Eryilmaz, Merve, Goncharov, Artem, Han, Gyeo-Re, Joung, Hyou-Arm, Ballard, Zachary S, Ghosh, Rajesh, Zhang, Yijie, Di_Carlo, Dino, and Ozcan, Aydogan. Machine learning-powered serology test: A paper-based multiplexed sensor monitors COVID-19 immunity levels. Retrieved from https://par.nsf.gov/biblio/10650386. Web. doi:10.1364/CLEO_SI.2025.SS145_4.

Eryilmaz, Merve, Goncharov, Artem, Han, Gyeo-Re, Joung, Hyou-Arm, Ballard, Zachary S, Ghosh, Rajesh, Zhang, Yijie, Di_Carlo, Dino, & Ozcan, Aydogan. Machine learning-powered serology test: A paper-based multiplexed sensor monitors COVID-19 immunity levels. Retrieved from https://par.nsf.gov/biblio/10650386. https://doi.org/10.1364/CLEO_SI.2025.SS145_4

Eryilmaz, Merve, Goncharov, Artem, Han, Gyeo-Re, Joung, Hyou-Arm, Ballard, Zachary S, Ghosh, Rajesh, Zhang, Yijie, Di_Carlo, Dino, and Ozcan, Aydogan. "Machine learning-powered serology test: A paper-based multiplexed sensor monitors COVID-19 immunity levels". Country unknown/Code not available: Optica Publishing Group. https://doi.org/10.1364/CLEO_SI.2025.SS145_4. https://par.nsf.gov/biblio/10650386.

@article{osti_10650386,
place = {Country unknown/Code not available}, title = {Machine learning-powered serology test: A paper-based multiplexed sensor monitors COVID-19 immunity levels}, url = {https://par.nsf.gov/biblio/10650386}, DOI = {10.1364/CLEO_SI.2025.SS145_4}, abstractNote = {A multiplexed paper-based vertical flow assay integrated with machine learning was developed, achieving 89.5% accuracy in classifying COVID-19 immunity levels of individuals as unprotected,” protected, and infected within 20 minutes.}, journal = {}, publisher = {Optica Publishing Group}, author = {Eryilmaz, Merve and Goncharov, Artem and Han, Gyeo-Re and Joung, Hyou-Arm and Ballard, Zachary S and Ghosh, Rajesh and Zhang, Yijie and Di_Carlo, Dino and Ozcan, Aydogan}, }