ACS Sensors最新文献

Sensitive and accurate detection of microRNAs (miRNAs) is critical for understanding biological processes, molecular diagnosis, and medical treatment. Plasmonic nanomaterials are powerful tools for optical sensing of nanoscale molecules, including DNA, enabling the construction of designed nanostructures and sensing platforms. However, the stable reusability of the sensing platform remains a challenge for practical sensing. Here, we develop a plasmonic nanobiosensor by integrating a single Au@Ag core−shell nanocube (Au@Ag NC) with azobenzene-functionalized tetrahedron-structured DNA (tsDNA), enabling cyclic microRNA-21 (miRNA-21) detection via photoresponsive conformational switching of doped azobenzene through alternating UV and visible light irradiation. We detect miRNA-21 by measuring the local surface plasmon resonance spectra and find that the peak shift has a wide range of response from 1 fM to 100 nM. The precise molecular recognition, ultrasensitive signal response, and stable photoresponsive reusability of this platform demonstrated its clinical potential as a robust miRNA detection method.

Specific and label-free biosensing with biological field-effect transistors (bioFETs) is highly pursued due to their high-end sensing performance, low-cost, and potential for multiplexed sensing in ultrasmall samples. Still, bioFET sensing in physiological samples, such as whole blood, presents two major hurdles: (1) short screening lengths due to high ionic strength and (2) nonspecific response due to high background population of various biological entities. Traditionally, the former challenge requires sample dilution or the employment of short receptors, and the latter requires multiple premeasurement washing steps for the removal of the nonspecific species. Hence, these required steps of sample preprocessing and multiple premeasurement washing are deleterious to the application of bioFET toward self-use, home-use, POC, bedside, etc. applications. To address this unmet need, we present a new method for low-cost, real-time, quantitative biosensing suitable for the above-mentioned applications. The presented approach is based on the Meta-Nano-Channel (MNC) bioFET. The MNC bioFET is specifically designed to detect minute concentrations of biomolecular targets in a label-free, specific, real-time, quantitative manner and in ultrasmall samples. This capability is enabled by the deterministic design of the MNC bioFET toward the identification of localized molecular interactions. The study presents sensing of L-Dopa in 0.5 μL of whole blood samples. Importantly, no preprocessing of the blood is required, and the sensing is performed directly in blood without premeasurement washing for the removal of nonspecific signals. L-Dopa is the cornerstone of the symptomatic treatment of Parkinson’s disease, and its dose in clinical settings is titrated according to clinical response. Measurement of L-Dopa plasma levels is performed in processed blood using analytical methods, which are costly, time-consuming, and irrelevant for standard clinical settings. We demonstrate a limit-of-detection of 10 fg/mL and a dynamic range of 10 orders of magnitude with excellent sensitivity and linearity. The methods and mechanisms employed by the MNC bioFET to address the challenges of screening length and nonspecific adsorption are discussed. The MNC bioFET is a promising methodology for future self-use and point-of-care medical diagnostics.

The complexity of in situ volatile organic compounds (VOCs) detection demands high selectivity and anti-interference capability of electronic noses (E-noses). Multivariant virtual sensor array (VSA) is a new generation of E-noses under research to overcome the insufficient VOCs selectivity limitation of existing electronic noses and alleviate the issue of sensor drifts. In this paper, a frequency-amplitude dual-parameter modulation strategy is presented on a wireless electrodeless quartz crystal microbalance with dissipation (WE-QCM-D) to realize the recognition and analysis of VOC mixtures and complex real-world analytes with a single multivariant VSA E-nose, by probing the gas dynamic sorption in a sensitive film at various scales of oscillating shear displacements and obtaining multiple partially independent responses to VOCs. Ten VOC analytes from alcohols, esters, and aromatic hydrocarbons were classified with accuracies of above 95% for both interclass and intraclass discriminations. Additionally, a discriminating accuracy of 95% has been achieved on VOC mixtures, and their component concentrations were predicted with coefficients of determination above 0.9. For practical testing, the system was exposed to the headspace VOCs of banana, pineapple, and mango under different concentrations. It recognized different fruits and identified the ripen state of bananas based on the detection of their volatiles. The dual-parameter modulated WE-QCM-D paves a promising multivariant way to realize online real-time VOCs monitoring with high performance on selectivity and quantitative analysis.

