Xiaojun Yang, NanoSensor Researcher
Saxion University of Applied Sciences | Enschede, Netherlands
Hardware development and applied research at Saxion University (2022 - 2025)
Electrochemical sensing platform for particle characterization
Developing a portable electrochemical system for sizing particles from 300 nm to 5 μm using ring ultra-microelectrodes and single-entity electrochemistry.
Funding:
Dutch SIA KIEM-GoChem (2024-2025) | €40,000
Applications:
Environmental contamination detection platform
Molecular Imprinted Polymer (MIP) functionalized electrodes for sensitive PFAS detection in water and environmental samples.
Technology:
Electrochemical detection with artificial recognition elements
Target:
SERS-based bacterial spore detection in milk
Surface-Enhanced Raman Scattering (SERS) platform for rapid detection of bacterial spores in dairy products.
Funding:
Dutch SIA KIEM-GoChem (2025-2026) | €40,000
Innovation:
Biosensor technology readiness enhancement
Collaboration with startup ECsens to enhance the technology readiness level (TRL) of electrochemical biosensor platforms.
Partner:
ECsens (Startup)
Focus:
Affordable, high-performance sensing platforms for research and development
Low-cost, high-performance electrochemical measurement platform. Portable and versatile for field applications.
MIP-functionalized screen-printed electrodes for specific molecule detection. Artificial recognition elements.
Complete development kit for wearable biosensors. Sweat analysis and continuous monitoring applications.
Cutting-edge sensing methodologies
Ring ultra-microelectrode (UME) technology enabling precise detection and sizing of individual micro- and nanoparticles.
Key Features:
Patent pending: "Electrochemical affinity biosensor for label-free detection using particle collision"
View PosterMolecularly Imprinted Polymers (MIPs) provide affordable, stable artificial recognition elements for selective molecule detection.
Applications:
Integrated with €100 potentiostat for affordable dev-kits
View PosterDeep learning applications for biosensor signal processing
CNN-Based Electrochemical Signal Classifier
Deep learning framework for analyzing electrochemical sensor signals. The system classifies particles by size (1μm, 2μm, 3μm) using collision signal data from single-entity electrochemistry experiments, achieving 80% validation accuracy.
Performance: ResNet1D achieves 80% validation accuracy with ~29 second training time and 9.61ms inference latency
License: MIT License - Open source for academic research
Research contributions to the field
Ring Ultramicroelectrodes for Current-Blockade Particle-Impact Electrochemistry
Taghi Moazzenzade, Tieme Walstra, Xiaojun Yang, Jurriaan Huskens, and Serge G. Lemay
Analytical Chemistry 2022, 94 (28), 10168-10174
DOI: 10.1021/acs.analchem.2c01503 →Self-Induced Convection at Microelectrodes: Its Influence on Impact Electrochemistry
Taghi Moazzenzade, Xiaojun Yang, Luc Walterbos, Jurriaan Huskens, Christophe Renault, and Serge G. Lemay
Journal of the American Chemical Society 2020, 142 (42), 17908-17912
DOI: 10.1021/jacs.0c08450 →Sensing Micro-/Nanoparticles on Nano-ring Electrodes
Poster presentation at the 2024 Dutch Micro-Nano Conference
Xiaojun Yang, Saxion University of Applied Sciences
View PosterOpen to partnerships in biosensor development, applied nanotechnology, and point-of-care diagnostics
Response time: typically within 48 hours
M.Sc. Applied Nanotechnology
Saxion University (2018-2020)
M.Sc. Mechanical Engineering
Shanghai Jiao Tong University (2012-2015)
HarvardX MCB63X
Biochemistry (2023)
Saxion University (2022-2025)
Biosensor researcher
University of Twente (2020-2021)
Internship researcher
Machine Learning for Data Analysis
CNN-based signal processing
Or email directly: support@seenano.nl