Xiaojun Xian

Education

Postdoc, Electrical & Biomedical Engineering, Arizona State University, USA
Research field: Chemical & Bio Sensors, Environmental Sensors, Medical Devices Development

Ph.D., Physical Chemistry (Nano-Chemistry), Peking University, China
Research field: Nanomaterials, Nanofabrication, Nanodevices

B. S., Physical Chemistry, Peking University, China
Research field: Molecular Electronics

Academic Interests

Biosensors, Chemical Sensors, Wearable Sensors, Wearable Healthcare Devices, Mobile Health, Data Science, Advanced Sensing Materials, Nanomaterials

Academic Responsibilities

EE454/554 Biomedical Instruments and Electrical Safety
EE460/560 Sensors and Measurements
EE260 Electronic Materials
EE792 Wearable Sensors and Devices
EE790 Materials Seminar

Committee Activities

EE Graduate Committee
EE Faculty Search Committee

Grants

• 1R43HL123164-01 National Institutes of Health (NIH) 04/16/2014-04/15/2015
Title: Mobile multifunctional tool for monitoring and management of respiratory diseases
Role: Principal Investigator

• 1R43ES025095-01 National Institutes of Health (NIH) 12/01/2014-11/30/2015
Title: A multi-analyte device for air quality monitoring
Role: Principal Investigator

• 2R44HL123164-02 National Institutes of Health (NIH) 04/16/2015-03/31/2016
Title: Mobile multifunctional tool for monitoring and management of respiratory diseases
Role: Principal Investigator

• 1R21EB020868-01 National Institutes of Health (NIH) 08/01/2015–07/31/2017
Title: Non-Invasive Mobile Device for Tracking Cardiovascular Functions
Role: Co-Investigator

• 5R44HL123164-03 National Institutes of Health (NIH) 04/01/2016-03/31/2017
Title: Mobile multifunctional tool for monitoring and management of respiratory diseases
Role: Principal Investigator

•1R44ES029006-01A1 National Institutes of Health (NIH) 07/01/2018-06/30/2019
Title: A badge-like exposure device for occupational safety and epidemiological study
Role: Principal Investigator

• 5R44HL123164-04 National Institutes of Health (NIH) 04/01/2017-03/31/2020
Title: Mobile multifunctional tool for monitoring and management of respiratory diseases
Role: Principal Investigator

• 1U01EB021980-01 National Institutes of Health (NIH) 09/30/2015-06/30/2020
Title: A Personal Exposure and Response Monitoring System for Pediatric Asthma Study
Role: Co-Investigator

• SAIT Research Project Samsung 01/01/2020-06/30/2021
Title: Gradient Based Colorimetric Array Sensor for Detection of Transdermal Biomarkers of Macronutrients Intake and Metabolic diseases
Role: Principal Investigator

• 4R44ES029006-02 National Institutes of Health (NIH) 08/01/2019-07/31/2022
Title: A badge-like exposure device for occupational safety and epidemiological study
Role: Principal Investigator

Patents

1. Zhongfan Liu, Liying Jiao, Xiaojun Xian, Yingying Zhang, Jin Zhang. Method of Axially Modulating the Band Structure of Single-Walled Carbon Nanotube, Chinese patent, No. ZL 2006 1 0113212.6.
2. Zhongfan Liu, Liying Jiao, Xiaojun Xian, Yingying Zhang, Jin Zhang. Method of Integrating Single-Walled Carbon Nanotube Based Devices, Chinese patent, No. ZL 2006 1 0113214.5.
3. Erica S. Forzani, Nongjian Tao, Xiaojun Xian, Francis Tsow, Mouthpiece For Accurate Detection Of Exhaled Nitric Oxide, US Patent, US 9,931,055 B2, 2018.
4. Francis Tsow, Xiaojun Xian, Erica S. Forzani, Nongjian Tao, Portable Metabolic Analyzer System, US Patent, US 10,078,074 B2, 2018.
5. Xiaojun Xian, Devon Bridgeman, Francis Tsow, Erica S. Forzani, Nongjian Tao, Self-Contained Wearable Metabolic Analyzer, International Patent, Application Number PCT/US19/55235, 2019.
6. Erica S. Forzani, Xiaojun Xian, Bhavesh Patel, Kelly McKay, Device and Method for Mitigating Aerosol Release from Nebulization, US Provisional Patent, Application Number: US 63/073,437, 2020.
7. Won Jong Jung, Di Wang, Vishal Varun Tipparaju, Xiaojun Xian, Jingjing Yu, Kak Namkoong, Apparatus for Estimating Concentration of Biomarker, and Electronic System Having the Same, US Provisional Patent, Application Number: US 63/183,870, 2021.

