University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Ultrasensitive 2D Bi2O2Se Phototransistors on Silicon Substrates

Abstract: 2D materials are considered as intriguing building blocks for next-generation optoelectronic devices. However, their photoresponse performance still needs to be improved for practical applications. Here, ultrasensitive 2D phototransistors are reported employing chemical vapor deposition (CVD)-grown 2D Bi2 O2 Se transferred onto silicon substrates with a noncorrosive transfer method. The as-transferred Bi2 O2 Se preserves high quality in contrast to the serious quality degradation in hydrofluoric-acid-assisted transfer. The phototransistors show a responsivity of 3.5 × 104 A W-1 , a photoconductive gain of more than 104 , and a time response in the order of sub-millisecond. With back gating of the silicon substrate, the dark current can be reduced to several pA. This yields an ultrahigh sensitivity with a specific detectivity of 9.0 × 1013 Jones, which is one of the highest values among 2D material photodetectors and two orders of magnitude higher than that of other CVD-grown 2D materials. The high performance of the phototransistor shown here together with the developed unique transfer technique are promising for the development of novel 2D-material-based optoelectronic applications as well as integrating with state-of-the-art silicon photonic and electronic technologies.

Wafer-scale synthesis of monolayer WS2 for high-performance flexible photodetectors by enhanced chemical vapor deposition

Abstract: Two-dimensional (2D) nanomaterials have recently attracted considerable attention due to their promising applications in next-generation electronics and optoelectronics. In particular, the large-scale synthesis of high-quality 2D materials is an essential requirement for their practical applications. Herein, we demonstrate the wafer-scale synthesis of highly crystalline and homogeneous monolayer WS2 by an enhanced chemical vapor deposition (CVD) approach, in which precise control of the precursor vapor pressure can be effectively achieved in a multi-temperature zone horizontal furnace. In contrast to conventional synthesis methods, the obtained monolayer WS2 has excellent uniformity both in terms of crystallinity and morphology across the entire substrate wafer grown (e.g., 2 inches in diameter), as corroborated by the detailed characterization. When incorporated in typical rigid photodetectors, the monolayer WS2 leads to a respectable photodetection performance, with a responsivity of 0.52 mA/W, a detectivity of 4.9 × 109 Jones, and a fast response speed (< 560 μs). Moreover, once fabricated as flexible photodetectors on polyimide, the monolayer WS2 leads to a responsivity of up to 5 mA/W. Importantly, the photocurrent maintains 89% of its initial value even after 3,000 bending cycles. These results highlight the versatility of the present technique, which allows its applications in larger substrates, as well as the excellent mechanical flexibility and robustness of the CVD-grown, homogenous WS2 monolayers, which can promote the development of advanced flexible optoelectronic devices.

Maximum Density Divergence for Domain Adaptation

Abstract: Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence (“match” in ATM) and maximizes the intra-class density (“tight” in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.

Muscle Fibers Inspired High-Performance Piezoelectric Textiles for Wearable Physiological Monitoring

Abstract: The next-generation wearable biosensors with highly biocompatible, stretchable, and robust features are expected to enable the change of the current reactive and disease-centric healthcare system to a personalized model with a focus on disease prevention and health promotion. Herein, a muscle-fiberinspired nonwoven piezoelectric textile with tunable mechanical properties for wearable physiological monitoring is developed. To mimic the muscle fibers, polydopamine (PDA) is dispersed into the electrospun barium titanate/polyvinylidene fluoride (BTO/PVDF) nanofibers to enhance the interfacial-adhesion, mechanical strength, and piezoelectric properties. Such improvements are both experimentally observed via mechanical characterization and theoretically verified by the phase-field simulation. Taking the PDA@BTO/PVDF nanofibers as the building blocks, a nonwoven light-weight piezoelectric textile is fabricated, which hold an outstanding sensitivity (3.95 V N−1) and long-term stability (<3% decline after 7,400 cycles). The piezoelectric textile demonstrates multiple potential applications, including pulse wave measurement, human motion monitoring, and active voice recognition. By creatively mimicking the muscle fibers, this work paves a cost-effective way to develop high-performance and self-powered wearable bioelectronics for personalized healthcare.