Hybrid microcavity systems combining optical resonators with responsive materials offer a promising route toward tunable, multifunctional photonic devices. Here, we demonstrate a hydrogel-based microfiber laser enhanced by plasmonic nanoparticles that enables single-mode operation with high sensitivity. The hydrogel network acts as a disordered scattering medium, inducing random lasing, while the microfiber geometry supports whispering-gallery modes with strong optical feedback. By tuning the microfiber diameter, we systematically investigate the interplay between microcavity modes and scattering. Incorporation of Au nanoparticles further enhances the optical confinement through localized surface plasmon resonances, providing single-mode lasing control. The resulting device exhibits strong humidity responsiveness and operational stability, with a sensitivity of 10³ pm/% RH and a rapid response time of 3.2 s. The results reported here provide a versatile approach for integrating smart materials and microcavities, advancing the development of ultrasensitive hydrogel-based photonic sensors.

The development of automated microfluidic systems for ultrasensitive detection of biomaterials via surface-enhanced Raman spectroscopy (SERS) represents one of the most promising areas in current research. Within this field, special attention is directed toward SERS-based detection and analysis of extracellular vesicles, aimed at identifying disease biomarkers either in the form of microRNA and mRNA or membrane-bound proteins. However, practical applications of SERS detection systems, particularly those employing silver-based SERS substrates, are significantly limited due to their temporal instability caused by surface contamination and oxidation. In this work, we propose a fabrication method and operational concept for SERS substrates that are activated immediately prior to experimentation, serving as a basis for integration into chip-based spectral recording chambers within automated microfluidic systems. The nonactivated SERS substrates described herein can be embedded into microfluidic chips and stored for extended periods without loss of functionality, being activated just before the start of experimental procedures. A theoretical model was developed to evaluate local electromagnetic field enhancement in such SERS substrates. The sensitivity of these substrates was determined experimentally, demonstrating the feasibility of rapid detection of individual extracellular vesicles from HEK293T cells as well as their clusters.

Acetone, a key biomarker for metabolic processes, and its excessive levels can cause ketosis or even ketoacidosis, posing significant risks to human health. Therefore, real-time acetone detection is crucial for noninvasive health monitoring. Ti₃C₂T_x-based sensors demonstrated considerable potential for detecting VOC gases but often face issues with sensitivity and environmental stability. In this work, a flower-like Ti₃C₂T_x anchored with MoS₂ quantum dots (F–Ti₃C₂T_x/MoS₂) was proposed for the first time to achieve accurate acetone detection even under high humidity conditions. The unique flower-like morphology significantly increased the surface area of Ti₃C₂T_x and amplified its electron scattering effects. MoS₂ modification not only reduced the content of Ti defects but also formed a passivation layer, providing a novel approach to address the inherent oxidation issue of Ti₃C₂T_x. Moreover, the p–n heterojunction between F–Ti₃C₂T_x and MoS₂ promoted charge separation, enabling high-performance acetone detection. Compared with intrinsic Ti₃C₂T_x, a 4.83-fold enhancement in response to 25 ppm of acetone was achieved by using a F–Ti₃C₂T_x/MoS₂ sensor with an ultralow detection limit of 163.2 ppb and a rapid response/recovery time (26.0 s/33.7 s). When integrated into a portable breath analyzer, the sensor demonstrated accurate acetone monitoring under atmospheric conditions, underscoring its potential for real-time and noninvasive health diagnostics.

The increasing interest in the precise detection of mixed gases in the industrial field has led to an unprecedented level of demand for gas sensing equipment. However, a single sensor with the classic single-output signal and cross-sensitivity can no longer meet the application requirements for detecting complex gas environments. Herein, we propose a feasible way to realize the low-cost fast detection of CO, H₂S, and NO₂ mixed gases based on an integrated sensor array. Sensor units with significant response distinguishability are designed. The rapidly switched heating signal is applied to the microheater, and then the time-domain and frequency-domain feature parameters are extracted to train the machine learning models. The results show that the proposed method significantly reduces the required data set size for rapidly detecting mixed gases, and successful classification and prediction can be achieved using only the response data of the first 20 s of the adsorption process. Eventually, an average accuracy of 96.30% and a determination coefficient (R²) of 0.97 have been achieved for the concentration classification and prediction toward the CO, H₂S, and NO₂ ternary mixtures. It is now possible to move an important step toward fully utilizing metal oxide semiconductor sensor arrays for rapid gas sensing applications.

