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Effective and robust electrocatalysts are mainly based on innovative materials and unique structures. Herein, we designed a flakelike cobalt phosphide-based catalyst supporting on NiCo2O4 nanorods array, which in-situ grew on the nickel foam (NF) current collector, referring as N–Co2P/NiCo2O4/NF electrode. By optimizing the microstructure and electronic structure through 3D hierarchy fabrication and nitrogen doping, the catalyst features with abundant electrochemical surface area, favorable surface wettability, excellent electron transport, as well as tailored d band center. Consequently, the as-prepared N–Co2P/NiCo2O4/NF electrode exhibits an impressive HER activity with a low overpotentials of 58 mV at 10 mA cm−2, a Tafel slop of 75 mV dec−1, as well as superior durability in alkaline medium. This work may provide a new pathway to effectively improve the hydrogen evolution performance of transition metal phosphides and to develop promising electrodes for practical electrocatalysis.

Transcriptome profiles of the red- and white-fleshed apples in an F1 segregating population of Malus sieversii f.Niedzwetzkyana and M.domestica 'Fuji' were generated using the next-generation high-throughput RNA sequencing (RNA-Seq) technology and compared. A total of 114 differentially expressed genes (DEGs) were obtained, of which 88 were up-regulated and 26 were down-regulated in red-fleshed apples. The 88 up-regulated genes were enriched with those related to flavonoid biosynthetic process and stress responses. Further analysis identified 22 genes associated with flavonoid biosynthetic process and 68 genes that may be related to stress responses. Furthermore, the expression of 20 up-regulated candidate genes (10 related to flavonoid biosynthesis, two encoding MYB transcription factors and eight related to stress responses) and 10 down-regulated genes were validated by quantitative real-time PCR. After exploring the possible regulatory network, we speculated that flavonoid metabolism might be involved in stress responses in red-fleshed apple. Our findings provide a theoretical basis for further enriching gene resources associated with flavonoid synthesis and stress responses of fruit trees and for breeding elite apples with high flavonoid content and/or increased stress tolerances.

Apple is one of the most economically important horticultural fruit crops worldwide. It is critical to gain insights into fruit ripening and softening to improve apple fruit quality and extend shelf life. In this study, forward and reverse suppression subtractive hybridization libraries were generated from 'Taishanzaoxia' apple fruits sampled around the ethylene climacteric to isolate ripening- and softening-related genes. A set of 648 unigenes were derived from sequence alignment and cluster assembly of 918 expressed sequence tags. According to gene ontology functional classification, 390 out of 443 unigenes (88%) were assigned to the biological process category, 356 unigenes (80%) were classified in the molecular function category, and 381 unigenes (86%) were allocated to the cellular component category. A total of 26 unigenes differentially expressed during fruit development period were analyzed by quantitative RT-PCR. These genes were involved in cell wall modification, anthocyanin biosynthesis, aroma production, stress response, metabolism, transcription, or were non-annotated. Some genes associated with cell wall modification, anthocyanin biosynthesis and aroma production were up-regulated and significantly correlated with ethylene production, suggesting that fruit texture, coloration and aroma may be regulated by ethylene in 'Taishanzaoxia'. Some of the identified unigenes associated with fruit ripening and softening have not been characterized in public databases. The results contribute to an improved characterization of changes in gene expression during apple fruit ripening and softening.

'Taishanzaoxia' fruit rapid softening and dehiscence during ripening stage and this process is very sensitive to endogenous ethylene. In this study, we cloned five ethylene signal transcription factors (ZMdEIL1, ZMdEIL2, ZMdEIL3, ZMdERF1 and ZMdERF2) and one functional gene, ZMdPG1, encoding polygalacturonase that could loose the cell connection which associated with fruit firmness decrease and fruit dehiscence to illustrate the reasons for this specific fruit phenotypic and physiological changes. Expression analysis showed that ZMdERF1 and ZMdEIL2 transcription were more abundant in 'Taishanzaoxia' softening fruit and dehiscent fruit and their expression was inhibited by an ethylene inhibitor 1-methylcyclopropene. Therefore, ZMdERF1 and ZMdEIL2 expression were responses to endogenous ethylene and associated with fruit softening and dehiscence. ZMdPG1 expression was induced when fruit softening and dehiscence but this induction can be blocked by 1-MCP, indicating that ZMdPG1 was essential for fruit softening and dehiscence and its expression was mediated by the endogenously occurred ethylene. ZMdPG1 overexpression in Arabidopsis led to silique early dehiscence while suppressing ZMdPG1 expression by antisense ZMdPG1 prevented silique naturally opening. The result also suggested that ZMdPG1 related with the connection between cells that contributed to fruit softening and dehiscence. ZMdERF1 was more closely related with ethylene signaling but it was not directly regulated the ZMdPG1, which might be regulated by the synergic pattern of ethylene transcription factors because of both the ZMdERF1 and ZMdERF2 could interact with ZMdEIL2.

