Flexible Solid-Electrolyte-Gated-Dielectric Carbon Nanotube Thin Film Transistors and Integrated Circuits with the Recorded Radiation Tolerance and Reparability.

Radiation-tolerance and repairable flexible transistors and integrated circuits (ICs) with low power consumption have become hot topics due to their wide applications in outer space, nuclear power plants, and X-ray imaging. Here, we designed and developed novel flexible semiconducting single-walled carbon nanotube (sc-SWCNT) thin-film transistors (TFTs) and ICs. Sc-SWCNT solid-electrolyte-gate dielectric (SEGD) TFTs showcase symmetric ambipolar characteristics with flat-band voltages (VFB) of ∼0 V, high ION/IOFF ratios (>105), and the recorded irradiation resistance (up to 22 Mrad). Moreover, flexible sc-SWCNT ICs, including CMOS-like inverters and NAND and NOR logic gates, have excellent operating characteristics with low power consumption (≤8.4 pW) and excellent irradiation resistance. Significantly, sc-SWCNT SEGD TFTs and ICs after radiation with a total irradiation dose (TID) ≥ 11 Mrad can be repaired after thermal heating at 100 °C. These outstanding characteristics are attributed to the designed device structures and key core materials including SEGD and sc-SWCNT.

High-sensitivity and energy-efficient chloride ion sensors based on flexible printed carbon nanotube thin-film transistors for wearable electronics.

The advent of flexible single-walled carbon nanotube thin-film transistors (SWCNT-TFTs) has transformed electronics, providing significant benefits like low operating voltage, reduced power consumption, cost-effectiveness, and improved signal amplification. This study focuses on leveraging these attributes to develop a novel flexible high-sensitivity and energy-efficient chloride ion sensors based on printed flexible SWCNT-TFTs utilizing polymers-sorted semiconducting SWCNTs (sc-SWCNTs) as the active layers and ion liquids-poly(4-vinylphenol as dielectric layers along with the evaporated deposition of aluminum electrodes and printed silver electrodes as the gate and source-drain electrodes, respectively. The sensors exhibit several operational advantages, including low voltage requirements (≤1 V), rapid response speed (5.32 s), significant signal amplification (Up to 702.6 %), low power consumption (0.31 µJ at 1 mmol chloride ion), good repeatability, high sensitivity for both low and high concentrations of chloride ion (up to 100 mmol/L) and excellent mechanical flexibility (No obvious changes after bending for 10,000 times with a 5 mm radius). The detection mechanism of chloride ions was analyzed using X-ray Photoelectron Spectroscopy (XPS). It was found that chloride ions react with silver nanoparticles (AgNPs) to form silver chloride (AgCl) on printed electrodes, impeding carrier transport and reducing the currents in SWCNT TFTs. Importantly, our sensors' compatibility with smart devices allows for real-time monitoring of chloride ion levels in human sweat, offering significant potential for daily health monitoring.

Photoprogrammed Multifunctional Optoelectronic Synaptic Transistor Arrays Based on Photosensitive Polymer-Sorted Semiconducting Single-Walled Carbon Nanotubes for Image Recognition.

The development of neuromorphic optoelectronic systems opens up the possibility of the next generation of artificial vision. In this work, the novel broadband (from 365 to 940 nm) and multilevel storage optoelectronic synaptic thin-film transistor (TFT) arrays are reported using the photosensitive conjugated polymer (poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-(bithiophene)], F8T2) sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) as channel materials. The broadband synaptic responses are inherited to absorption from both photosensitive F8T2 and sorted sc-SWCNTs, and the excellent optoelectronic synaptic behaviors with 200 linearly increasing conductance states and long retention time > 103 s are attributed to the superior charge trapping at the AlOx dielectric layer grown by atomic layer deposition. Furthermore, the synaptic TFTs can achieve IOn/IOff ratios up to 106 and optoelectronic synaptic plasticity with the low power consumption (59 aJ per single pulse), which can simulate not only basic biological synaptic functions but also optical write and electrical erase, multilevel storage, and image recognition. Further, a novel Spiking Neural Network algorithm based on hardware characteristics is designed for the recognition task of Caltech 101 dataset and multiple features of the images are successfully extracted with higher accuracy (97.92%) of the recognition task from the multi-frequency curves of the optoelectronic synaptic devices.