Dual-emission carbon dots with UV-induced fluorescence enhancement: Construction of fluorescence sensor array and ratiometric probe for discrimination and detection of antibiotics.

A dual-mode paper-based microfluidic sensor array with high peroxidase-like activity at neutral pH for on-site therapeutic drug monitoring of opioids.

Qualitative discrimination of common phenolic disinfectants by sensor array based on nanozymes for facile environmental sensing.

• A transparent micro-thermistor array enables multisite cellular temperature sensing • Simultaneously, the sensor array is compatible with fluorescence imaging of organelle structures • Inverse thermal analysis allows estimation of organelle-specific thermal conductivities Temperature-dependent thermal conductivities reflect structural heterogeneity in cells Quantitative determination of heat flow through subcellular structures is essential for understanding intracellular heat transport and the thermophysical properties of living cells. However, organelle-resolved thermal properties cannot be measured because existing intracellular thermometry techniques either perturb cells with exogenous probes or lack the spatial resolution required for organelle-scale analysis. To overcome these limitations, we developed a transparent vanadium dioxide (VO₂) micro-thermistor array that enables simultaneous, multisite temperature measurements in one to several adherent cells while maintaining full compatibility with fluorescence microscopy. This micro-thermistor array uniquely integrates transparent VO₂ thermistors with fluorescence-compatible microfabrication, enabling the estimation of organelle-resolved thermal conductivity through thermal-circuit-based inverse analysis. The six-element array, fabricated on a quartz substrate with indium tin oxide (ITO) electrodes, exhibited a temperature coefficient of resistance of 1.51%°C⁻¹, temperature resolution of 8.8 m°C, and sufficient visible transmittance for confocal fluorescence imaging. By integrating dynamic temperature responses under localized IR laser heating with three-dimensional fluorescence images of nuclei, mitochondria, cytoplasm, and other organelles, we constructed a thermal-circuit model and performed inverse analysis to estimate the thermal conductivities of individual organelles. The resulting organelle-level conductivities reflected known intracellular organization, showed distinct temperature dependence at 25°C, 37°C, and 45°C, and yielded effective whole-cell thermal conductivities of 0.55–0.70 W m⁻¹ K⁻¹, consistent with previously reported single-cell values. These findings demonstrate that transparent micro-thermistor arrays function as probe-free sensors enabling organelle-resolved estimation of intracellular thermal properties, establishing a quantitative sensing platform for the systematic analysis of heat transport and thermoregulation in living cells.

• Sustainable e-nose systems enhance real-time monitoring of meat and seafood freshness • Metal oxide nanostructures and biodegradable sensors reduce environmental impact • AI and machine learning improve the detection of VOCs in spoilage assessment • Integration with IoT and smart packaging enables non-invasive freshness evaluation • Case studies show e-nose systems can reduce food waste and improve shelf-life control • Self-powered and MEMS-based sensors lower energy consumption in food quality monitoring Electronic nose (e-nose) sensor arrays have emerged as a crucial technology for meat and seafood preservation through their ability to detect volatile organic compounds (VOCs). The growing need for sustainable food preservation methods has driven significant developments in this field, particularly focusing on environmental responsibility and economic viability. This review examines recent innovations in e-nose technology, focusing on sustainable materials and energy-efficient designs. It analyzes developments in sensing materials, including metal oxide semiconductors and biodegradable components, along with energy-efficient innovations such as self-powered sensors and optimized arrays. The study also evaluates the integration of e-nose systems with spectroscopic methods, biosensors, and sustainable cloud computing solutions, supported by machine learning algorithms. The review reveals significant advancements in sustainable e-nose technology, demonstrating improved detection accuracy while maintaining environmental responsibility. Integration with complementary technologies has enhanced comprehensive quality assessment capabilities. Case studies in meat and seafood preservation showcase the technology's potential for reducing food waste and improving monitoring efficiency. While challenges remain in optimizing sensor selectivity and stability for low-concentration VOCs, ongoing developments in sustainable materials and energy-efficient designs indicate promising future applications in food preservation practices. These innovations contribute to both environmental sustainability and economic feasibility in the food industry.

