Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency.

Michaels M, Yu SY, Zhou T, Du F, Al Faruque MA, Kulinsky L

DOI: 10.3390/mi13030399
Published: 2022 Feb 28
Container: Micromachines
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/mi13030399,
  title = {Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency.},
  author = {Michaels M and Yu SY and Zhou T and Du F and Al Faruque MA and Kulinsky L},
  year = {2022},
  journal = {Micromachines},
  doi = {10.3390/mi13030399},
  url = {https://doi.org/10.3390/mi13030399}
}

RIS

TY  - JOUR
TI  - Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency.
AU  - Michaels M
AU  - Yu SY
AU  - Zhou T
AU  - Du F
AU  - Al Faruque MA
AU  - Kulinsky L
PY  - 2022
JO  - Micromachines
DO  - 10.3390/mi13030399
UR  - https://doi.org/10.3390/mi13030399
ER  -

APA

M, M., SY, Y., T, Z., F, D., MA, A. F., & L, K. (2022). Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency.. Micromachines. https://doi.org/10.3390/mi13030399

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pubmed · retrieved 2026-09-25T08:25:58.171Z