Autocatalytic reduction-assisted synthesis of segmented porous PtTe nanochains for enhancing methanol oxidation reaction

Morphology engineering has been developed as one of the most widely used strategies for improving the performance of electrocatalysts. However, the harsh reaction conditions and cumbersome reaction steps during the nanomaterials synthesis still limit their industrial applications. Herein, one-dimensional (1D) novel-segmented PtTe porous nanochains (PNCs) were successfully synthesized by the template methods assisted by Pt autocatalytic reduction. The PtTe PNCs consist of consecutive mesoporous architectures that provide a large electrochemical surface area (ECSA) and abundant active sites to enhance methanol oxidation reaction (MOR). Furthermore, 1D nanostructure as a robust sustaining frame can maintain a high mass/charge transfer rate in a long-term durability test. After 2,000 cyclic voltammetry (CV) cycles, the ECSA value of PtTe PNCs remained as high as 44.47 m2·gPt–1, which was much larger than that of commercial Pt/C (3.95 m2·gPt–1). The high catalytic activity and durability of PtTe PNCs are also supported by CO stripping test and density functional theory calculation. This autocatalytic reduction-assisted synthesis provides new insights for designing efficient low-dimensional nanocatalysts

The 2018 GaN power electronics roadmap

Gallium nitride (GaN) is a compound semiconductor that has tremendous potential to facilitate economic growth in a semiconductor industry that is silicon-based and currently faced with diminishing returns of performance versus cost of investment. At a material level, its high electric field strength and electron mobility have already shown tremendous potential for high frequency communications and photonic applications. Advances in growth on commercially viable large area substrates are now at the point where power conversion applications of GaN are at the cusp of commercialisation. The future for building on the work described here in ways driven by specific challenges emerging from entirely new markets and applications is very exciting. This collection of GaN technology developments is therefore not itself a road map but a valuable collection of global state-of-the-art GaN research that will inform the next phase of the technology as market driven requirements evolve. First generation production devices are igniting large new markets and applications that can only be achieved using the advantages of higher speed, low specific resistivity and low saturation switching transistors. Major investments are being made by industrial companies in a wide variety of markets exploring the use of the technology in new circuit topologies, packaging solutions and system architectures that are required to achieve and optimise the system advantages offered by GaN transistors. It is this momentum that will drive priorities for the next stages of device research gathered here

Taxation and Enterprise Innovation: Evidence from China’s Value-Added Tax Reform

This article used China as an example to study how tax reform affects the innovative behavior of companies. Our research showed that value-added tax (VAT) reform can affect corporate innovation behavior. On the basis of patent-application data of Chinese enterprises, we used the difference-in-differences framework to study the differences in the performance of Chinese industrial enterprises in patent applications before and after China’s 2009 VAT reform. We demonstrated that China’s VAT reform had a positive impact on corporate innovation; this conclusion is robust. In subsequent research, we demonstrated that the VAT reform promoted corporate innovation by expanding corporate investment in fixed assets and reducing corporate debt ratios; however, due to the Chinese government’s subsidies to corporations and financing constraints, the pecking-order effect of corporate innovation was increased. In addition, the VAT reform had a greater impact on the innovation of export enterprises and non-state-owned enterprises. This research provided insights for emerging countries into formulating innovation-driven sustainable development tax reduction policies

Two-dimensional MXene membranes with biomimetic sub-nanochannels for enhanced cation sieving

Abstract Membranes with high ion permeability and selectivity are of considerable interest for sustainable water treatment, resource extraction and energy storage. Herein, inspired by K+ channel of streptomyces A (KcsA K+), we have constructed cation sieving membranes using MXene nanosheets and Ethylenediaminetetraacetic acid (EDTA) molecules as building blocks. Numerous negatively charged oxygen atoms of EDTA molecules and 6.0 Å two-dimensional (2D) sub-nanochannel of MXene nanosheets enable biomimetic channel size, chemical groups and tunable charge density for the resulting membranes. The membranes show the capability to recognize monovalent/divalent cations, achieving excellent K+/Mg2+ selectivity of 121.2 using mixed salt solution as the feed, which outperforms other reported membranes under similar testing conditions and transcends the current upper limit. Characterization and simulations indicate that the cation recognition effect of EDTA and partial dehydration effects play critical roles in cations selective sieving and increasing the local charge density within the sub-nanochannel significantly improves cation selectivity. Our findings provide a theoretical basis for ions transport in sub-nanochannels and an alternative strategy for design ions separation membranes