• Systematically integrates four core sensing mechanisms with cutting-edge material innovations (2019–2025) : For the first time, it categorizes piezoelectric, piezoresistive, triboelectric, and capacitive tactile sensors by their physicochemical principles, constructing a "material-mechanism-performance" roadmap. This framework incorporates landmark advances such as Liu et al.’s (2024) hierarchical structural design for wide-range detection, Kang et al.’s (2024) wireless integrated e-skin, and a 2025 PVDF-based hybrid piezoelectric-triboelectric platform with 200% enhanced output, addressing the fragmentation gap in existing reviews. • Critical evaluation of state-of-the-art multimodal signal decoupling strategies: Targeting the long-standing crosstalk bottleneck, it dissects two validated solutions with 2022–2025 evidence: signal decoupling (e.g., Yin et al.’s graphene-based temperature-pressure separation) and structural optimization (e.g., Xie et al.’s 2024 triboelectric-capacitive static/dynamic pressure array). It further benchmarks Yang et al.’s (2025) interference-free dual-mode sensing against emerging architectures like 2024 leather-based printed sensor arrays, providing actionable design guidelines. • Bridges e-skin and human-computer interaction (HCI) to reveal the "multimodal system" paradigm shift: Breaking disciplinary silos, it positions multifunctional tactile sensors as the "perceptual core" linking Song et al.’s (2023) collagen organogel e-skin for health monitoring and Yang et al.’s (2024) triboelectric-optical hybrid sensors for immersive HCI. This integration aligns with 2024 dual-modal e-skin for bidirectional human-robot interaction and 2025 AI-driven tactile perception systems, highlighting the field’s move beyond single-function devices. • Identifies three urgent unresolved challenges grounded in 54 recent studies: Based on critical analysis of 2019–2025 literature, it pinpoints persistent bottlenecks: unaddressed signal interference despite Yang et al.’s (2025) advances, lack of human-like pain perception in e-skins, and nascent AI-tactile integration. These insights resonate with 2025 breakthroughs like Tactile-Diffusion Policies for robotic manipulation and super-resolution sensor arrays enabled by deep learning, guiding targeted future research. In recent years, traditional unimodal sensing mechanisms have exhibited significant limitations, making it difficult to meet the growing demand for composite signal acquisition. Consequently, an increasing number of scholars have dedicated themselves to the research of multifunctional tactile sensors. Characterized by high sensitivity, a wide-range detection capability, rapid dynamic response, and excellent repeatability, multifunctional tactile sensors have evolved into a critical interface in robot-environment interaction processes. Application fields such as electronic skin systems and human-machine interaction interfaces are becoming focal points of academic attention. This paper systematically reviews the research progress of multifunctional tactile sensors in the field, focusing on the material properties, device design, and performance differences of different sensing mechanisms such as piezoelectric, piezoresistive, triboelectric, and capacitive. Through horizontal comparison, the study reveals the comparative advantages and trade-offs of these sensors in various application scenarios. It also systematically elaborates on their specific applications in electronic skin construction, health monitoring, and human-machine interaction scenarios. Finally, it summarizes the core challenges currently faced by multifunctional tactile sensors in terms of signal crosstalk, environmental stability, and integration process, and envisions future directions for breaking through bottlenecks by integrating interdisciplinary approaches such as artificial intelligence, advanced materials, and novel architectures. In recent years, traditional single sensing mechanisms have exhibited significant limitations, making it difficult to meet the growing demand for composite signal acquisition. Consequently, an increasing number of scholars have dedicated themselves to the research of multifunctional tactile sensors. Characterized by high sensitivity characteristics, wide-range detection capability, rapid dynamic response, and excellent repeatability, multifunctional tactile sensors have evolved into a critical mediating carrier in robot-environment interaction processes. Application fields such as electronic skin systems and human-machine interaction interfaces are becoming focal points of academic attention. This paper systematically reviews the research progress and significant achievements of multifunctional tactile sensors in recent years. Based on their current shortcomings, their future development paths and application potential in electronic skin construction and human-machine interaction technology are elucidated.