Professional Memberships

Member of American Chemical Society (ACS)
Member of Institute of Electrical and Electronics Engineers (IEEE)
Member of Materials Research Society (MRS)
Topic Editor of Chemosensors
Topic Editor of Biosensors

Work Experience

• 2021 – Present Assistant Professor, EECS Department, South Dakota State University, USA
• 2020 – 2021 Associate Research Professor, The Biodesign Institute, Arizona State University, USA
• 2011 – Present VP of Product & Production, TF Health Co., Tempe, Arizona, USA
• 2017 – 2020 Research Scientist, The Biodesign Institute, Arizona State University, USA
• 2014 – 2016 Associate Research Scientist, The Biodesign Institute, Arizona State University, USA
• 2010 – 2014 Assistant Research Scientist, The Biodesign Institute, Arizona State University, USA
• 2009 – 2010 Postdoc Research Associate, The Biodesign Institute, Arizona State University, USA

Area(s) of Research

• Advanced Sensing Materials

Nano and hybrid sensing materials:
nanomaterials such as nanoparticles, nanowires, nanotubes, graphene, and 2D materials are of high interest because their novel physical and chemical properties make them very useful in many applications including electronics, optics, and sensors. To achieve certain functions, e.g. chemical or biosensing, a bottom-up approach is usually required to engineer the nanomaterials during synthesis, such as assembly, combination, and grafting.

Hierarchical colorimetric sensing materials:
Traditionally, colorimetry is an analytical technique to determine the concentration of colored chemical compounds in solution through measuring the optical absorbance at a certain wavelength. To make the colorimetry compatible with the wearable platform, two important transitions need to be made: 1) from solution phase to solid phase; 2) from 3D configuration to 2D configuration.

• Wearable Sensing Technologies

Flexible thin-film transistor sensing:
Thin-film transistor (TFT) can be used to build flexible sensors for wearables. But conventional thin-film transistors are less tolerant to mechanical deformations such as bending and stretching, which limits their application in wearable electronics. TFT with nano-veneers-like structures can combine the structural continuity and processability of polymers with the high conductivity and functionality of discontinuous nanomaterials. These nano-veneers are particularly attractive in areas of TFT-based flexible sensors fabrication.

Colorimetric array sensing:
The colorimetric array sensing approach is a miniaturized and high-throughput chemical sensing platform for specific and sensitive detection of multiple analytes simultaneously. It configurates the flat LED light source, the sensor cartridge, and the CMOS imager in a tiny “sandwich” structure for multiplex colorimetric sensing.

Micro Quartz tuning fork (MQTF) sensing:
The micro quartz tuning fork (MQTF) based sensing platform can translate the analytes binding events into resonant oscillating frequency shift. It is attractive for developing miniaturized sensors because MQTF is intrinsically tiny, low-cost, and highly sensitive to slight mass change. With the advance of film coating technologies, such as molecularly imprinted polymer coating, the surface of MQTF prongs can be modified to achieve high selectivity to different chemicals.

• Wearable Devices and Data Science

Wearable device design and integration:
Wearable system design and integration requires interdisciplinary knowledge and teamwork. As one category of medical device, wearable healthcare devices development must follow the conventional process: concept phase, development phase, verification and validation phase. One needs to have deep understanding of the needs, the users, the technologies, and the regulations to develop a successful medical device.

Wearable-based data science:
Wearable devices and their networks could capture and record continuous streams of health data about the patient. To make evidence-based clinical decisions, it requires to process large amounts of data to create new insights and build predictive models to guide personalized treatment. The scientific methods in data science offers effective tools for developing algorithms to extract knowledge and insights from the raw and unstructured data generated by wearables.