Designing an amine modified silica derived from coal fly ash (CFA) towards direct air CO2 capture (DAC) provides an economic way to manage increasing atmospheric CO2 concentration utilizing solid wastes. In this work, rod-like SBA-15 (SBA-15-R) and wheat-like SBA-15 (SBA-15-W) with distinct pore lengths are synthesized from CFA by controlling the synthesis conditions and then modified by PEI via the wet impregnation method. Their CO2 adsorption behaviors under sub-ambient conditions are investigated to explore their feasibility under aggressive conditions. The adsorbents with various silica morphologies exhibit distinctive CO2 adsorption performance under ambient and sub-ambient conditions. At 35°C under dry conditions, the CO2 adsorption capacity increases with amine loading. The CO2 adsorption capacity (qe) of SBA-15-W-PEI with long channels reaches the highest qe of 1.92 mmol/g with 70 wt.% PEI loading due to a large amount of strong chemisorption sites. Under the sub-ambient conditions (-20°C, 400 ppm), SBA-15-R-PEI30 with short channels having weaker CO2 diffusion resistance exhibits promising CO2 uptake of 0.83 mmol/g. These findings indicate that the ultra-dilute CO2 adsorption at ambient temperature requires an adsorbent with large amounts of adsorption sites, whereas the adsorbents for cold temperature application should reconcile amine loading with CO2 diffusion resistance inside pores. The co-adsorption of H2O and CO2 significantly improves CO2 capacity from 0.83 mmol/g and 1.92 mmol/g to 1.81 mmol/g and 3.48 mmol/g for SBA-15-R-PEI30 (-20°C, 400 ppm) and SBA-15-W-PEI70 (35°C, 400 ppm), respectively. Furthermore, the CFA derived SBA-15-PEI shows good cyclic stability during five consecutive TSA cycles. Preparing the amine silica adsorbents from coal fly ash potentially reduces the adsorbent cost of DAC devices. Moreover, the systematic exploration of amine silica adsorbents at a wide range of temperatures guides the design of high-performance CO2 adsorbents employed under various climatic conditions.

Frequent agricultural activities in farmland ecosystems bring challenges to crop information extraction from remote sensing (RS) imagery. The accurate spatiotemporal information of crops serves for regional decision support and ecological assessment, such as disaster monitoring and carbon sequestration. Most traditional machine learning algorithms are not appropriate for prediction classification due to the lack of historical ground samples and poor model transfer capabilities. Therefore, a transferable learning model including spatiotemporal capability was developed based on the UNet++ model by integrating feature fusion and upsampling of small samples for Sentinel-2A imagery. Classification experiments were conducted for 10 categories from 2019 to 2021 in Xinxiang City, Henan Province. The feature fusion and upsampling methods improved the performance of the UNet++ model, showing lower joint loss and higher mean intersection over union (mIoU) values. Compared with the UNet, DeepLab V3+, and the pyramid scene parsing network (PSPNet), the improved UNet++ model exhibits the best performance, with a joint loss of 0.432 and a mIoU of 0.871. Moreover, the overall accuracy and macro F1 values of prediction classification results based on the UNet++ model are higher than 83% and 58%, respectively. Based on the reclassification rules, about 3.48% of the farmland was damaged in 2021 due to continuous precipitation. The carbon sequestration of five crops (including corn, peanuts, soybean, rice, and other crops) is estimated, with a total carbon sequestration of 2460.56, 2549.16, and 1814.07 thousand tons in 2019, 2020, and 2021, respectively. The classification accuracy indicates that the improved model exhibits a better feature extraction and transferable learning capability in complex agricultural areas. This study provides a strategy for RS semantic segmentation and carbon sequestration estimation of crops based on a deep learning network.