Michaels, M.; Yu, S.-Y.; Zhou, T.; Du, F.; Al Faruque, M.A.; Kulinsky, L.
Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency. Micromachines 2022, 13, 399.
https://doi.org/10.3390/mi13030399
AMA Style
Michaels M, Yu S-Y, Zhou T, Du F, Al Faruque MA, Kulinsky L.
Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency. Micromachines. 2022; 13(3):399.
https://doi.org/10.3390/mi13030399
Chicago/Turabian Style
Michaels, Matthew, Shih-Yuan Yu, Tuo Zhou, Fangzhou Du, Mohammad Abdullah Al Faruque, and Lawrence Kulinsky.
2022. "Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency" Micromachines 13, no. 3: 399.
https://doi.org/10.3390/mi13030399
APA Style
Michaels, M., Yu, S.-Y., Zhou, T., Du, F., Al Faruque, M. A., & Kulinsky, L.
(2022). Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency. Micromachines, 13(3), 399.
https://doi.org/10.3390/mi13030399