Optimization of Sensing and Feedback Control for Vibration/Flutter of Rotating Disk by PZT Actuators via Air Coupled Pressure

In this paper, a feedback control mechanism and its optimization for rotating disk vibration/flutter via changes of air-coupled pressure generated using piezoelectric patch actuators are studied. A thin disk rotates in an enclosure, which is equipped with a feedback control loop consisting of a micro-sensor, a signal processor, a power amplifier, and several piezoelectric (PZT) actuator patches distributed on the cover of the enclosure. The actuator patches are mounted on the inner or the outer surfaces of the enclosure to produce necessary control force required through the airflow around the disk. The control mechanism for rotating disk flutter using enclosure surfaces bonded with sensors and piezoelectric actuators is thoroughly studied through analytical simulations. The sensor output is used to determine the amount of input to the actuator for controlling the response of the disk in a closed loop configuration. The dynamic stability of the disk-enclosure system, together with the feedback control loop, is analyzed as a complex eigenvalue problem, which is solved using Galerkin’s discretization procedure. The results show that the disk flutter can be reduced effectively with proper configurations of the control gain and the phase shift through the actuations of PZT patches. The effectiveness of different feedback control methods in altering system characteristics and system response has been investigated. The control capability, in terms of control gain, phase shift, and especially the physical configuration of actuator patches, are also evaluated by calculating the complex eigenvalues and the maximum displacement produced by the actuators. To achieve a optimal control performance, sizes, positions and shapes of PZT patches used need to be optimized and such optimization has been achieved through numerical simulations

A Spatial–Temporal Depth-Wise Residual Network for Crop Sub-Pixel Mapping from MODIS Images

To address the problem caused by mixed pixels in MODIS images for high-resolution crop mapping, this paper presents a novel spatial–temporal deep learning-based approach for sub-pixel mapping (SPM) of different crop types within mixed pixels from MODIS images. High-resolution cropland data layer (CDL) data were used as ground references. The contributions of this paper are summarized as follows. First, we designed a novel spatial–temporal depth-wise residual network (ST-DRes) model that can simultaneously address both spatial and temporal data in MODIS images in efficient and effective manners for improving SPM accuracy. Second, we systematically compared different ST-DRes architecture variations with fine-tuned parameters for identifying and utilizing the best neural network architecture and hyperparameters. We also compared the proposed method with several classical SPM methods and state-of-the-art (SOTA) deep learning approaches. Third, we evaluated feature importance by comparing model performances with inputs of different satellite-derived metrics and different combinations of reflectance bands in MODIS. Last, we conducted spatial and temporal transfer experiments to evaluate model generalization abilities across different regions and years. Our experiments show that the ST-DRes outperforms the other classical SPM methods and SOTA backbone-based methods, particularly in fragmented categories, with the mean intersection over union (mIoU) of 0.8639 and overall accuracy (OA) of 0.8894 in Sherman County. Experiments in the datasets of transfer areas and transfer years also demonstrate better spatial–temporal generalization capabilities of the proposed method

A Spatial–Temporal Depth-Wise Residual Network for Crop Sub-Pixel Mapping from MODIS Images

To address the problem caused by mixed pixels in MODIS images for high-resolution crop mapping, this paper presents a novel spatial–temporal deep learning-based approach for sub-pixel mapping (SPM) of different crop types within mixed pixels from MODIS images. High-resolution cropland data layer (CDL) data were used as ground references. The contributions of this paper are summarized as follows. First, we designed a novel spatial–temporal depth-wise residual network (ST-DRes) model that can simultaneously address both spatial and temporal data in MODIS images in efficient and effective manners for improving SPM accuracy. Second, we systematically compared different ST-DRes architecture variations with fine-tuned parameters for identifying and utilizing the best neural network architecture and hyperparameters. We also compared the proposed method with several classical SPM methods and state-of-the-art (SOTA) deep learning approaches. Third, we evaluated feature importance by comparing model performances with inputs of different satellite-derived metrics and different combinations of reflectance bands in MODIS. Last, we conducted spatial and temporal transfer experiments to evaluate model generalization abilities across different regions and years. Our experiments show that the ST-DRes outperforms the other classical SPM methods and SOTA backbone-based methods, particularly in fragmented categories, with the mean intersection over union (mIoU) of 0.8639 and overall accuracy (OA) of 0.8894 in Sherman County. Experiments in the datasets of transfer areas and transfer years also demonstrate better spatial–temporal generalization capabilities of the proposed method