Traditional detection methods for soil nitrate nitrogen (NO₃^––N), a critical nutrient for crop growth, suffer from poor timeliness and susceptibility to matrix interference. To address these issues, this study presents a novel dual-band frequency-splitting coupled sensor with a concentric copper ring structure for in situ rapid detection of soil NO₃^––N via dielectric spectroscopy. The key technological innovation lies in using the low-frequency band (1–50 MHz) to isolate water and salinity interferences via stable impedance matching of the copper rings and capturing the characteristic NO₃^––N relaxation signals in the high-frequency band (100–500 MHz) via enhanced electromagnetic coupling. Field trials across five soil types (brown, black, red, saline-alkali, and loess) demonstrated excellent performance, with determination coefficient (R²) values of 0.943–0.987, mean absolute error values of ≤0.75 mg/kg, root-mean-square error values of ≤0.92 mg/kg, and millisecond-level response. Signal drift remained <0.25 mg/kg even under extreme conditions (−5 °C, 90% relative humidity (RH)), with Pearson correlation coefficient values of 0.995–0.999 in typical agricultural scenarios (pre/postfertilization and precipitation). The developed sensor eliminates the need for sampling and pretreatment, reducing the detection time from 3–5 days using traditional methods to milliseconds, and provides high-precision data for dynamic nitrogen management. Moreover, the IoT integration potential of the sensor advances smart agriculture and sustainable development.

Self-powered gas sensors based on zinc–air batteries (ZABs) integrate battery and gas sensing functions. Various gaseous reduction reactions at the battery cathode will induce a measurable and reproducible change in the battery’s output voltage, eliminating the need for any external power supply. However, they still face significant challenges in achieving ultrasensitive, selective, and drift-resistant sensing under complex conditions. Herein, we report a NO₂ gas sensor based on a ZAB, utilizing Fe-doped nickel phosphide (FNP) as the gas-sensitive material and incorporating deep learning algorithms to enhance the sensing performance. In the context of FNP gas-sensitive layers, the charge carrier mobility is significantly enhanced, owing to the electron delocalization and redistribution induced by the Fe dopants. Furthermore, the shift in the d-band center toward the Fermi level induced by Fe dopants facilitates stronger O 2p/Fe d orbital hybridization. As a result, the adsorbate–substrate interaction is enhanced and the Gibbs free energy of the NO₂ reduction reaction is reduced. The charge density difference also indicates the facilitated electron transfer from FNP to NO₂ and the electron accumulation at the O sites. Consequently, the sensors exhibit a high response (0.22 V @ 20 ppm), a low limit of detection (LOD: 61.8 ppb), and fast sensing speed (14 s). After that, the introduction of the InceptionTime model and wavelet transformation algorithm enables the sensor to achieve remarkable gas recognition and concentration quantification, along with a significantly reduced LOD of 36.9 ppb. Finally, a smart sensing device is constructed with the sensors, microcontrollers, and electrochromic devices for remote and visualized gas detections.

In the emerging ammonia (NH₃) economy, both olfactory and visual sensing are desired for leakage detection, in which olfactory sensing enables a fast response and wide detection range, while visual sensing ensures high selectivity. However, little is reported on the sensing material as a mediator to synergistically combine the dual-modality NH₃ sensing. Here, Co₃[Co(CN)₆]₂ microcubes (Co–CN MCBs) mediators have been developed for olfactory and visual dual-modality NH₃ sensing. In olfactory sensing, porous chitosan-decorated Co₃O₄/Co₃[Co(CN)₆]₂ microcages (CS@Co–O/Co–CN MCGs) have been obtained via [Co(CN)₆]^3–-governed self-assembly, glycerol-assisted reduction, and Kirkendall effect-driven hollowing. Beneficially, MCGs exhibit a 1 s fast response (500 ppm) and a 0.5–10,000 ppm wide detection range at room temperature. In visual sensing, colorimetric sensing paper (CSP) incorporated with anthocyanin, Co–CN, and poly(vinyl alcohol) has been achieved, which presents 10–320 ppm visual NH₃ detection through a pink-to-brown transition. Further, highly selective NH₃ sensing has been acquired through machine learning-powered classification of MCGs’ response and CSP’s specific NH₃ chromogenic behavior. Notably, combining dual-modality sensing has achieved desired NH₃ detection due to the boosted NH₃ adsorption on Co–CN MCBs’ active sites and the synergistic effect of their-mediated heterostructures. Practically, our dual-modality sensing device has been integrated to detect NH₃ with reliable responses.