My research focuses on wearable devices, biomarker sensors, electrochemical characterization, and soft materials. I design and fabricate enzymatic and ionic sensors, build supporting hardware, and apply mathematical and machine learning models to turn raw physiological signals into reliable, clinically meaningful measurements.
I am particularly interested in bridging soft materials, microfabrication, and hardware design to advance non-invasive continuous health-monitoring technologies.
Wearable, non-invasive electrochemical biosensors — from soft materials and microfabrication to hardware design and machine-learning signal processing.
Publication Summary
First / Co-first author5 papers in Biosensors and Bioelectronics, IEEE MEMS, and Annals of Biomedical Engineering — [1][3][4][5][8]
Contributing author3 papers in Nature Communications, Sensors and Actuators B: Chemical, and Journal of Machine Design — [6][7][9]
Manuscript under review1 first-authored paper submitted to Nature Sensors — [2]
Research Experience
Wearable Epidermal Biosensor for Interstitial-Fluid Non-Invasive Monitoring
State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University · Sep. 2024 – Present
1. Hydrogel sensor drift correction
Investigated molecular-interaction-induced drift of hydrogel-based electrochemical sensors and proposed an impedance-driven drift-correction strategy for long-term compensation.
Conducted molecular dynamics (MD) and COMSOL simulations, hydrogel preparation, screen-printed sensor fabrication, and EIS/CA characterization.
Reduced current coefficient of variation by 10.3× (glucose) and 5.4×(H₂O₂) across varied hydrogel thickness and analyte concentration.
[1]
M. Wang, X. Zhang, W. Zhu, J. Li, J. Wang, D. Li, W. Li, Z. Pu,
"Impedance-guided drift correction for long-term hydrogel electrochemical sensors,"
IEEE MEMS 2027, Big Island, Hawaii, USA. (Accepted)
First authorImpedance-guided drift-correction strategy for long-term hydrogel electrochemical sensors.
[2]
M. Wang, X. Zhang, W. Zhu, J. Li, J. Wang, D. Li, W. Li, Z. Pu,
"Drift correction in diffusion-constrained hydrogel sensors for continuous electrochemical monitoring."
Submitted to Nature Sensors.EIS-guided drift-correction framework: anomalous diffusion modeling, GFSW fitting, and corrected continuous monitoring.
2. Non-invasive lactate detection via reverse iontophoresis (RI)
Researched alternating-polarity RI for continuous interstitial-fluid lactate sensing; optimized sampling parameters to suppress Ag/AgCl extraction-electrode degradation.
Completed the majority of in-vitro and in-vivo experiments; designed the relay circuit and C/Python scripts for automated measurement.
Lead post-publication translational engineering efforts toward commercial deployment.
Graphical abstract — alternating-polarity reverse iontophoresis for continuous lactate monitoring.Conducting human experiments to validate the non-invasive reverse-iontophoresis lactate sensor. Photographed on June 4, 2026.
Concept and sensing mechanism — reverse iontophoresis extraction and LOx-based lactate detection.Presenting my poster on non-invasive continuous lactate monitoring at IEEE MEMS 2026, Salzburg, Austria. Photographed on January 28, 2026.
[5]
W. Zhu#, M. Wang#, J. Li, J. Wang, W. Li, D. Li, H. Zheng, X. Zhang, Z. Pu,
"Continuous lactate monitoring via alternating-polarity reverse iontophoresis for exercise metabolism,"
IEEE MEMS 2027, Big Island, Hawaii, USA. (Accepted)
Co-firstIn-vivo continuous lactate monitoring: alternating-polarity reverse iontophoresis vs. fixed polarity, correlated against fingerstick blood lactate.
3. Non-invasive blood-glucose detection via reverse iontophoresis
Contributed to wearable sensor fabrication (screen-printing, glucose / Na⁺ / pH sensors) and assisted with clinical trials and data analysis.
Led commercial-translation optimizations: reference-electrode membrane, interface circuitry, and in-human validation.
Wearable finger-PPG blood-glucose monitoring device and MLP-based prediction system.
Early-stage prototype demonstration video.
Kinematics Design & Simulation of Ultrasonic TOFD Pipeline-Inspection Robot
Shandong University of Technology · Sep. 2021 – Oct. 2022
Built a D–H kinematic model and simulated pipe-bend tracks in the MATLAB Robotics Toolbox.
