Xiaojun Xian

Wearable healthcare technologies are transforming the healthcare landscape by enabling remote, real-time health data collection, supporting early diagnosis, personalizing treatment plans, and reducing healthcare costs and medical burdens. Central to these advancements are wearable sensors, which continuously capture physiological data such as heart rate, temperature, activity levels, and biomarker concentrations. However, the large volume and complexity of this data demand effective processing to extract meaningful medical insights. Artificial intelligence (AI) and machine learning (ML) have significantly enhanced the capabilities of wearable sensors by enabling advanced data analysis, pattern recognition, and predictive modeling. AI-enhanced wearable sensors can detect early signs of health issues, such as heart attacks, chronic diseases, and mental health conditions like stress, often before clinical symptoms become apparent. This review examines the integration of AI/ML models with wearable sensors across physical activity recognition, stress assessment, cardiovascular monitoring, personal exposure monitoring, and sweat biomarker detection. Unlike prior application-centered reviews, we emphasize methodological and translational evaluation by comparing task formulations, sensing modalities, dataset scale, validation protocols, performance metrics, and deployment constraints across domains. We further discuss advanced architectures, multimodal fusion, explainable AI, edge deployment, privacy and regulatory considerations, and the translational gap between research prototypes and clinically deployable wearable AI systems.

Vertically aligned tungsten diselenide (WSe₂) nanosheets were synthesized via chemical vapor deposition and subsequently modified through nitrogen plasma treatment to obtain nitrogen-doped WSe₂ (N-WSe₂) with enhanced gas-sensing capabilities. The introduction of nitrogen dopants not only preserves the crystalline integrity of the WSe₂ nanosheets but also significantly improves their electrical conductivity and surface reactivity. Chemiresistive gas sensors based on N-WSe₂ demonstrate markedly enhanced sensitivity and lower detection limits toward both nitrogen dioxide (NO₂) and ammonia (NH₃) compared to their pristine counterparts. First-principles density functional theory calculations reveal that nitrogen doping introduces localized defect states that modulate charge transfer and spin polarization upon gas adsorption, thereby facilitating more efficient carrier modulation. This synergistic combination of structural alignment and electronic tuning offers a versatile strategy for engineering high-performance 2D TMD-based gas sensors for environmental and industrial monitoring applications.

As the healthcare system transitions from traditional hospital-centered care to point-of-care and home-care systems, the demand for wearable sensors is increasing rapidly due to their ability to continuously monitor human health and support medical diagnostics

Covalent organic frameworks (COFs), owing to their high surface area, well-defined porous structures, and abundant polar recognition sites, have shown great potential in trace gas detection. However, conventional COFs often suffer from aggregation, limited spatial distribution, and low structural utilization, which restrict their performance in gas sensors. To enhance the structural dispersity and gas sensing capability of COFs, we developed a miniaturized quartz tuning fork (MQTF) gas sensor based on a composite of EPCo-COF and pollen carbon (EPCo-COF@PC). In this hybrid sensing material, sheet-like COFs were grown in situ on a three-dimensional (3D) honeycomb-like pollen carbon scaffold, enabling effective spatial regulation of COF distribution and the formation of a stable and efficient gas transport network. The EPCo-COF@PC composite integrates abundant polar sites and 3D mass transport pathways, thereby enhancing selective adsorption of formaldehyde (HCHO), a common indoor air pollutant. Taking advantage of the high mass sensitivity of the MQTF, the adsorption-induced mass change is translated into a stable frequency shift signal. Experimental results revealed that the sensor exhibits rapid and reversible frequency responses even for ppb-level HCHO, with a broad linear detection range and excellent recovery behavior. It also shows superior selectivity against various common interfering gases and maintains 96.7% of its response after 40 days of storage at room temperature, indicating excellent long-term stability. This work provides an effective material strategy and design concept for the development of high-performance, low-cost, and integrable miniaturized gas sensors, and expands the potential applications of COF-based materials in environmental monitoring and portable sensing devices.

We have studied the structural, magnetic, and electrical transport properties of the YMB ₄ series of compounds (M= Cr, Fe, and Co). These materials exhibit high stability and possess notable refractory and thermoelectric properties. Furthermore, recent theoretical predictions suggest that some of these compounds may display quantum magnetism and dimer formation among the magnetic ions. We have synthesized almost single phase YMB ₄ compounds in the orthorhombic crystal structure (space group Pbam) using arc-melting and annealing. The Rietveld analysis of the room-temperature X-ray diffraction patterns reveals a clear change in the lattice parameter c , which correlates with the atomic radii of Cr, Fe, and Co. The temperature variation of resistivity measured between 2 K and 300 K reveals a metallic electron transport in all three YMB ₄ compounds. However, no abrupt change in resistivity due to structural or magnetic phase transitions is observed. Additionally, a weak positive magnetoresistance of 1%–2% has been measured at 2.5 K, the contribution from the metallic impurity cannot be ruled out. The carrier concentration, on the order of 10 ²¹ cm ⁻³ , has been determined using conventional Hall measurements. The thermomagnetic curves recorded between 2 K and 350 K reveal the non-magnetic behavior of all these compounds. However, a small magnetic moment from paramagnetic/ferromagnetic impurity phases is detected at low temperatures. Above room temperature, no magnetic transition associated with the breaking of a dimer is observed.