• Stretchable 4 × 4 IrOₓ pH array with super-Nernstian (∼71 mV/pH) sensitivity • Integrated Zn-ion battery and BLE readout for autonomous wireless operation • PDMS-sealed Ag–PDMS vias enable stable pH sensing under prolonged strain • Real-time, high-density pH mapping demonstrated on tissue and food samples Spatial pH mapping on soft and deformable surfaces is important for applications such as wound healing assessment and food freshness mapping. However, conventional rigid pH sensors have limited use in these fields due to poor mechanical compliance, inadequate spatial resolution, and the need for wired power supplies. Here, we present an autonomous stretchable sensor array based on iridium oxide (IrO x ), powered by a low-power wireless multiplexed readout and a flexible zinc-ion battery, for 4 × 4 spatial pH sensing. These microstructurally optimised IrO x sensors exhibit a narrow super-Nernstian sensitivity of 71.25–72.69 mV/pH, with a drift below 0.01 pH/hour and robust performance after 100 stretchable cycles. The bespoke circuit employs a gain-optimised, low-noise analogue front-end with 16-channel acquisition, achieving near full-scale Analog-to-Digital Converter (ADC) utilisation (1 mV resolution) and sensitivity deviations below 1% when operated in ultra-low-power mode for wireless readout. The flexible zinc-ion battery integrates seamlessly with the circuits, providing sufficient operating time for the intended applications, and offering higher safety and biocompatibility than conventional lithium-based systems. This platform was validated on chicken and fish tissues at different stages of degradation and under mechanical strain, demonstrating strain-induced deviations below 0.02 pH. The proposed platform offers a reliable, mechanically compatible and fully integrated solution for real-time conformal biochemical mapping in clinical, environmental and food quality applications.

Gaussian process regression algorithm for radiation source localization based on scintillation fiber optic sensor networks

Time-resolved dual nanozyme colorimetric array for antioxidant fingerprinting and beverage shelf-life assessment.

• High-resolution monitoring reveals species responses to pulse disturbances. • Gaussian HMM framework for detecting species vocalisation dynamics. • Using changes in log vocalisation counts and constraints on the mean to focus on temporal variability. • Three-state mean-constrained HMM detects a warning state preceding disturbances. • Identified short-term behavioural dynamics in response to pulse disturbance. Continuous biodiversity monitoring is crucial for understanding ecosystem dynamics in an era of global environmental change. Advances in bioacoustic hardware facilitate autonomous monitoring of vocalising animals in terrestrial and aquatic ecosystems. Time series of processed audio data can provide insights into multi-species behavioural responses to imminent disturbances. Here, we present a general Gaussian hidden Markov model (HMM) framework for analysing processed species detection data from audio recordings to identify changes in species behavioural dynamics under sudden and short-term (pulse) disturbances. Our framework transforms species detection data by calculating the logarithmic change in species detection counts between consecutive time points, focusing on shifts in temporal variability rather than counts per se . The framework includes a suite of HMMs with varying complexities in their number of states, constraints on the mean, and inclusion of covariates. We recommend an ensemble of in-sample and out-of-sample model selection methods that balance complexity, interpretability, and forecasting ability. We illustrate the framework using processed bird species detection data from an acoustic sensor array in Okinawa, Japan. To demonstrate the ability of our framework to detect changes in species vocalisation behaviour, we analysed 66 days of bird vocalisation data from before, during, and after two large typhoons struck Okinawa in 2018. A parsimonious three-state mean-constrained model and its non-homogeneous variant with precipitation were selected. The estimated HMM states represent ‘ambient’, ‘warning’ and ‘disturbed’ periods, respectively capturing low, medium, and high variability in vocal activity. A warning state consistently preceded a disturbed state, suggesting that our framework could help detect early behavioural responses to impending pulse disturbances. These findings demonstrate how species behavioural dynamics inferred from high-resolution monitoring can provide early warning signals of emerging ecological disturbances.

Adaptive helmet design for optically pumped magnetometry

Three-dimensional spatial damage localization in GFRP using embedded piezoresistive sensor arrays

Monitoring flavour component variation during chilli pepper drying using a colourimetric sensor array and complementary optical strategy based on multi-modal data fusion.

Finger pulp-inspired flexible pressure sensor array for gap balance monitoring during total knee arthroplasty.

MOF-enhanced colorimetric sensor array for early detection of citrus infestation by Bactrocera dorsalis

A dual-functional fluorescent sensor based on a single soybean-derived carbon dots for discrimination and quantification of tetracyclines in food.

