integration of digital microfluidics with a DNA monolayer detection method using hyperspectral interferometry, this platform enables rapid, specific, and sensitive pathogen detection without the need for exogenous labeling or complex procedures. The system demonstrated high sensitivity (10 CFU·mL), specificity (differentiating four species), detection efficiency (fully automated within 50 min for Gram-negative bacteria), and throughput (simultaneous detection of four indices). This integrated approach to pathogen quantitation on a single microfluidic chip represents a significant advancement in rapid pathogen diagnostics, providing a practical solution for timely pathogen detection and analysis.

Electrochemical aptamer-based (EAB) sensors are the first technology supporting high-frequency, real-time, in vivo molecular measurements that is independent of the chemical reactivity of its targets, rendering it easily generalizable. As is true for all biosensors, however, EAB sensor performance is affected by the measurement environment, potentially reducing accuracy when this environment deviates from the conditions under which the sensor was calibrated. Here, we address this question by measuring the extent to which physiological-scale environmental fluctuations reduce the accuracy of a representative set of EAB sensors and explore the means of correcting these effects. To do so, we first calibrated sensors against vancomycin, phenylalanine, and tryptophan under conditions that match the average ionic strength, cation composition, pH, and temperature of healthy human plasma. We then assessed their accuracy in samples for which the ionic composition, pH, and temperature were at the lower and upper ends of their physiological ranges. Doing so, we find that physiologically relevant fluctuations in ionic strength, cation composition, and pH do not significantly harm EAB sensor accuracy. Specifically, all 3 of our test-bed sensors achieve clinically significant mean relative accuracy (i.e., better than 20%) over the clinically or physiologically relevant concentration ranges of their target molecules. In contrast, physiologically plausible variations away from the temperature used for calibration induce more substantial errors. With knowledge of the temperature in hand, however, these errors are easily ameliorated. It thus appears that physiologically induced changes in the sensing environment are likely not a major impediment to clinical application of this in vivo molecular monitoring technology.

-methylpyrrolidone (NMP) is an excellent advanced solvent that can be easily absorbed by the human body and has the characteristics of flammability and explosion. To reduce the risk, the environmental concentrations of NMP need to be measured. A series of covalent organic frameworks (COF) connected by an imine bond have been successfully prepared at room temperature by changing the synthesis time catalyzed by scandium(III) trifluoromethanesulfonate (Sc(OTf)). The effect of the synthesis time on the sample properties was compared by XRD, FT-IR, XPS, SEM, TEM, and BET. The results showed that synthesis time had almost no effect on the morphology, specific surface area, and functional groups of the COF samples but had a significant impact on the pore size distribution, residual bonds, and other defects, which in turn affected the gas sensing performance. The sensor results showed that all samples had good sensing performance for NMP, among which the sample synthesized for 48 h had the best sensing performance, with a limit of detection of 692 ppb and good stability and repeatability. The excellent performance of the COF samples benefits from the large specific surface area, hydrogen bonding interactions, electrostatic attraction, and high defects. This study provides an effective method for NMP detection and expands the application range of the COF materials.

Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.

Hyperpolarized (HP) C-labeled probes are emerging as promising agents to noninvasively image pH in vivo. HP [1,5-C]Z-OMPD (Z-4-methyl-2-oxopent-3-enedioic acid) in particular has recently been used to simultaneously report on kidney perfusion, filtration, and pH homeostasis, in addition to the ability to detect local tumor acidification. In previous studies, dissolution dynamic nuclear polarization was used to hyperpolarize Z-OMPD. Here, we pioneered the hyperpolarization of [1,5-C]Z-OMPD via SABRE-SHEATH (signal amplification by reversible exchange in shield enabling alignment transfer to heteronuclei), which is relatively simple and fast and promises to be highly scalable. With SABRE-SHEATH, we achieve enhancement values of ∼3950 and ∼2400 at 1.1 T ( = 0.4 and 0.25%) on the labeled C-1 and C-5 positions of Z-OMPD. Density functional theory calculations at the B3LYP level of theory were used to investigate possible binding modes of Z-OMPD on the iridium-based polarization transfer catalyst. The experimental and theoretical results suggest that the equatorial binding mode to the catalyst, where Z-OMPD binds to the catalyst at both C-1 and C-5 carboxylate positions, is the most stable complex. The HP signals were used to measure the Z-OMPD chemical shift as a function of pH showing an ∼3 ppm shift across pH 4-11. This work lays a foundation for the development of a simple, low-cost hyperpolarization technique to image pH.

Gas sensors are pivotal across industries, encompassing environmental monitoring, industrial safety, and healthcare. Recently, a surge in demand for low power gas sensors has emerged, driven by the huge need for applications in portable devices, wireless sensor networks, and the Internet of things (IoT). The practical realization of a densely interconnected sensor network demands gas sensors to have low power consumption for energy-efficient operation. This Perspective offers a comprehensive overview of the progress of low-power sensors for gas and volatile organic compound detection, with a keen focus on the interplay between sensing materials (including metal oxide semiconductors, metal-organic frameworks, and two-dimensional materials), sensor structures, and power consumption. The main gas sensing mechanisms are discussed, and we delve into the mechanisms for achieving low power consumption including material properties and sensor design. Furthermore, typical applications of low power gas sensors are also presented, including wearable technology, food safety, and environmental monitoring. The review will end by discussing some open questions and ongoing needs.

