ACS Sensors最新文献

Preventing the spread of airborne diseases in crowded indoor spaces is a global challenge. Infected individuals release virus-laden respiratory droplets (RDs) that can remain suspended in air and infectious for hours. Current monitoring methods cannot distinguish these droplets from airborne particulate matter (PM) in a real time. Here, we present a capacitive sensor that selectively detects and counts the individual droplets in indoor spaces, regardless the presence of PM. The device exploits the dielectric constant (ε) of water (78.2) to differentiate the droplets from solid PM particles (ε < 15). In a nonventilated conference-room study, RDs concentrations (40–330 RDs/L) were found to be correlated with human occupancy, but not with PM_2.5 levels. The developed technology enables a real-time monitoring of number concentration of RDs, which represent a potential health risk when they carry viral or bacterial infections. The detected increase in RD concentration can serve as a trigger for data-driven ventilation and infection-prevention measures, providing an effective tool for mitigating the spread of respiratory diseases in hospitals, schools and other public spaces.

Efficient monitoring of methane is crucial for avoiding gas explosions in industrial processes. Metal oxide methane sensors exhibit promising gas detection performance, which has been well studied recently. However, conventional metal oxide sensors suffer from high operating temperatures, limited selectivity in multigas interference scenarios, and insufficient compatibility between hardware efficiency and algorithmic complexity for real-life applications. Here, we developed methane sensors based on SnO₂-Ag-ZnO composite materials. Experimental results demonstrate that Ag-doping reduces the optimal operating temperature and enhances the methane response 1.79-fold, compared with that of pure SnO₂ sensors. Introducing ZnO further amplifies gas adsorption and reaction activity by heterojunction effects. Furthermore, the SqueezeNet transfer learning model was applied to analyze the gas response signals, achieving 91.6% accuracy in the classification task of combustible gas mixtures. This research provides a comprehensive solution for monitoring methane in complex gas mixture environments.

We report the development of a dual-gas Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) sensor operating in the mid-infrared range for the simultaneous detection of ¹²CH₄ and ¹³CH₄. The sensor employs a frequency-modulated multiplexing scheme using two distributed-feedback quantum cascade lasers to independently excite the fundamental (f_o) and overtone (f₁) vibrational modes of a quartz tuning fork coupled with resonator tubes. The f₀-demodulated signal is dedicated to monitoring ¹²CH₄, while the f₁-demodulated signal selectively quantifies ¹³CH₄, enabling the analysis of the isotopic composition of methane samples. Calibration measurements demonstrated a linear response of the QEPAS signal to varying ¹³CH₄ concentrations in CH₄-based samples diluted in N₂, with a precision of 1‰ in detecting isotopic delta ratio variations for 1% CH₄ mixtures at 0.8 s integration time. The proposed system is suitable for real-time, high-precision isotopic methane sensing aimed at applications such as environmental monitoring, geochemical tracing, and industrial process control.

The development of the digital enzyme-linked immunosorbent assay (ELISA) has improved the detection of low-abundance protein biomarkers in biological samples, achieving a 1000-fold increase in sensitivity compared to conventional protein detection methods. However, existing digital ELISA technologies encounter difficulties in simultaneously identifying numerous protein biomarkers at ultralow concentrations, particularly in trace samples. This limitation calls for improvements in both detection multiplicity and sensitivity. Herein, a simple digital ELISA platform, the Barcodes Integrated SlipChip (BIS-chip), was developed for the ultrasensitive identification of multiple proteins from trace samples. The BIS-chip employs a two-step loading method that eliminates substrate precatalysis, thereby ensuring the specificity of 10-plex target detection. Additionally, its high bead-loading efficiency improves the assay sensitivity for each target. By counting digital “On” and “Off” signals, the BIS-chip quantifies a panel of 10 cytokines with low limits of detection (LoD) in the femtogram per milliliter (fg/mL) range, achieving a sensitivity improvement of several orders of magnitude compared with standard highly multiplexed suspension array technology. To validate its clinical applicability, the BIS-chip was used to simultaneously measure 10 cytokines from only 20 μL of intralesional plasma of patients diagnosed with oral lichenoid reactions (OLRs), a condition where cytokines are typically undetectable via commercial multiplexed immunoassays. Supported by machine-learning algorithms, the OLR diagnostic models were successfully developed with 95.7% sensitivity and 100% specificity. With its exceptional detection multiplicity, ultrasensitivity, and straightforward workflow, the BIS-chip provides an innovative solution for the quantitative analysis of clinical low-abundance biomarkers in trace samples.

