Search

Few-layer MoS2 with exceptional physical and chemical properties has attracted notable attention for next-generation gas sensors. The layer-dependent sensing performance for detecting NO2 based on MoS2 is not fully understood. Here, we report the direct synthesis of high-crystallinity uniform few-layer MoS2 via chemical vapor deposition. The influence of layer thickness on the NO2-sensing performance is evaluated. We show that, as compared to the monolayer and trilayer counterparts, the bilayer MoS2 presents the best sensitivity at room temperature. Further density functional theory calculations reveal that the bilayer MoS2 with an AA stacking sequence is more favorable for the physisorption of NO2 molecules due to the more negative adsorption energy, facilitating charge transfer during the NO2 adsorption and thus increasing the electrical response of the gas sensors. This work provides a basic understanding of layer-dependent gas-sensing performance and serves as a reference for designing room-temperature gas sensors for low power consumption and reliable responsivity. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to introduce an innovative approach that uses a decoder with multiple layers to accurately identify Chinese nested entities across various nesting depths. To address potential human intervention, an advanced optimization algorithm is used to fine-tune the decoder based on the depth of nested entities present in the data set. With this approach, this study achieves remarkable performance in recognizing Chinese nested entities. Design/methodology/approach: This study provides a framework for Chinese nested named entity recognition (NER) based on sequence labeling methods. Similar to existing approaches, the framework uses an advanced pre-training model as the backbone to extract semantic features from the text. Then a decoder comprising multiple conditional random field (CRF) algorithms is used to learn the associations between granularity labels. To minimize the need for manual intervention, the Jaya algorithm is used to optimize the number of CRF layers. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance on both Chinese nested NER and flat NER tasks. Findings: The experimental findings illustrate that the proposed methodology can achieve a remarkable 4.32% advancement in nested NER performance on the People's Daily corpus compared to existing models. Originality/value: This study explores a Chinese NER methodology based on the sequence labeling ideology for recognizing sophisticated Chinese nested entities with remarkable accuracy. [ABSTRACT FROM AUTHOR]

This brief disposes the finite-time anti-unwinding trajectory tracking control problem of the autonomous underwater vehicle (AUV) encountering model uncertainties, ocean disturbances and actuator failures. Primarily, the kinematic model of translational and rotational motions is depicted by unit quaternion in lieu of classical Euler angle such that the AUV's dynamics could be globally and uniquely formulated. Subsequently, two finite-time control strategies are presented here to leave the state variables of AUV can converge to an adjustable region. Additionally, the adaptive laws could be applicable to the existence of actuator faults by utilizing a passive fault-tolerant technology. By integrating the initial value of the scalar quaternion into the sliding mode surface, the proposed controllers are characterized with anti-unwinding property. Then, with the application of hyperbolic tangent function, finite-time stability will be achieved for tracking errors without singularity. Stability of the closed-loop system is verified via the Lyapunov theorem. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed controllers. • The unit quaternion is applied for attitude representation of AUVs. • A novel sliding mode surface is proposed for the achievement of finite-time convergence, anti-unwinding and singularity avoidance. • Actuator faults could be handled by a constructive fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]

The freshwater scarcity and inadequate access to clean water globally have rallied tremendous efforts in developing robust technologies for water purification and decontamination, and heterogeneous catalysis is a highly-promising solution. Sub-nanometer-confined reaction is the ultimate frontier of catalytic chemistry, yet it is challenging to form the angstrom channels with distributed atomic catalytic centers within, and to match the internal mass transfer and the reactive species' lifetimes. Here, we resolve these issues by applying the concept of the angstrom-confined catalytic water contaminant degradation to achieve unprecedented reaction rates within 4.6 Å channels of two-dimensional laminate membrane assembled from monolayer cobalt-doped titanium oxide nanosheets. The demonstrated degradation rate constant of the target pollutant ranitidine (1.06 ms−1) is 5–7 orders of magnitude faster compared with the state-of-the-art, achieving the 100% degradation over 100 h continuous operation. This approach is also ~100% effective against diverse water contaminates with a retention time of <30 ms, and the strategy developed can be also extended to other two-dimensional material-assembled membranes. This work paves the way towards the generic angstrom-confined catalysis and unravels the importance of utilizing angstrom-confinement strategy in the design of efficient catalysts for water purification. Subnanometer-confined reaction is the frontier of catalytic chemistry, yet it is challenging to form the angstrom channels with distributed atomic catalytic centers within, and to match the internal mass transfer and the reactive species' lifetimes. Here, the authors resolve these issues by applying the concept of the angstrom-confined catalytic water contaminant degradation. [ABSTRACT FROM AUTHOR]

The critical phase transition temperature (Tc) of Cu 2 Se thermoelectric nanomaterials has been a focal point of extensive research, yet the quantification of surface energy on Tc is frequently ignored. In this paper, we systematically investigate the impact of both the width/thickness and surface configuration of Cu 2 Se nanobridges (NBs) on Tc. We find that the Tc decreases with size reduction, which becomes particularly accelerated when the size decreases to a few nanometers. Additionally, the NBs with higher energy surfaces exhibit lower Tc. Then we propose an optimized thermodynamic model to quantify the combined effect of size and surface energy on Tc in Cu 2 Se NBs, which provides an approach to predict Tc in Cu 2 Se and other thermoelectric nanomaterials. Our study facilitates the understanding of the dependence of Tc on size and surface in Cu 2 Se, with an eye towards the stable room temperature thermoelectric applications of Cu 2 Se nanomaterials. [Display omitted] • The effects of size and surface on the Tc in Cu 2 Se are revealed based on the in-situ transmission electron microscopy. • The Tc of Cu 2 Se nanobridges decreases with size reduction, accelerating significantly when the size drops to a few nanometers. • The Cu 2 Se nanobridges with higher energy surfaces exhibit lower Tc. • A thermodynamic model is proposed to quantify the effect of size and surface configurations on Tc in Cu 2 Se nanobridges. [ABSTRACT FROM AUTHOR]