Sub-10-nm Intracellular Bioelectronic Probes from Nanowire-Nanotube Heterostructures
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CitationFu, Tian-Ming, Xiaojie Duan, Zhe Jiang, Xiaochuan Dai, Ping Xie, Zengguang Cheng, and Charles M. Lieber. 2014. Sub-10-nm Intracellular Bioelectronic Probes from Nanowire-Nanotube Heterostructures. Proceedings of the National Academy of Sciences 111, no. 4: 1259–1264.
AbstractThe miniaturization of bioelectronic intracellular probes with a wide dynamic frequency range can open up opportunities to study biological structures inaccessible by existing methods in a minimally invasive manner. Here, we report the design, fabrication, and demonstration of intracellular bioelectronic devices with probe sizes less than 10 nm. The devices are based on a nanowire–nanotube heterostructure in which a nanowire field-effect transistor detector is synthetically integrated with a nanotube cellular probe. Sub-10-nm nanotube probes were realized by a two-step selective etching approach that reduces the diameter of the nanotube free-end while maintaining a larger diameter at the nanowire detector necessary for mechanical strength and electrical sensitivity. Quasi-static water-gate measurements demonstrated selective device response to solution inside the nanotube, and pulsed measurements together with numerical simulations confirmed the capability to record fast electrophysiological signals. Systematic studies of the probe bandwidth in different ionic concentration solutions revealed the underlying mechanism governing the time response. In addition, the bandwidth effect of phospholipid coatings, which are important for intracellular recording, was investigated and modeled. The robustness of these sub-10-nm bioelectronics probes for intracellular interrogation was verified by optical imaging and recording the transmembrane resting potential of HL-1 cells. These ultrasmall bioelectronic probes enable direct detection of cellular electrical activity with highest spatial resolution achieved to date, and with further integration into larger chip arrays could provide a unique platform for ultra-high-resolution mapping of activity in neural networks and other systems.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12872203
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