Flower-like g-C3N4/TiO2 array sensor integrated with a flow injection thin-layer cell for continuous and sensitive photoelectrochemical detection of copper ions

Perfluorooctanoic acid (PFOA) precursors are a class of compounds that are commonly released into the environment through aqueous film-forming foams (AFFFs) and are known to decompose into PFOA. PFOA is one of the most used per- and polyfluoroalkyl substances (PFAS), a class of highly persistent synthetic chemicals. Exposure to PFOA through environmental contamination has been linked to a variety of health concerns, and precursors from AFFFs are sources of PFOA contamination. Although PFOA precursors are often not considered, studies have demonstrated that they contribute to the overall levels of PFOA contamination, meaning that the ability to detect them is important for removing PFOA from the environment. However, the detection of PFOA precursors is limited to mass spectrometry methods, which are expensive and time-consuming. While higher-throughput methods have been developed for PFOA, no high-throughput sensing platforms have been reported for PFOA precursors. To address this problem, we developed a fluorescent sensor platform for detection and differentiation of three specific PFOA precursors, both from each other and from PFOA itself. We demonstrate that dynamic combinatorial libraries (DCLs) made up of dithiol monomers and templated with a solvatochromic fluorophore can be used to form a sensor array that achieves this detection and differentiation at low nanomolar, environmentally relevant concentrations. We can discriminate individual PFOA precursors from each other and perfluoroalkyl carboxylic acids of varying chain lengths, mixtures of varying ratios of the precursor to PFOA, and use our system in complex samples extracted from soil spiked with the precursors. To our knowledge, this is the first report of a fluorescence-based method for the detection and differentiation of PFOA precursors.

The advancement of high-efficiency multifunctional sensing systems for physiological signal detection holds considerable promise in the intelligent electronics field; however, it still poses substantial challenges. In this work, we present a dual temperature/pressure sensor system, which incorporates a flexible 3D MXene-based 3 × 3 microsupercapacitor array fabricated by inkjet printing and an integrated 3 × 3 waterborne polyurethane (WPU)/MXene bimodal sensor array. These MXene-based microsupercapacitors demonstrate a remarkable energy density of 2.12 mW h cm-2 along with a power density of 22.59 mW cm-2, rendering them a reliable energy supply for powering temperature and pressure sensors. Meanwhile, the WPU/MXene composite possesses large interlayer spacing, abundant Ti-O terminals, and a 3D porous network structure, all of which contribute to enhancing the detection performance. The bifunctional sensor arrays achieve a high temperature coefficient of -1.17%/°C within the range of 25-55 °C, as well as a favorable pressure sensitivity of 0.211 kPa-1 in the range from 0 to 20 kPa. They are capable of monitoring physiological conditions encountered in daily human life, such as real-time finger pressure and respiratory signals, without any mutual signal interference. Finite element analysis further confirms the crosstalk-free operation of the system, ensuring accurate multiparameter detection capabilities. This study, which focuses on the design and integration of MXene-based materials, provides a universal platform for the next generation of multifunctional sensing systems.

Detection and discrimination of structurally similar triazole fungicide (TF) subtypes remain highly desirable yet challenging. This work presents a straightforward room-temperature phosphorescence (RTP) sensor array for the visual discrimination of TFs, based on host-guest doping-induced RTP signal amplification. Five TF subtypes were doped into a boric acid (BA) matrix via thermal treatment, yielding intense, multicolored, and long-lived afterglow composites. The rigid BA matrix amplified the phosphorescence of guest molecules by reducing the singlet-triplet energy gap and suppressing nonradiative decay. The composites exhibited concentration-dependent RTP fingerprints in terms of emission color, intensity, and lifetime, enabling the discrimination of TFs, including binary and ternary mixtures, through linear discriminant analysis and hierarchical cluster analysis. Time-resolved RTP signal collection effectively eliminated background interference from autofluorescence and scattering, ensuring robust detection in real samples. Furthermore, an intelligent artificial vision platform utilizing the DenseNet algorithm achieved automated identification of TF types and concentrations directly from afterglow images with high accuracy (>91%) and speed (<1 s). This study offers a visual strategy for trace-level TF discrimination, demonstrating significant potential for on-site environmental and food safety monitoring.