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On the other hand, the accessible multiple oxidation states of reactive Cu elements (Cu SP 0 sp , Cu SP I sp , Cu SP II sp , and Cu SP III sp ) within Cu-based catalysts can enable targeted catalytic reactions that proceed via both single- and multi-electron transfer. Dang et al. [[5]] synthesized self-supported Cu/Cu SB 3 sb P nanoarrays through a low-temperature phosphidation process, which were directly grown on the surfaces of Cu nanosheets from Cu/CuO nanoarrays precursor. Compared with the Cu(111) surface, the Cu-skin Cu SB 3 sb Au(111) surface showed higher stability of O SB 2 sb adsorption and a lower dissociation energy barrier of the adsorbed O SB 2 sb . [Extracted from the article]

Currently, green hydrogen harvesting from electrochemical water splitting is overly reliant on noble metals with high catalytic activity but low natural abundance and high cost. As one of potential earth‐abundant alternatives, copper, however, displays a poor hydrogen evolving capability, mainly due to inherently a weak hydrogen binding energy (HBE). It poses a substantial restriction to metallic Cu alone serving as primary active sites for high‐performance hydrogen evolution. Thanks to the presence of hydrogen spillover that was often overlooked during the electrocatalysis, the hydrogen evolution in a nonacidic electrolyte can be accelerated by the recombination desorption of adsorbed hydrogen over Cu with a weak HBE. Here, we concisely highlighted this promotion effect of secondary active Cu on electrocatalytic hydrogen evolution and eagerly expected to extend hydrogen spillover effects to the advance of multi‐site electrocatalytic systems. [ABSTRACT FROM AUTHOR]

The cytochrome P450 (CYP450) monooxygenase superfamily, which is involved in the biosynthesis pathways of many primary and secondary metabolites, plays prominent roles in plant growth and development. However, systemic information about CYP450s in Brassica napus (BnCYP450) was previously undiscovered and their biological significance are far from understood. Members of clan 86 CYP450s, such as CYP704Bs, are essential for the formation of pollen exine in plant male reproduction, and the targeted mutagenesis of CYP704B genes has been used to create new male sterile lines in many crops. In the present study, a total of 687 BnCYP450 genes were identified in Brassica napus cultivar "Zhongshuang 11" (ZS11), which has nearly 2.8-fold as many CYP450 members as in Arabidopsis thaliana. It is rationally estimated since Brassica napus is a tetraploid oil plant with a larger genome compared with Arabidopsis thaliana. The BnCYP450 genes were divided into 47 subfamilies and clustered into nine clans. Phylogenetic relationship analysis reveals that CYP86 clan consists of four subfamilies and 109 BnCYP450s. Members of CYP86 clan genes display specific expression profiles in different tissues and in response to ABA and abiotic stresses. Two BnCYP450s within the CYP704 subfamily from CYP86 clan, BnCYP704B1a and BnCYP704B1b, display high similarity to MS26 (Male Sterility 26, also known as CYP704B1). These two BnCYP704B1 genes were specifically expressed in young buds. We then simultaneously knocked-out these two BnCYP704B1 genes through a clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) genome engineering system. The edited plants displayed a pollenless, sterile phenotype in mature anthers, suggesting that we successfully reproduced genic male sterility (GMS, also known as nuclear male sterility) lines in Brassica napus. This study provides a systemic view of BnCYP450s and offers a strategy to facilitate the commercial utility of the CRISPR/Cas9 system for the rapid generation of GMS in rapeseed via knocking-out GMS controlling genes. [ABSTRACT FROM AUTHOR]

Organic electronics is a rising field, with novel applications including but not limited to stretchable solar cells, flexible display screens, and biosensors. The high performance of these organic electronics is enabled by the outstanding optoelectronic and thermomechanical features of organic semiconducting materials. However, the production of the promising organic semiconducting materials at industrial scales has not yet become feasible, due to huge energy and capital costs in the large-scale synthesis as well as the potential damage to the environment and human health caused by vast hazardous chemical waste released. This review summarizes recent research advances in improving the environmental friendliness of the organic semiconducting material synthesis by appying atom economical C–H functionalization-based synthetic routes, minimizing hazardous chemical waste, lowering the energy consumption, and employing safe and abundant chemicals including naturally sourced semiconducting building blocks. This review showcases the remarkable progress that has been made towards the environmentally friendly organic semiconductor synthesis and provides insight for researchers developing green synthetic strategies and organic semiconductor building blocks in the future. [ABSTRACT FROM AUTHOR]

Owing to the high abundance, good conductivity and excellent tolerance to harsh environment, carbon host materials have recently attracted considerable research interest in the fields of electrochemical hydrogen evolution reaction (HER). However, the deficiency of intrinsic active sites within the carbon host materials substantially gives rise to an inferior HER performance. In this work, atomically dispersed ruthenium active sites are deliberately introduced into the carbon host structure by controlled pyrolysis of Ru-doped ZIF-8. With atomic Ru sites on a unique whisker-like secondary microstructure and a favorable porous texture, the optimal product exhibits a high intrinsic activity as well as robust durability, which especially outperforms the Pt/C benchmarking in alkaline media. A combination of control experiments and theoretical calculations demonstrates that atomically dispersed Ru sites within the carbon host matrix serve as the dominant catalytically active sites, and remarkably optimize the free energy of water molecule dissociation during the Volmer step, thus boosting the HER performance. [ABSTRACT FROM AUTHOR]

