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Visible-light-driven CO2 photoreduction to achieve renewable materials, such as syngas, hydrocarbons, and alcohols, is a key process that could relieve environmental problems and the energy crisis simultaneously. Reduction of syngas products with different H2:CO proportions is highly expected to produce high value-added chemicals in the industry. However, the development of technologies employing long-wavelength irradiation to achieve CO2 photoreduction and simultaneous tuning of the resultant H2:CO proportion remains a challenging endeavor. In this work, we carried out interfacial engineering by designing a series of heterostructured layered double-hydroxide/MoS2 nanocomposites via electrostatic self-assembly. The syngas proportion (H2:CO) obtained from CO2 photoreduction could be modulated from 1:1 to 9:1 by visible-light irradiation (λ > 400 nm) under the control of the interface-rich heterostructures. This work provides a cost-effective strategy for solar-to-fuel conversion in an artificial photosynthetic system and describes a novel route to produce syngas with targeted proportions.

A heterostructured CoAl-layered double hydroxide/MoS2 nanocomposite photocatalyst (CoAl-LDH/MoS2) for CO2 photoreduction was prepared by simple electrostatic interactions. The syngas ratio (H2 : CO) was precisely tuned from 1.3 : 1 to 15 : 1 by altering only the catalyst concentration in the photocatalytic CO2 reduction system under visible light (λ > 400 nm). Interestingly, a rather high evolution rate can be obtained from CO2 photoreduction to CO up to 4575 μmol g−1 h−1 even under irradiation above 500 nm.

Because of the continuous improvement of technology, mechanization has emerged in various fields. Due to the different suitable seasons for the growth of agricultural plants, agricultural mechanization faces problems different from other industries. That is, agricultural machinery and equipment may be used frequently for a period of time, or may be idle for a long time. This leads to the aging of equipment no longer becoming regular, the maintenance time of spare parts is not fixed, the number of spare parts stored in the spare parts warehouse cannot be too large to occupy funds, and the number cannot be too small to meet the maintenance needs, so the prediction of agricultural machinery spare parts has become particularly important. Due to the lack of information, the difficulty of labeling, and the imbalance of positive and negative sample classification, this paper used a semi-supervised learning algorithm to solve the problem of agricultural machinery spare parts data classification. In order to forecast the demand for spare parts of agricultural machinery, this paper compared the IPSO-BP neural network algorithm and BP neural network algorithm. It was found that the IPSO-BP neural network was used to forecast the demand for spare parts of agricultural machinery, and the error between the predicted value and the actual value was small and met the accuracy requirements.

In view of the centralized operation, high failure rate and large number of harvesters involved in the cross-regional operation of combine harvesters, which has led to a surge in maintenance service demand and a lack of effective maintenance service systems, in order to be able to quickly solve problems arising from failures during the process of cross-regional operation, an operation and maintenance (O&M) service platform for the cross-regional operation of combine harvesters was designed in this research on the basis of data resources, supported by the computing power of a big data platform and centered on an artificial intelligence algorithm. Meeting the demand for maintenance service during cross-regional operation, we built a system platform integrating service order management, maintenance service activity management, and maintenance service resource management, and a technical algorithm for operation and maintenance service resource allocation and service path optimization was developed in order to achieve service function modularization and intelligent monitoring, while early warning and display were realized using multi-dimensional platforms such as a PC, a control screen, and a mobile App. This platform was able to solve problems arising when harvesters break down, maintenance service can be carried out quickly when traditional resource information is blocked and the demand for the service is difficult to meet. The reduction in cost and the increased efficiency for agricultural machinery enterprises was also achieved, while the problem of ensuring continued service was systematically solved during the process of cross-regional operation. Finally, the performance of the software architecture and the effect of path optimization were verified. The results showed that the platform system developed using the three-layer C/S architecture offered more stable characteristics, and the path optimization in the platform system was better able to reduce the maintenance time and distance, thus making it possible to realize the dynamic on-demand configuration and scheduling management of cross-region job service resources.

A heterostructured CoAl-layered double hydroxide/MoS2 nanocomposite photocatalyst (CoAl-LDH/MoS2) for CO2 photoreduction was prepared by simple electrostatic interactions. The syngas ratio (H2 : CO) was precisely tuned from 1.3 : 1 to 15 : 1 by altering only the catalyst concentration in the photocatalytic CO2 reduction system under visible light (λ400 nm). Interestingly, a rather high evolution rate can be obtained from CO2 photoreduction to CO up to 4575 μmol g-1 h-1 even under irradiation above 500 nm.