The evaluation of selectivity/cross-sensitivity is one of the most important tests for gas sensor development, particularly that based on chemiresistive technology. It is known that chemiresistive gas sensors suffer from low selectivity when they provide sensitivity to several analytes. Typically, selectivity testing involves independently assessing a sensor's response to a specific gas. However, there is a growing need to evaluate performance with interfering gases or gas mixtures since gas sensors are always exposed to gas mixtures in practice. Despite the great importance of selectivity characterization, currently, there are no standard methods of selectivity tests at conditions when target gas coexists with interfering gas compounds, which mimics real conditions. We outlined the four main methods researchers use to evaluate the cross-sensitivity of gas sensors. It highlights key aspects of selectivity test performance, assessment methodologies, and procedure features, attempting to classify them by their distinct characteristics. This review covers the essentials of gas properties, adsorption and desorption processes, and gas molecule interactions. Finally, we tried to address the lack of standardized protocols for evaluating chemiresistive gas sensors' cross-sensitivity to interfering gases and guide researchers.

Surface functionalization of semiconducting metal oxides has emerged as a highly effective approach for enhancing their sensing capabilities. In the present work, the surface of randomly oriented zinc oxide (ZnO) nanowires is modified with an optimized thickness (7 nm) of magnesium oxide (MgO), which exhibits an exceptionally sensitive and selective behavior toward NO gas, yielding a response of approximately 310 for 10 ppm concentration at room temperature. The synergistic interplay between ZnO and MgO leads to a remarkable 20-fold improvement in sensor response compared to a pristine ZnO film and allows the detection of concentrations as low as 50 ppb. The ZnO-MgO composite was characterized using X-ray diffraction (XRD), XPS, and SEM-EDS to gain structural, compositional, and morphological insights. The interaction of the NO molecule with the sensor film was investigated using density functional theory (DFT) simulations, revealing that oxygen vacant sites on the MgO surface are most favorable for NO adsorption, with an adsorption energy of -3.97 eV and a charge transfer of 1.74e toward NO. The XPS, photoluminescence (PL), and EPR measurements experimentally verified the presence of oxygen vacancies in the sensing material. The introduction of localized levels within the band gap by oxygen vacancies significantly promotes the interaction of gas molecules with these sites, which enhances the charge transfer toward NO gas molecules. This augmentation has a profound influence on the space charge region at the ZnO-MgO interface, which is pivotal for modulating the charge transport in the ZnO layer, resulting in the substantial improvement of NO response at room temperature.

Ionic conductive hydrogel-based temperature sensors have emerged as promising candidates due to their good stretchability and biocompatibility. However, the unsatisfactory sensitivity, sluggish response/recovery speed, and poor environmental stability limit their applications for accurate long-term health monitoring and robot perception, especially in extreme environments. To address these concerns, here, the stretchable temperature sensors based on a double-side elastomer-encapsulated thin-film organohydrogel (DETO) architecture are proposed with impressive performance. It is found that the water-polyol binary solvent, organohydrogel film, and sandwiched device structure play important roles in the temperature sensing performance. By modifying the composition of binary solvent and thicknesses of organohydrogel and elastomer films, the DETO microsensors realize a thickness of only 380 μm, unprecedented temperature sensitivity (37.96%/°C), fast response time (6.01 s) and recovery time (10.53 s), wide detection range (25-95.7 °C), and good stretchability (40% strain), which are superior to those of conventional hydrogel-based sensors. Furthermore, the device displays good environmental stability with negligible dehydration and prolonged operation duration. With these attributes, the wearable sensor is exploited for the real-time monitoring of various physiological signals such as human skin temperature and respiration patterns as well as temperature perception for robots.

Sulfur-containing gases, such as HS and SO, play significant roles in a multitude of biological processes affecting human life and health. Precise and efficient detection of these gases is therefore crucial for advancing one's understanding of their biological roles and developing effective diagnostic strategies. Fluorescent sensing offers a highly sensitive and versatile approach for detecting these gases. This Review examines the recent advances in the fluorescent detection of HS and SO, highlighting the key mechanisms involved in fluorescence signal transduction, including changes in intensity and wavelength shifts. The diverse array of probe molecules employed for this purpose, including those utilizing mechanisms such as nucleophilic reactions, Förster resonance energy transfer (FRET), and sulfur affinity interactions are explored. In additional to organic sensors, the focus of the Review is particularly directed toward quantum dot (QD) systems, emphasizing their tunable optical properties that hold immense potential for fluorescence sensing. Beyond the traditional III-V QDs, we delve into the emerging fluorescence sensors based on halide perovskite QDs, upconversion nanocrystals, and other novel materials. These advanced QD systems hold promise for the development of highly sensitive and cost-effective gas detectors, paving the way for significant advances in biomedical and environmental monitoring. This Review provides a comprehensive overview of the current state-of-the-art in QD-based fluorescence sensing of sulfur-containing gases and provides a multifaceted discussion comparing organic fluorescent sensors with QD sensors, highlighting the key challenges and opportunities for the integration of fluorescence sensing as it evolves. The Review aims to facilitate further research and development of innovative sensing platforms to enable more accurate and sensitive detection of these important gases.

In the realm of wearable technology, strategically placing sensors at various body locations enhances the detection of diverse physiological indicators crucial for remote medical care. However, current devices often focus on a single body part for specific physical parameters, which hinders the seamless integration of sensors across multiple body parts and necessitates redesign for new detection capabilities. Here, we propose a modular, reconfigurable circuit assembly method that can be adaptable for multiple body locations to construct the body net. By simply reassembling different child modules with the base module using flexible printed circuit board connectors, we can efficiently detect various parameters including sweat ion indicators, electrocardiogram signals, electromyography signals, motion data, heart rate, blood oxygen saturation, and skin temperature. These data can be transmitted to a mobile phone app via a Bluetooth Low Energy protocol for further evaluation. Comparative evaluations against established commercial devices substantiate the viability of our sensor technology. In addition, results from wearable body network detections using reconfigurable sensors across multiple body parts of volunteers also indicate promising application prospects, demonstrating the extensive potential for regular health monitoring and clinical applications.