Publications

A new single nanowire based nanosensor is demonstrated for illustrating its ultrahigh sensitivity for gas sensing. The device is composed of a single ZnO nanowire mounted on Pt electrodes with one end in Ohmic contact and the other end in Schottky contact. The Schottky contact functions as a "gate" that controls the current flowing through the entire system. By tuning the Schottky barrier height through the responsive variation of the surface chemisorbed gases and the amplification role played by the nanowire to Schottky barrier effect, an ultrahigh sensitivity of 32,000% was achieved using the Schottky contacted device operated in reverse bias mode at 275 degrees C for detection of 400 ppm CO, which is 4 orders of magnitude higher than that obtained using an Ohmic contact device under the same conditions. In addition, the response time and reset time have been shortened by a factor of 7. The methodology and principle illustrated in the paper present a new sensing mechanism that can be readily and extensively applied to other gas sensing systems.

Background: The aim of the present study was to investigate the impact of time in therapeutic range TTR on long-term outcomes of living kidney transplants. Methods: We included 1,241 living kidney transplants and randomized them into development and validation cohorts with a ratio of 2:1. The tacrolimus TTR percentage was calculated by linear interpolation with a target range (5-10 ng/ml months 0-3, 4-8 ng/ml months 4-12). The optimal TTR cutoff was estimated by the receiver operating characteristic curve analysis on the basis of acute rejection (AR) within 12 months in the development cohort. Outcomes were analyzed between patients with high TTR and low TTR in the development and validation cohorts, respectively. The TTR was also compared with other tacrolimus measures. Results: The optimal TTR cutoff value was 78%. In the development cohort, patients with TTR > 78% had significantly higher rejection- and infection-free survival. TTR < 78% was an independent risk factor for AR (OR: 2.97, 95%CI: 1.82-4.84) and infection (OR: 1.55, 95%CI: 1.08-2.22). Patient and graft survival were significantly higher in those with TTR>78%, and TTR<78% was associated with graft loss (OR: 3.2, 95%CI: 1.38-7.42) and patient death (OR: 6.54, 95%CI: 1.34-31.77). These findings were confirmed in the validation cohort. Furthermore, we divided all included patients into a high and low TTR group. TTR was more strongly associated with patient and graft survival than mean level, standard deviation, and intrapatient variability (IPV). Conclusions: Increasing the TTR of tacrolimus in the first year was associated with improved long-term outcomes in living kidney transplants, and TTR may be a novel valuable strategy to monitor tacrolimus exposure.

Human-computer interaction (HCI) strategies communicate the human mind and machine intelligence based on different devices and technologies. The majority of HCI strategies assume normal physical conditions that limit accessibility for users with disabilities. Certain products, such as Braille keyboards, work fine for people with specific disabilities. However, a more general HCI strategy that can neglect users' physical conditions would enhance the accessibility of these tools for disabled persons. Here, we report an HCI strategy that utilises triboelectricity of the human body (TEHB) for HCI. The TEHB can be generated by many parts of the human body, eliminating the obstacles imposed by physical function disabilities. Such an HCI approach has been used for text inputs, graphical inputs, and mimicked mouse functions. With the assistance of deep learning, an accuracy of approximately 98.4 % is achieved for text inputs obtained directly from handwriting. Our findings provide a new approach for HCI and demonstrate the feasibility of multiple interaction modes.

Abstract The characteristics of low current and high output impedance of triboelectric nanogenerators (TENGs) constrain their development and application. Recently, corresponding energy management methods have been proposed to solve the problems. Among them, the switch plays an important role in energy management to improve output performance and reduce output impedance. Therefore, a self‐triggering switch controlled by TENGs is proposed for power management to achieve high‐performance output. It is comprised of an electronic logic switch and the triggering TENG to release the energy stored by the energy‐TENG. The experimental results demonstrate that the self‐triggering switch can adjust the duty cycle with the assistance of the inherent load characteristics of the TENG. In addition, the coupling output of the two TENGs increases the single‐cycle peak current by 137 times (from 32 µA to 4.32 mA) in the vertical contact‐separation mode and 5284 times (from 1.3 µA to 6.87 mA) in the horizontal contact mode. In application, the commercial sensor can be powered, and six 100 W lamps in parallel can be lit. The coupling output method of switch‐TENGs and energy‐TENGs provides important guidance for the development of power management and a new approach toward practical applications.

'Plasmonic Structures for Solar Energy Harvesting' published in 'Encyclopedia of Nanotechnology'

Polyacrylamide hydrogel electrolytes with highly concentrated salt solutions have been developed for solid-state supercapacitors of slow self-discharge. When the supercapacitors are charged by a triboelectric nanogenerator, a much enhanced charging efficiency has been obtained.

'Piezoelectric MEMS Switches' published in 'Encyclopedia of Nanotechnology'

Robotic perception can have simple and effective sensing functions that are unreachable for humans using only the isolated tactile perception method, with the assistance of a triboelectric nanogenerator (TENG). However, the reliability of triboelectric sensors remains a major challenge due to the inherent environmental limitations. Here, an intelligent tactile sensing system that combines a TENG and deep-learning technology is proposed. Using a triboelectric triple tactile sensor array, typical characteristics of each testing material can be maintained stably even under different contact conditions (touch conditions and external environmental conditions) by extracting features from three independent electrical signals as well as the normalized output signals. Furthermore, a convolutional neural network model is integrated, and a high accuracy of 96.62% is achieved in a material identification task. The tactile sensing system is exhibited to an open environment for material identification and the real-time demonstration. Compared to the complex process that humans must integrate multiple sensing (touching and viewing) to accomplish tactile perception, the proposed sensing system shows a huge advantage in cognitive learning for the visually impaired, biomimetic prosthetics, and virtual spaces construction.