Our academic footprint is firmly rooted in the Food-Energy-Water nexus. With a focus on improving the safety of food and agricultural systems, our research program will develop and apply advanced and innovative Bioengineering and Biosensing approaches in solving the critical and emerging issues in food safety engineering, ensuring enough better quality and safer foods for the world’s population. Our research interests span from molecular-level microorganism editing to engineering-level smart packaging design. We are currently focusing on the following specific areas:


During the detection of target pathogens in food samples, recognition elements are required to recognize target bacteria cells. Antibodies and aptamers are the most commonly used elements in the bioanalytical system to capture bacteria cells for enumeration and identification. Unfortunately, their disadvantages (e.g. relatively high cost and inconsistencies from batch to batch) have led to research towards alternative recognition elements. Unlike the conventional antibodies and aptamers, bacteriophages (also named phages, bacteria-infecting viruses) are relatively easy and inexpensive to synthesize and purify. As part of my research platform, my research will explore phage-based biosensors to rapidly detect pathogenic bacteria in the food and agricultural system.

Selected References:

(1) J. Chen, R.A. Picard, D. Wang, V.M. Rotello, and S.R. Nugen, “Lyophilized Engineered Phages for Escherichia coli Detection in Food Matrices”, ACS Sensors 2017, 2(11), 1573-1577.

(2) J. Chen, S.D. Alcaine,  A.A. Jackson, V.M. Rotello, S.R. Nugen, “Colorimetric Detection and Antibiotic Resistance Profiling of Escherichia coli Using Enzymatic Engineering Bacteriophage”, ACS sens., 2017, 2(4), 484-489.

(3) D. Wang, J. Chen, S.R. Nugen, “Electrochemical Detection of Escherichia coli from Aqueous Samples Using Engineered Phages”Anal. Chem. 2017, 89(3), 1650-1657.


Each year,~10 point mutations occur in bacterial genome. Consequently, the point mutants could increase the bacteria antibiotic-resistant ability, alter the bacteria virulence, or produce unknown bacteria toxins. The traditional DNA detection method (quantitative PCR) would fail to detect point mutation in bacteria genome. Although next-generation sequencing (NGS) technology is highly sensitive and selective for the detection of a point mutation, it requires an extremely long-time frame (2-3 weeks) to obtain sequencing information. Also, expensive instrumentation and skilled operators are required to conduct such complicated experiments. These limitations of the conventional methods (qPCR and NGS) make it impossible to quickly detect DNA point mutation. As part of my research platform, I will develop and apply CRISPR-based biosensors to detect point mutation in bacteria gene.

Selected References:

(1) J. Chen, F. Jiang, C-W, Huang, and L. Lin, “Rapid genotypic antibiotic susceptibility test using CRISPR-Cas12a for urinary tract infection”, Analyst. 2020, 145, 5226-5231.

(2) Q. He, D. Yu, M. Bao, G. Korensky, J. Chen, M. Shin, J. Kim, M. Park, P. Qin, and K. Du, “High-throughput and all-solution phase African swine fever virus (ASFV) detection using CRISPR-Cas12a and fluorescence-based point-of-care system”, Biosensors & Bioelectronics. 2020, 154, 112068.


A practical and efficient mean to separate biological and chemical contaminants remains the bottleneck for biosensor-based detection in food system. Without an effective separation method, the need to rapidly and sensitively detect food contaminants in the food and agricultural samples will go unmet. The conventional separation methods (e.g., centrifugation, filtration, and chromatography) require extended periods of time and may not be suitable for the complexed food samples. Thus, there exists a need to separate these contaminant analytes in a manner which will facilitate both analyte identification and enumeration. In recent decades, nanomaterials (less than 100 nm) have attracted wide attention due to their unique physical and chemical properties. To improve early detection of food contaminants, my research will explore the utilization of innovative nanoscale magnetic biosensors for the separation and detection of food contaminants. 

Selected References:

(1) J. Chen, A.A. Jackson, V.M. Rotello, S.R. Nugen, “Colorimetric Detection of Escherichia Coli Based on the Enzyme-Induced Metallization of Gold Nanorods”, small 2016, 12, 18, 2469-2475.

(2) J. Chen, S. Pang, L. He, S.R. Nugen, “Highly Sensitive and Selective Detection of Nitrite Ions Using Fe3O4@Au Magnetic Nanoparticles by Surface-Enhanced Raman Spectroscopy”, Biosens. Bioelectron. 2016, 85, 726-733.


Rare bacteria cells can be difficult to detect and analyze because they either occur in low numbers or affect by other compounds in food or environmental samples. Due to the low infectious does of pathogenic microorganisms, their identification and analysis are still important. To overcome this issue, the traditional method is to enrich the cells to a high enough density to provide a significant signal. However, this approach increases the detection time, for example, Mycobacterium tuberculosis requires three to four weeks’ enrichment to a high cell density. It is impossible to investigate the behavior of a single bacteria cell using ​traditional methods. To meet these challenges, my research program will explore the use of microfluidic device as a mean to separate a single bacteria cell into emulsion droplets for detection.

Selected References:

(1) J. Chen, Y. Zhou, D. Wang, F. He, V.M. Rotello, K.R. Carter, J.J. Watkins, S.R. Nugen, “UV-Nanoimprint Lithography as a Tool to Develop Flexible Microfluidic Devices for Electrochemical Detection”, Lab Chip, 2015, 15, 3086-3094.

(2) J. Chen, Y. Li, K. Huang, P. Wang, L. He, K.R. Carter, S.R. Nugen, “Nanoimprint Patterned Pillar Substrates for Surface-enhanced Raman Scattering Application”, ACS Appl. Mater. Interfaces 2015, 7, 22106-22113.

Internet-of-Things (IoTs)

Due to chemical reaction and microorganism growth, food is a dynamic system. In order to determine the food quality and safety, one-time or interval timing sampling is required. However, this sampling has difficulty to precisely represent real-time food safety and quality. What is worse, when food recall happens due to microbial contamination, it is time- and cost-consuming to trace back to the contamination source. Currently, although temperature sensors have been installed and used to remotely monitor food conditions in the food supply chain, it cannot reflect the real-time information about food safety and quality. Thus, it is significant to develop a technology that can monitor food safety and quality remotely. To meet the challenges, my research program will explore the Internet-of-Things (IoTs) and Computing Engineering (Artificial Intelligence) to address food safety and quality in the food supply chain.

Selected References:

(1) Z. Yu, D. Jung, S. Park, Y. Hu, K. Huang, B.A. Rasco, S. Wang, J. Ronholm, X. Lu, and J. Chen*, “Smart traceability for food safety”, Critical Reviews in Food Science & Nutrition. 2020, 1-12.