Research Interests

Wearable Sensors for Health, Agriculture, and Environment

The growing demand for real-time, continuous monitoring in healthcare, agriculture, and environmental protection calls for sensors that are not only highly sensitive and selective, but also portable, user-friendly, and low-cost. Our research focuses on designing and fabricating next-generation wearable and point-of-care chemical sensors and biosensors to address these global challenges. By integrating advanced materials, device engineering, and artificial intelligence, we develop platforms that transform raw signals into actionable insights for human health monitoring, plant disease diagnosis, and environmental assessment.

CMOS Imager–Based Colorimetric Sensing: We have pioneered the use of CMOS imaging chips as chemical sensors, enabling miniaturized, high-throughput, and multiplexed detection of multiple analytes with exceptional sensitivity and specificity. This platform offers unique advantages—calibration-free operation, lightweight design, low power consumption, and continuous monitoring—making it highly compatible with wearable and portable devices. Our group has engineered micron-scale sensing spots (~10 μm), using nanocomposites of colorimetric probes and silica nanoparticles, to achieve scalable, multiplexed detection across millions of pixels. These sensors can detect gaseous biomarkers, pollutants, and green leaf volatiles emitted by crops under stress, with their performance further enhanced through AI-powered detection algorithms and optimized sensor designs that improve gas diffusion and extend lifespan.

Piezoelectric Chemical Sensing: In parallel, we advance piezoelectric micro quartz tuning fork (MQTF) sensors, which translate analyte binding events into precise frequency shifts. By coating the prongs with molecularly imprinted polymers and nanostructured films, we achieve highly selective detection of volatile organic compounds (VOCs) and fine particulate matter (PM2.5). Moving beyond conventional single-signal sensors, we developed a novel colorimetric tuning fork (CTF) platform that integrates both optical and mechanical signals, producing comprehensive, orthogonal data for robust analyte detection. With its compact size, low cost, reversibility, and simple fabrication, this dual-mode sensor is ideally suited for wearable and IoT applications, offering a powerful tool for continuous monitoring in human health, environmental safety, and precision agriculture.

Our vision is to create intelligent, multifunctional wearable sensors that seamlessly merge with everyday life, providing continuous insight into our bodies, crops, and environment—ultimately empowering smarter healthcare, sustainable farming, and cleaner communities.

Multiplexed Chemical Sensing CMOS Imager

MXene-Based Piezoelectric Gas Sensor

Piezoelectric-Colorimetric Hybrid Sensor for Monitoring Green Leaf Volatiles

Related Publications:

1. Laxmi R Jaishi, Wei Ding, Rick A Kittelson, Francis Tsow, and Xiaojun Xian*, Metal-Organic Frameworks (MOFs)-Based Piezoelectric-Colorimetric Hybrid Sensor for Monitoring Green Leaf Volatiles, ACS Sensors, 9, 6553-6562 (2024) (https://doi.org/10.1021/acssensors.4c02016)

2. Jingjing Yu, Di Wang, Vishal Varun Tipparaju, Wonjong Jung, and Xiaojun Xian, Detection of Transdermal Biomarkers of Macronutrients Intake Using Gradient-Based Colorimetric Array Sensor, Biosensors and Bioelectronics, 195, 113650 (2022)

3. Laxmi R Jaishi, Jingjing Yu, Wei Ding, Francis Tsow, and Xiaojun Xian*, A Novel Colorimetric Tuning Fork Sensor for Ammonia Monitoring, Sensors and Actuators B-Chemical, 405, 135342 (2024) (https://doi.org/10.1016/j.snb.2024.135342)

4. Laxmi R Jaishi, Wei Ding, Rick A Kittelson, Francis Tsow, and Xiaojun Xian*, Metal-Organic Frameworks (MOFs)-Based Piezoelectric-Colorimetric Hybrid Sensor for Monitoring Green Leaf Volatiles, ACS Sensors, 9, 6553-6562 (2024) (https://doi.org/10.1021/acssensors.4c02016)

5. Amirhossein Amjad, Xiaojun Xian*, Optical Sensors for Transdermal Biomarker Detection: A Review, Biosensors and Bioelectronics, 267, 116844 (2025) (https://doi.org/10.1016/j.bios.2024.116844)