Accurate estimates of plant transpiration (T sf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (r c), the Penman-Monteith (PM) model is commonly used in T sf modeling. However, the r c is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the r c of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environmental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (R n), and air temperature (T a), and of soil water content (θ a) on the accuracy of JA sub-models to calculate apple tree r c and T sf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008–2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate r c. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of T sf estimates with five empirical r c models, combined with the PM model. The results showed that sub-model JA 3322 , i.e., the third constraint for VPD, the third constraint for R n , the second constraint for T a , and the second constraint for θ a attained the best estimate of r c with a coefficient of determination (R2 = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m-1), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage were 0.78, 0.72, 1203.3 s m-1 and 0.99, and the values for the dense canopy stage were 0.78, 0.77, 445.6 s m-1 and 0.97, respectively. Segmented JA models based on the leaf area index threshold significantly improved the accuracy of r c estimation, where the median R2 and NSE were improved by 7.1 % and 12.4 % in the partial canopy stage and by 12.2 % and 13.4 % in the dense canopy stage. Despite pointing out the best environmental constraint functions of the JA model in the different growth stages, results indicated that the MEA yielded the most accurate estimates of r c , followed by QPSO, GA, and LSM. Moreover, the JA model with environmental constraints was the most accurate method to estimate the apple tree T sf , and MEA was the most suitable parameter optimization algorithm. Overall, the findings of this study provide accurate actual water consumption information of apple trees using easily accessible meteorological data for the effective day-to-day water management decision-making of rain-fed apple tree orchards on the Loess Plateau of China previously. • Jarvis was superior to other empirical models for canopy resistance estimation. • Jarvis segmented by canopy growth stage gave a more accurate estimation. • Finding the most appropriate Jarvis model for apple tree canopy resistance. • Intelligence optimization algorithms improved the accuracy of the Jarvis models. [ABSTRACT FROM AUTHOR]

Quantitatively characterizing and accurately predicting plant transpiration are of great significance, but directly measuring transpiration is impractical, time-consuming, and labor-intensive. This study compared the transpiration estimation performance of multiple linear regression (MLR), modified Jarvis–Stewart (MJS), and Shuttleworth–Wallace (S-W) with deep belief network (DBN), long short-term memory recurrent neural network (LSTM-RNN), and LSTM-RNN improved with multiple restricted Boltzmann machines (R- L -RNN) using 31 input combinations comprising complete subsets of Vapor pressure deficit (VPD), Net solar radiation (R n), Average air temperature (T a), Soil water content (SWC), and Leaf area index (LAI) observations collected at Wuwei, Changwu and Taigu stations on the Loess Plateau in China. The results showed that R- L -RNN obtained the most accurate estimations in the partial canopy stage, dense canopy stage, and whole growth stage, compared to MLR, MJS, S-W, DBN, and LSTM-RNN. The accuracy of the deep learning models (DNN) increased exponentially as the number of input variables increased, and the importance of the input variables followed the orders of: LAI > VPD > R n > T a > SWC in the partial and whole canopy stage, and VPD > R n > T a > LAI > SWC in the dense canopy stage. The apple tree transpiration models were more accurate in the partial and dense canopy stages than the whole growth stage. The coefficient of determination and Nash-Sutcliffe efficiency coefficient for the R- L -RNN model increased by 8.1–13.1% and 11.2–25.4% in the partial canopy stage, respectively, and by 2.6–6.9% and 14.7–20.1% in the dense canopy stage, whereas the relative root mean square error decreased by 8.7–28.6% and 17.3–38.2%. Overall, R- L -RNN is the most recommended model for estimating the apple tree transpiration, because it is such a simple method that agricultural water managers can easily determine the water consumption of apple trees using limited accessible observational data. • A hybrid deep learning model provided most accurate apple tree transpiration model. • Importance of different inputs to deep learning models differed with growth stages. • Accuracy of deep learning models improved exponentially with more inputs. • The transpiration models were more accurate in the partial and dense canopy stages. [ABSTRACT FROM AUTHOR]

In this work, we report a facile strategy for the preparation of polypyrrole/platinum nanocomposite (PPy/Pt nanocomposite), namely small and surface-clean Pt nanoparticles (Pt NPs) densely dispersed on polypyrrole nanoparticles (PPy NPs) by a simple, ultrafast, and microwave-assisted polyol process. The as-made nanocomposite was demonstrated to possess a good electrocatalytic activity toward the reduction of H 2 O 2 by obviously enhancing the detection sensitivity and decreasing H 2 O 2 reduction overpotential at −0.175 V (vs. Ag/AgCl). Accordingly, a selective, stable, repeatable and reproducible non-enzymatic electrochemical sensor of H 2 O 2 based on the nanocomposite was constructed. [ABSTRACT FROM AUTHOR]