2018
DOI: 10.1016/j.microrel.2018.09.001
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Towards reliability enhancement of graphene FET biosensor in complex analyte: Artificial neural network approach

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Cited by 16 publications
(14 citation statements)
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“…Accurate biosensor design requires fundamental knowledge on the chemical structure of the target NPs, the concentration levels of the analyte [ 36 ] and its interfering species, the type of matrix, and the type and volume of the sample [ 37 ]. The next step is selecting a biological process that mediates the detection of the target analyte [ 38 ].…”
Section: Biosensors Design For Detection Of Npsmentioning
confidence: 99%
“…Accurate biosensor design requires fundamental knowledge on the chemical structure of the target NPs, the concentration levels of the analyte [ 36 ] and its interfering species, the type of matrix, and the type and volume of the sample [ 37 ]. The next step is selecting a biological process that mediates the detection of the target analyte [ 38 ].…”
Section: Biosensors Design For Detection Of Npsmentioning
confidence: 99%
“…Then a sinusoidal voltage for 60 s has been applied (peak to peak 19.5 V). To improve the adhesion, the graphene deposited SPEs have been heated for 20 min at 75 °C 37 . After that, ZnO seed layer has been grown on graphene deposited SPEs incorporating sol–gel method.…”
Section: Methodsmentioning
confidence: 99%
“…Notably, this repeatability analysis is often limited by the scalability of the measurements and the subjectivity of the experimenters . To this end, some groups employed 50–250 technical replicates based on large arrays of 2D FET sensors, which enables statistically robust analysis of the performance. ,, …”
Section: Prototypical Designmentioning
confidence: 99%