Antibody Immobilization in ZnO-Thin Film Transistors for Low-Cost Biosensors Applications

Domínguez, Rafael Antonio Salinas; Hinostroza, Ovier Obregón; Orduña-Díaz, A.; Dominguez, Miguel A.

doi:10.3390/micromachines2021-09587

Cited by 1 publication

(1 citation statement)

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“…As another example, Salinas et al implemented a ZnO TFT biosensor with recyclable plastic substrates for real-time E. coli detection. , However, these TFT-based biosensing methods could not differentiate between live and dead bacteria and did not provide quantification of the CFU concentration of the sample under test.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Detection and Classification of Bacterial Colonies Using a Thin-Film Transistor (TFT) Image Sensor

Liu

Koydemir

et al. 2022

ACS Photonics

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Early detection and identification of pathogenic bacteria such as Escherichia coli (E. coli) is an essential task for public health. The conventional culture-based methods for bacterial colony detection usually take ≥24 h to get the final readout. Here, we demonstrate a bacterial colony-forming-unit (CFU) detection system exploiting a thin-film-transistor (TFT)-based image sensor array that saves ∼12 h compared to the Environmental Protection Agency (EPA)-approved methods. To demonstrate the efficacy of this CFU detection system, a lens-free imaging modality was built using the TFT image sensor with a sample field-of-view of ∼7 cm2. Time-lapse images of bacterial colonies cultured on chromogenic agar plates were automatically collected at 5 min intervals. Two deep neural networks were used to detect and count the growing colonies and identify their species. When blindly tested with 265 colonies of E. coli and other coliform bacteria (i.e., Citrobacter and Klebsiella pneumoniae), our system reached an average CFU detection rate of 97.3% at 9 h of incubation and an average recovery rate of 91.6% at ∼12 h. This TFT-based sensor can be applied to various microbiological detection methods. Due to the large scalability, ultra large field-of-view, and low cost of the TFT-based image sensors, this platform can be integrated with each agar plate to be tested and disposed of after the automated CFU count. The imaging field-of-view of this platform can be cost-effectively increased to >100 cm2 to provide a massive throughput for CFU detection using, e.g., roll-to-roll manufacturing of TFTs, as used in the flexible display industry.

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