Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors

Hyun-June Jang; Hyou-Arm Joung; Artem Goncharov; Anastasia Gant Kanegusuku; Clarence W. Chan; Kiang-Teck Jerry Yeo; Wen Zhuang; Aydogan Ozcan; Junhong Chen

doi:10.1021/acsnano.4c02897

journal article·2024-09-10

Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors

Hyun-June Jang, Hyou-Arm Joung, Artem Goncharov, Anastasia Gant Kanegusuku, Clarence W. Chan, Kiang-Teck Jerry Yeo, Wen Zhuang, Aydogan Ozcan, Junhong Chen

ACS Nano· Volume 18· Issue 36

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Abstract

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of <6.46% and a decent concentration measurement correlation with an r 2 value of >0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies have the potential to advance FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.

Source: openalex_inverted_index · Quality: baseline

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Electronic materials

OpenAlex topics: Biosensors and Analytical Detection · Advanced Chemical Sensor Technologies · Advanced Sensor and Energy Harvesting Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States

Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, Illinois 60439, United States

Department of Electrical and Computer Engineering, University of California, Los Angeles, California 90095, United States

Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois 60153, United States

Department of Pathology, The University of Chicago, Chicago, Illinois 60637, United States

Pritzker School of Medicine, The University of Chicago, Chicago, Illinois 60637, United States

Department of Bioengineering, University of California, Los Angeles, California 90095, United States

California NanoSystems Institute (CNSI), University of California, Los Angeles, California 90095, United States

Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States

Funding

Directorate for Engineering · University of Chicago

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