[9]
H. Wang*, M. Wang, S. Yu, Y. Wang,
"Design and kinematic simulation of pipeline flaw-detection robot based on ultrasonic time-of-flight diffraction,"
Journal of Machine Design 41, 84–90 (2024). (Advisor as first author)
TOFD pipeline-inspection robot: 3D model, bending-pipe adaptation, and D–H kinematic modeling.
Research Interests
Core research directions in wearable biosensing and non-invasive continuous monitoring.
Non-invasive sampling of interstitial fluid for continuous metabolite monitoring
Reverse iontophoresis (RI) applies a low, alternating-polarity electrical current to the skin to non-invasively extract interstitial fluid (ISF), enabling wearable, reagent-free access to biomarkers such as lactate and glucose without needles.
Alternating-polarity RI to suppress Ag/AgCl extraction-electrode degradation and improve long-term stability.
Optimization of sampling parameters (current density, polarity switching) for reproducible ISF extraction.
Integration of Na⁺ and pH sensing for real-time calibration and drift compensation.
🖼Imageassets/research/drift_correction.png
Impedance-Guided Drift Correction
Long-term stability of hydrogel electrochemical sensors
Hydrogel-based electrochemical sensors suffer from signal drift caused by molecular-interaction-induced structural changes. I develop impedance-driven correction strategies that compensate for this drift, enabling reliable continuous monitoring over extended periods.
Investigated molecular-interaction-induced drift mechanisms in hydrogel sensors.
Proposed an impedance-guided drift-correction framework validated by MD and COMSOL simulations.
Reduced current coefficient of variation by 10.3× (glucose) and 5.4× (H₂O₂).
🖼Imageassets/research/microfabrication.png
Soft Materials & Microfabrication
Screen-printed sensors, hydrogels, and paper-based microfluidics
I design and fabricate wearable sensing platforms from the materials level up: screen-printed electrodes, enzymatic and ionic sensors, hydrogels, and paper-based microfluidic interfaces optimized for biofluid handling.
Screen-printed electrode fabrication for glucose, lactate, Na⁺, and pH sensing.
Enzymatic (LOx, GOx) and ionic sensor development with hydrogel encapsulation.
Geometry-engineered PI–paper microfluidic interfaces for passive biofluid renewal.
🖼Imageassets/research/ml_signal.png
Hardware Design & Machine-Learning Signal Processing
From MCU circuits to MLP/PINN-based physiological signal interpretation
I build the complete hardware and software stack — from MCU and relay-controlled measurement circuits to machine-learning models — to turn raw physiological signals into reliable, clinically meaningful measurements.
MCU and relay-controlled circuits for automated, multiplexed sensor readout.
C/Python signal-acquisition and processing scripts.
Machine-learning models (MLP, PINNs) for PPG-based glucose prediction (99.33% in Clarke zones A&B).
Skills & Projects
Technical skills across electrochemistry, simulation, fabrication, and hardware & programming — and the projects where I applied them.
Leading hydrogel drift-correction and reverse-iontophoresis lactate sensing — combining MD/COMSOL simulation, screen-printed fabrication, and EIS/CA characterization.
🖼Project imageassets/skills/project_isf.png
Sep. 2023 – Jun. 2024
Finger-PPG-Based Blood-Glucose Monitor
Built low-cost PPG glucose-detection hardware and a multi-parameter (MLP) machine-learning model, achieving 99.33% points in Clarke Error Grid zones A&B.
🖼Project imageassets/skills/project_ppg.png
Sep. 2021 – Oct. 2022
Ultrasonic TOFD Pipeline-Inspection Robot
Built a D–H kinematic model and simulated pipe-bend tracks in the MATLAB Robotics Toolbox for a pipeline flaw-detection robot.
🖼Project imageassets/skills/project_robot.png
Contact
I am actively seeking PhD opportunities starting Fall 2027 and welcome inquiries from prospective advisors and research groups.
Mutian Wang
AffiliationSchool of Precision Instrument and Opto-electronics Engineering, Tianjin University
Laboratory of Digital-Intelligent Sensing Chips and Instruments
I conduct my research at the Laboratory of Digital-Intelligent Sensing Chips and Instruments (数智传感芯片及仪器实验室), Tianjin University. The lab focuses on sensing chips, precision instruments, and advanced manufacturing for life-health and micro/nano sensing applications.