Lithium-sulfur (Li-S) batteries are promising candidates for next-generation energy storage devices due to their high energy density, low cost, and environmental friendliness. However, their practical application remains limited by the polysulfide shuttle effect, sulfur volume expansion, and poor electronic conductivity of S and Li₂S. In this study, titanium carbide (TiC) was grown on titanium foam (Ti foam) using chemical vapor deposition (CVD) to construct a binder-free, highly conductive, and porous TiC-Ti 3D framework sulfur host for enhancing the Li-S battery performance. This 3D framework combines highly porous Ti foam with the polar surface of CVD-synthesized TiC nanoflowers, enabling efficient electron and ion transport, anchoring polysulfides to mitigate the shuttle effect, and accommodating sulfur volume expansion for enhanced cycling stability. Our study demonstrated that Li-S batteries utilizing TiC-Ti 3D framework@S cathodes achieved a high initial discharge capacity of 1506 mAh g^-1 at 0.1C and maintained 93.3% of their capacity after 500 cycles at 1C, with an exceptionally low average capacity decay of 0.01% per cycle. Additionally, the TiC-Ti 3D framework@S cathodes exhibited reduced charge transfer resistance after cycling, indicating enhanced interfacial reaction kinetics and stability. These findings confirm that the CVD-synthesized TiC-Ti 3D framework can serve as an efficient, binder-free sulfur host, which provides a promising material and structural strategy for high-performance Li-S battery design.

Plants emit volatile organic compounds (VOCs) in response to biotic and abiotic stress, serving as early indicators of health. Among them, (E)-2-Hexenal─a key green leaf volatile (GLV)─signals stress from pathogen infection or herbivory. Real-time detection of (E)-2-hexenal at trace levels is crucial for early diagnosis, crop protection, and environmental sustainability. However, current VOC sensors often suffer from low sensitivity, poor selectivity, and limited detection at low concentrations. Although gas chromatography-mass spectrometry (GC-MS) is the gold standard for VOC analysis, its bulky instrumentation, high cost, and lack of portability make it unsuitable for real-time, in-field applications. To overcome these limitations, we developed a highly sensitive and selective (E)-2-hexenal sensor using a bimetallic Zn-Co/ZIF-8 receptor integrated with a microquartz tuning fork (MQTF) transducer. The sensor combines the high mass sensitivity of the piezoelectric MQTF with the nanoporous structure of Zn-Co/ZIF-8 to enable dual-mode detection: resonant frequency shift and visible color change due to chemosorption. The two-dimensional sensing performance can be finely tuned by adjusting the Zn:Co ratio, which influences both the adsorption behavior and the optical response of the material. By optimizing this ratio, the 1:1 composition demonstrated the best performance across a dynamic range of 125 ppb to 50 ppm, achieving a detection limit as low as 125 ppb. This robust, thermally and chemically stable sensor is low-cost, easy to fabricate, and ideal for real-time and in-field monitoring of plant VOCs. Its implementation could enable early plant stress detection, enhance crop management, and support sustainable agriculture.

Tungsten diselenide (WSe₂) has emerged as a highly promising material for lithium/sodium-ion batteries due to its exceptional electrical and chemical properties and unique sandwich-like two-dimensional structures. In this work, we present a chemical vapor deposition approach for synthesizing densely packed vertically oriented 2H-phase WSe₂ nanosheets uniformly grown on tungsten foil. As an electrode material, the as-synthesized WSe₂ exhibits remarkable electrochemical performance including excellent cycling stability, high-rate capability, and superior electrochemical activity. The WSe₂ nanosheets demonstrate a high reversible specific capacity of 159.3 mAh g^-1 at a current density of 5 A g^-1, maintaining a near-100% Coulombic efficiency over 2400 cycles. The underlying mechanism governing electrochemical performance was further investigated using first-principles density functional theory computations. These findings underscore the potential of WSe₂ nanosheets as high-performance materials for next-generation LIBs.

Green leaf volatiles (GLVs) are organic compounds emitted by plants in response to insect attacks, offering early detection potential. Current GLV detection methods like gas chromatography-mass spectroscopy (GC-MS) are costly and complex and lack real-time monitoring capability. There is an unmet need for affordable and portable sensors with high sensitivity to monitor GLVs in real time. In this study, we developed a novel sensor capable of capturing piezoelectric and colorimetric signals for the sensitive and selective detection of 1-hexanol, a well-known green leaf volatile. We used a piezoelectric micro quartz tuning fork (MQTF) as the multifunctional transducer. The MQTF's two prongs were coated with a metal-organic framework (MOF)-thymol blue hybrid sensing material, enabling detection through both color change and resonating frequency shift upon 1-hexanol binding. MOFs offer a high surface area and tunable pore size, which enhance sensor sensitivity and selectivity. The sensor's frequency shift indicates mass change due to 1-hexanol binding to MOFs, while the colorimetric sensing signal relies on thymol blue's reaction with 1-hexanol. Our test results demonstrate the sensor's ability to detect 1-hexanol from 62.5 ppb to 250 ppm with high sensitivity and selectivity when the colorimetric and piezoelectric sensing signals are integrated. Due to its compact size, affordability, easy fabrication, wide detection range, and high sensitivity and selectivity, this colorimetric-piezoelectric sensor could serve as an effective tool for early detection of insect herbivore attacks and timely crop protection strategies.