Copper-Bridge-Enhanced p-Band Center Modulation of Carbon-Bismuth Heterojunction for CO2 Electroreduction.

Bismuth-based catalysts have advanced CO2 electroreduction to formic acid, but their intrinsic electronic structure remains a key obstacle to achieving a high catalytic performance. Herein, a copper bridge strategy is proposed to enhance electronic modulation effects in bismuth/carbon composites. Density functional theory calculations prove the novel p-d-p hybrid orbitals on the carbon-copper-bismuth heterojunction structure (Bi-Cu/HMCS) could stabilize the HCOO* intermediate and lower the thermodynamic barrier from CO2 to formic acid. With the rapid electron-supplying effect of "copper bridge", the faradaic efficiency of formate reaches 100% (±2%) at a low overpotential of 500 mV and remains above 90% within a wide potential range. Using a solid-state electrolyte device, pure 0.6 M HCOOH is produced at a stable current density of 100 mA cm-2 within 7.5 h, boasting an impressive energy efficiency of 53.8%. This work offers a new strategy for optimizing electronic structure of metal/carbon composite electrocatalysts.

Research on deep learning garbage classification system based on fusion of image classification and object detection classification.

With the development of national economy, the output of waste is also increasing. People's living standards are constantly improving, and the problem of garbage pollution is increasingly serious, which has a great impact on the environment. Garbage classification and processing has become the focus of today. This topic studies the garbage classification system based on deep learning convolutional neural network, which integrates the garbage classification and recognition methods of image classification and object detection. First, the data sets and data labels used are made, and then the garbage classification data are trained and tested through ResNet and MobileNetV2 algorithms, Three algorithms of YOLOv5 family are used to train and test garbage object data. Finally, five research results of garbage classification are merged. Through consensus voting algorithm, the recognition rate of image classification is improved to 2%. Practice has proved that the recognition rate of garbage image classification has been increased to about 98%, and it has been transplanted to the raspberry pie microcomputer to achieve ideal results.

Engineering adjacent N, P and S active sites on hierarchical porous carbon nanoshells for superior oxygen reduction reaction and rechargeable Zn-air batteries.

Heteroatom-doped carbon materials have been regarded as sustainable alternatives to the noble-metal catalysts for oxygen reduction reaction (ORR), while the catalytic performances still remain unsatisfactory. Herein, we develop a metal-free adjacent N, P and S-codoped hierarchical porous carbon nanoshells (NPS-HPCNs) through a novel layer-by-layer template coating method. The NPS-HPCNs is rationally fabricated by crosslinking of polyethyenemine (PEI) and phytic acid (PA) on nano-SiO2 template surface and subsequently coating of viscous sulfur-bearing petroleum pitch, followed by pyrolysis and alkaline etching. Soft X-ray absorption near-edge spectroscopy (XANES) analysis and density functional theory (DFT) calculations prove the engineering of adjacent N, P and S atoms to generate synergistic and reinforced active sites for oxygen electrocatalysis. The NPS-HPCNs manifests excellent ORR activity with a half-wave potential (E1/2) of 0.86 V, as well as promoted durability and methanol tolerance in alkaline medium. Remarkably, the NPS-HPCNs-based Zn-air battery delivers an open-circuit voltage of 1.479 V, a considerable peak power density of 206 mW cm-2 and robust cycling stability (over 200 h), even exceeding the commercial Pt/C catalyst. This study offers fundamental insights into the construction and synergistic mechanism of adjacent heteroatoms on carbon substrate, providing advanced metal-free electrocatalysts for Zn-air batteries and other energy conversion and storage devices.

Lightweight blockchain fuzzy decision scheme through MQTT and Fibonacci for sustainable transport.

The unprecedented progress in field of IoT enabled rapid developments in the vehicle intelligent transportation systems and most of these provide services in a centralized way. However, the centralized system architecture is vulnerable to the external attacks as a result both information and equipment are prone to eavesdropping and destruction. Therefore, there is a trend to apply blockchain technology to the vehicle intelligent transportation systems in order to achieve sustainable transportation. Nevertheless, the system is so great and very sophisticated and the ultimate task will be harder to implement. In view of this, an attempt is made in this paper to propose a lightweight fuzzy decision blockchain scheme through MQTT and Fibonacci, and through this scheme, the extent of blockchain server can be scaled and easy to deploy. Also through MQTT, reliable communication and transmission of blockchain can be realized. LF-BC is formed by using DH and Fibonacci transformation to enhance security, and F-PBFT consensus algorithm can reduce the communication overhead and improve the fault tolerance tremendously. Using LF-BC scheme, the experimental results show that the fault tolerance rate is significantly improved by 22.3%, and the sustainable safety and reliability of the vehicle intelligent transportation system is increased consumedly. At the same time, the feasibility of the scheme is also verified by taking specific cases.

Detection system of dead and sick chickens in large scale farms based on artificial intelligence.