Traditional plant pathogen detection often relies on molecular technologies, which allow species-level detection but are often time-consuming. Plant volatile organic compounds (VOCs) have recently been harnessed to assist in disease detection and plant health monitoring. However, current VOC detection methods are unsuitable for field use due to the need for expensive laboratory equipment and slow processing times. To address this, we developed a portable paper-based colorimetric sensing technology for early detection of ramorum blight in rhododendron caused by Phytophthora ramorum. This colorimetric sensor array, which includes nanomaterials and organic dyes, was optimized to detect alcohol, terpene, and ester, key VOC biomarkers emitted by infected rhododendron leaves. Color quantification was done quickly by smartphone imaging. Principal component analysis (PCA) was used to cluster and classify individual plant volatiles. Our VOC sensing platform detected ramorum blight 2 days after inoculation, aligning with real-time loop-mediated isothermal amplification (LAMP) analysis. Moreover, the platform distinguished pathogen-induced VOCs from those produced by nonbiological stresses such as drought and mechanical damage. This noninvasive diagnostic technology demonstrates significant potential for disease detection in the field.

With the increasing demand for target biomarkers in multiplexed simultaneous detection, encoding technologies with sufficient encoding capacity are urgently needed. Here, we present a suspension array based on thickness-encoded, MIP-functionalized quartz microchip arrays for reaction and high-throughput detection. This platform incorporates a custom thickness-fluorescence decoding system that achieves micrometer-scale resolution over a millimeter dynamic range, allowing for 440 distinct codes. The platform demonstrates detection limits of 7.6, 7.9, and 15 μg mL^–1 for human, mouse, and rabbit IgG, respectively. Fabricated via a simple one-pot process, these arrays eliminate the need for expensive reagents or lithography, enabling high-throughput analysis. Quantitative multiplexed assays demonstrate high sensitivity, selectivity, and temporal stability, establishing this MIP-based platform as a robust tool for environmental monitoring, food safety, and clinical diagnostics.

Despite widespread use of SnO₂-based conductometric gas sensors, the sensing of NO₂ remains poorly described on the atomic scale, limiting the design of next-generation sensors. Here, near-ambient-pressure X-ray photoelectron spectroscopy combined with in situ resistance measurement was used to investigate the interaction between NO₂ and a SnO₂-based sensor at room temperature and 300 °C. Through stepwise exposure and evacuation cycles, we tracked real-time changes in the O/Sn atomic ratio and electronic structure alongside the macroscopic resistance response. Exposure to NO₂ consistently increased the O/Sn ratio, indicating the healing of surface oxygen vacancies, and this effect directly correlated with an increase in resistance. At room temperature, the response was cumulative and irreversible, while at high temperatures, it was rapid, reversible, and saturated at lower gas concentrations. These findings directly support vacancy-modulated “surface conductivity” and provide experimental validation that NO₂ sensing in SnO₂ occurs via modulation of shallow donor concentrations, rather than through the classical description of ionosorption of charged oxygen species. The results contribute to an emerging unified model of gas sensing and offer insight into how dynamic equilibrium between vacancy healing and regeneration underpins temperature-dependent sensor behavior.

Sweat glucose detected by wearable sensors can serve as a potential reference factor for diabetes management. However, inefficient sweat collection and fluctuating sweat pH hinder the precise detection of glucose. Herein, we designed a wearable sensing system comprising an ant-nest-structured sensor and a pH-responsive microchannel with multiple sweat inlets to continuously and accurately monitor the glucose level in sweat. Compared with the traditional planar electrode, this internally interconnected biomimetic structure provided a large number of accessible pores for contact with glucose molecules and electrolytes, remarkably decreasing the diffusion resistance. Furthermore, due to the formation of adjacent distributed cascade sensing units, the electron transmission distance from the catalytic center to the electrode surface was shortened, and the inoperative diffusion of H₂O₂ in the sensor was also weakened. The microchannel could convey the perspiration from the epidermis to the sensing zone within 250 s, while the coated pH indicator in the microchannel monitors the liquid’s pH in a colorimetric zone. As a result, our wearable sensor integrated with a pH-responsive microchannel accurately tracked the change of glucose levels in sweat before and after a diet, displaying reliable results after calibration similar to those of commercial colorimetric kits. In addition, a longitudinal investigation of the relationship between blood and sweat glucose from healthy people and diabetic patients revealed a good correlation in glucose between the two. Wearable biosensors for real-time noninvasive analysis of glucose in sweat may significantly facilitate diabetes management at home.

The merits of an ideal wearable sensor are high sensitivity, excellent stability, and user-friendly operation, typically requiring room-temperature storage and sample-to-answer detection capability. Here, we developed aptamer-functionalized multivalent fluorescent DNA nanostructures (Ap-MFDNs) and demonstrated that they can improve the detection sensitivity for sweat biomarkers, i.e., cortisol, lactate, and uric acid, compared with monovalent probes. The results showed that the multivalent binding strategy exhibited varying effects on enhancing the detection sensitivity for different small molecules, which was particularly evident for cortisol. Molecular docking was employed to help understand these differences. Meanwhile, we demonstrated that the Ap-MFDNs can be stored as a lyophilized powder under appropriate ionic strength. Based on the multivalent DNA nanostructures, we designed corresponding wearable sensors for direct detection of the above three targets in human sweat with the aid of smartphones. The detection range of the wearable sensor can cover the physiological levels of the three small molecules in sweat. Therefore, multivalent DNA nanostructures have the potential to improve the performance of wearable sensors.