TY - GEN
T1 - Multiplexed molecular biomarker analysis using an expanded library of nanoelectronically barcoded particles enabled through machine learning analysis
AU - Sui, Jianye
AU - Xie, Pengfei
AU - Lin, Zhongtian
AU - Javanmard, Mehdi
N1 - Funding Information:
This work was supported by National Science Foundation Award No. IDBR 1556253.
Funding Information:
We wish to thank Dr. Yicheng Lu and Robert Lorber from Microelectronics Research Laboratory (MERL) for the help in sensor fabrication. This work was supported by National Science Foundation Award No. IDBR 1556253.
PY - 2018/4/24
Y1 - 2018/4/24
N2 - Electronically barcoded micro-particles have been demonstrated for use in various multiplexed molecular biomarker assays. Traditional optical and plasmonic methods for barcoding are capable of high throughput and high sensitivity, but require bulky instrumentation for readout, which cannot be easily made into a portable device. Previously, we reported a novel impedance based barcoding technique by fabricating tunable nano-capacitors on micro-particle surfaces thus modulating the overall particle impedance. In this work, we expand the library of barcoded particles using atomic layer deposited oxides of varying thickness and dielectric permittivity and study the effect of thickness and dielectric permittivity using multi-frequency impedance flow cytometry and utilize machine learning to classify different particle barcodes.
AB - Electronically barcoded micro-particles have been demonstrated for use in various multiplexed molecular biomarker assays. Traditional optical and plasmonic methods for barcoding are capable of high throughput and high sensitivity, but require bulky instrumentation for readout, which cannot be easily made into a portable device. Previously, we reported a novel impedance based barcoding technique by fabricating tunable nano-capacitors on micro-particle surfaces thus modulating the overall particle impedance. In this work, we expand the library of barcoded particles using atomic layer deposited oxides of varying thickness and dielectric permittivity and study the effect of thickness and dielectric permittivity using multi-frequency impedance flow cytometry and utilize machine learning to classify different particle barcodes.
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U2 - 10.1109/MEMSYS.2018.8346584
DO - 10.1109/MEMSYS.2018.8346584
M3 - Conference contribution
AN - SCOPUS:85046993842
T3 - Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS)
SP - 444
EP - 447
BT - 2018 IEEE Micro Electro Mechanical Systems, MEMS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2018
Y2 - 21 January 2018 through 25 January 2018
ER -