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Abstract The Asian ladybeetle, Coccinella septempunctata, is an important insect of predatory natural enemy, which has a strong control effect and application prospects for aphids, whiteflies, mealybugs, and other small-sized pests of agriculture and forestry crops. Gut microbiota composition posed impacts on development of insects. In order to clarify the effect of artificial feed feeding on the intestinal microbial species and structure of C. septempunctata, we compared the intestinal microbial flora of C. septempunctata reared on bean aphids and artificial food for 15 days. Results show that Proteobacteria was the dominant component in all groups at phylum level, Rhodobacter, Methylovigula, Burkholderia, and Bradyrhizobium were the dominant bacteria among all groups at genus level. As to the differences in bacterial community structure and diversity, there is no significant difference between Shannon index and Simpson index, the principal components analysis of the bacterial communities, and the samples were roughly distributed in different regions. After 15 days of feeding, artificial diet did not significantly reduce the microbial diversity of the gut of C. septempunctata compared to the aphid group, and there was no significant effect on the abundance of dominant flora in the gut of C. septempunctata, C. septempunctata gut has a similar core microbiota. This study clarifies the effects in intestinal microbial diversity and composition structure of the C. septempunctata with artificial diet, and provides a theoretical basis for understanding the intestinal microorganisms and optimizating the artificial diet of C. septempunctata.

Abstract Small-scale distributed water treatment equipment such as sequencing batch reactor (SBR) is widely used in the field of rural domestic sewage treatment because of its advantages of rapid installation and construction, low operation cost and strong adaptability. However, due to the characteristics of non-linearity and hysteresis in SBR process, it is difficult to construct the simulation model of wastewater treatment. In this study, a methodology was developed using artificial intelligence and automatic control system that can save energy corresponding to reduce carbon emissions. The methodology leverages random forest model to determine a suitable soft sensor for the prediction of COD trends. This study uses pH and temperature sensors as premises for COD sensors. In the proposed method, data were pre-processed into 12 input variables and top 7 variables were selected as the variables of the optimized model. Cycle ended by the artificial intelligence and automatic control system instead of by fixed time control that was an uncontrolled scenario. In 12 test cases, percentage of COD removal is about 91. 075% while 24. 25% time or energy was saved from an average perspective. This proposed soft sensor selection methodology can be applied in field of rural domestic sewage treatment with advantages of time and energy saving. Time-saving results in increasing treatment capacity and energy-saving represents low carbon technology. The proposed methodology provides a framework for investigating ways to reduce costs associated with data collection by replacing costly and unreliable sensors with affordable and reliable alternatives. By adopting this approach, energy conservation can be maintained while meeting emission standards.

Abstract A simple, rapid and eco-friendly approach based on matrix solid-phase dispersion microextraction (MSPDM) followed by ultrahigh performance liquid chromatography coupled with electrochemical detection (UHPLC-ECD) was presented for the microextraction and determination of six phenolic acids in a plant preparation (Danshen tablets). The parameters that influenced the extraction performance of phenolic acids were investigated and optimized. The optimal MSPDM conditions were determined as follows: sorbent, using graphene nanoplatelets with sample/sorbent ratio of 1:1, grinding time set at 60 s, and 0.2 mL of water as elution solvent. Under the optimum conditions, the validation experiments indicated that the proposed method exhibited good linearity (r2 ≥ 0.9991), excellent precision (RSD ≤ 4.57%), and satisfactory recoveries (82.34–98.34%). The limits of detection were from 1.19 to 4.62 ng/mL for six phenolic acids. Compared with other reported methods, this proposal required less sample, solvent and extraction time. Consequently, the proposed method was successfully used to the extraction and determination of phenolic acids in Danshen tablets.

Two-dimensional (2D) materials and their corresponding van der Waals heterostructures have drawn tremendous interest due to their extraordinary electrical and optoelectronic properties. Insulating 2D hexagonal boron nitride (h-BN) with an atomically smooth surface has been widely used as a passivation layer to improve carrier transport for other 2D materials, especially for Transition Metal Dichalcogenides (TMDCs). However, heat flow at the interface between TMDCs and h-BN, which will play an important role in thermal management of various electronic and optoelectronic devices, is not yet understood. In this paper, for the first time, the interface thermal conductance (G) at the MoS2/h-BN interface is measured by Raman spectroscopy, and the room-temperature value is (17.0 ± 0.4) MW · m−2K−1. For comparison, G between graphene and h-BN is also measured, with a value of (52.2 ± 2.1) MW · m−2K−1. Non-equilibrium Green's function (NEGF) calculations, from which the phonon transmission spectrum can be obtained, show that the lower G at the MoS2/h-BN interface is due to the weaker cross-plane transmission of phonon modes compared to graphene/h-BN. This study demonstrates that the MoS2/h-BN interface limits cross-plane heat dissipation, and thereby could impact the design and applications of 2D devices while considering critical thermal management. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) materials and their corresponding van der Waals heterostructures have drawn tremendous interest due to their extraordinary electrical and optoelectronic properties. Insulating 2D hexagonal boron nitride (h-BN) with an atomically smooth surface has been widely used as a passivation layer to improve carrier transport for other 2D materials, especially for Transition Metal Dichalcogenides (TMDCs). However, heat flow at the interface between TMDCs and h-BN, which will play an important role in thermal management of various electronic and optoelectronic devices, is not yet understood. In this paper, for the first time, the interface thermal conductance (G) at the MoS 2 /h-BN interface is measured by Raman spectroscopy, and the room-temperature value is (17.0 ± 0.4) MW · m -2 K -1 . For comparison, G between graphene and h-BN is also measured, with a value of (52.2 ± 2.1) MW · m -2 K -1 . Non-equilibrium Green's function (NEGF) calculations, from which the phonon transmission spectrum can be obtained, show that the lower G at the MoS 2 /h-BN interface is due to the weaker cross-plane transmission of phonon modes compared to graphene/h-BN. This study demonstrates that the MoS 2 /h-BN interface limits cross-plane heat dissipation, and thereby could impact the design and applications of 2D devices while considering critical thermal management.