With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face of this problem, there are many solutions to identify dead and sick chickens through sound and image, but they can not achieve the ideal effect. In this paper, a sensor detection method based on artificial intelligence is proposed. This method 1) The maximum displacement of chicken activity is measured by fastening a foot ring on each chicken, and the three-dimensional total variance is designed and calculated to represent the chicken activity intensity. 2) The detection terminal collects the sensing data of foot ring through ZigBee network. 3) The state of chicken (dead chicken and sick chicken) can be identified by machine learning algorithm. This method of artificial intelligence combined with sensor network not only has high recognition rate, but also can reduce the operation cost. The practical results show that the accuracy of the system to identify dead and sick chickens is 95.6%, and the cost of the system running for 4 years can be reduced by 25% compared with manual operation.

Robust NiCoP/CoP Heterostructures for Highly Efficient Hydrogen Evolution Electrocatalysis in Alkaline Solution.

Electrocatalytic hydrogen evolution reaction, the cornerstone of the emerging hydrogen economy, can be essentially facilitated by robustly heterostructural electrocatalysts. Herein, we report a highly active and stably heterostructural electrocatalyst consisting of NiCoP nanowires decorated with CoP nanoparticles on a nickel foam (NiCoP-CoP/NF) for effective hydrogen evolution. The CoP nanoparticles are strongly interfaced with NiCoP nanowires producing abundant electrocatalytically active sites. Combined with the integrated catalyst design, NiCoP-CoP/NF affords a remarkable hydrogen evolution performance in terms of high activity, enhanced kinetics, and outstanding durability in an alkaline electrolyte, superior to most of the Co (or Ni)-phosphide-based catalysts reported previously. Density functional theory calculations demonstrate that there is an interfacial effect between NiCoP and CoP, which allows a preferable hydrogen adsorption and thus contributes to the significantly enhanced performance. Furthermore, an electrolyzer employing NiCoP-CoP/NF as the cathode and RuO2/NF as the anode (NiCoP-CoP/NF||RuO2/NF) exhibits excellent water-splitting activity and outstanding durability, which is comparable to that of the benchmark Pt-C/NF||RuO2/NF electrolyzer.

Different CdSeTe structure determined photoelectrocatalytic reduction performance for carbon dioxide.

CdSeTe nanoparticles (CdSeTe NPs)/TiO2 nanotubes (TiO2 NTs) and CdSeTe nanosheets (CdSeTe NSs)/TiO2 NTs were fabricated by photoelectric deposition method and hydrothermal method, respectively. The band gap of the CdSeTe NPs/TiO2 NTs (1.24eV) is narrower than that of the CdSeTe NSs/TiO2NTs (1.48eV). The photoelectrochemical conversion efficiency of the CdSeTe NPs/TiO2 NTs is 2.72 times larger than that of the CdSeTe NSs/TiO2 NTs, manifesting that the CdSeTe NPs/TiO2 NTs possessed more excellent photocatalytic performance. Furthermore, the current density of electrocatalytic reduction CO2 on the CdSeTe NPs/TiO2 NTs was 2.47 times larger than that of the CdSeTe NSs/TiO2 NTs, showing the excellent electrocatalytic performance of the CdSeTe NPs/TiO2 NTs. The major product methanol was detected in the PEC reduction process on the two as-prepared materials. The yield reached 18.57mmolL-1 after 300min on CdSeTe NPs/TiO2NTs, which is 16 times larger than that of CdSeTe NSs/TiO2 NTs.

One dimensional SnO2 NRs/Fe2O3 NTs with dual synergistic effects for photoelectrocatalytic reduction CO2 into methanol.

The hydrothermal method was explored to prepare SnO2 nanorods (SnO2 NRs) with the special faces of (110) and (101) on the surface of Fe2O3 nanotubes (Fe2O3 NTs). According to the SEM and XRD results, the formation process of the hierarchically assembled SnO2 NRs was deduced. The SnO2 NRs/Fe2O3 NTs catalyst that had reached for 120 mins behaved the best photoelectrocatalytic properties. From the view of photocatalytic reduction, the conduction band (-0.75eV vs NHE) is negative enough to drive CO2 reduction, and the valence band (1.82eV) is positive enough to oxidize H2O to generate proton, and then the proton is used for CO2 reduction. From the electrocatalytic reduction point, the net CO2 reduction current density of the composite is 7.48 times that of Fe2O3 NTs at -1.1V, indicating that the electrocatalytic performance of Fe2O3 NTs is greatly enhanced by the introduction of 6-fold branched SnO2 NRs. The predominant reduction product is analyzed by GC was methanol. Herein, two synergistic effects are proved according to the methanol yields, one is the synergistic effect of the photocatalytic and electrocatalytic reduction, the other is the synergistic effect between SnO2 NRs and Fe2O3 NTs. The results indicated that the composite catalyst behaves excellent photoelectrocatalytic activity for CO2 reduction.

Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo.

Global tractography estimates brain connectivity by determining the optimal configuration of signal-generating fiber segments that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. That is, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its both ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that updating of independent fiber segments can be done concurrently. The experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or improve tractography performance.