BACKGROUND Alkali metals are considered important chemical promoters used in Fischer–Tropsch synthesis (FTS) iron‐based catalysts. The purpose of this study is to distinguish the relative effects of alkali metals on iron based FTS catalysts. RESULTS It is found that Li and Na promoters can easily diffuse into/out the bulk of catalysts while K, Rb and Cs mostly concentrate on the catalyst surface. Alkalis also inhibited H2 adsorption, improved CO adsorption and dissociation, and suppressed the hydrogenation of carbon species on the surfaces of iron or iron carbides. Li and Na decreased the FTS activity while K, Rb and Cs largely increased the FTS activity of iron catalysts. The selectivities to methane and alkane were decreased while those to olefin and heavier hydrocarbons were increased by alkalis. Among these alkalis, potassium showed an optimal promotional effect on FTS performances. CONCLUSION The distributions of alkalis in catalysts were quite different owing to their atomic radii. The majority of Li and Na promoters diffused into the bulk of catalysts while K, Rb and Cs mostly concentrated on the catalyst surface. The selectivity of methane exhibits a parabola‐like trend with increasing atomic number of alkalis and reaches a minimum at KFeSi catalyst. © 2016 Society of Chemical Industry

BackgroundCultivar recognition is a basic work in flower production, research, and commercial application. Chinese large-flowered chrysanthemum (Chrysanthemum × morifolium Ramat.) is miraculous because of its high ornamental value and rich cultural deposits. However, the complicated capitulum structure, various floret types and numerous cultivars hinder chrysanthemum cultivar recognition. Here, we explore how deep learning method can be applied to chrysanthemum cultivar recognition.ResultsWe propose deep learning models with two networks VGG16 and ResNet50 to recognize large-flowered chrysanthemum. Dataset A comprising 14,000 images for 103 cultivars, and dataset B comprising 197 images from different years were collected. Dataset A was used to train the networks and determine the calibration accuracy (Top-5 rate of above 98%), and dataset B was used to evaluate the model generalization performance (Top-5 rate of above 78%). Moreover, gradient-weighted class activation mapping (Grad-CAM) visualization and feature clustering analysis were used to explore how the deep learning model recognizes chrysanthemum cultivars.ConclusionDeep learning method applied to cultivar recognition is a breakthrough in horticultural science with the advantages of strong recognition performance and high recognition speed. Inflorescence edge areas, disc floret areas, inflorescence colour and inflorescence shape may well be the key factors in model decision-making process, which are also critical in human decision-making.

BACKGROUND Maize is frequently subjected to simultaneous water (drought or waterlogging) and heat (HS) stresses during grain formation in southern China. This work examined the effect of high temperature combined with drought (HD) or waterlogging (HW) during grain formation on the starch physicochemical properties of two waxy maize hybrids, namely Suyunnuo5 (SYN5) and Yunuo7 (YN7). RESULTS Heat stress enlarged the starch granule size, and water stresses aggravated this effect. Heat stress reduced the ratio of small molecular weight fractions for both hybrids, and HD aggravated this reduction only in SYN5. Relative crystallinity in SYN5 was increased by stresses but in YN7 it was unaffected by HD, reduced by HS, and increased by HW. Fourier‐transform infrared (FTIR) spectrometry results showed that the 1045/1022 cm−1 ratio in SYN5 was not influenced by HW but was increased by other stresses, and that in YN7 it was increased by all stresses, with the highest value induced by HW. Peak viscosity was decreased, whereas gelatinization temperatures and retrogradation percentage were increased by all of these stresses. These effects were exacerbated by combined heat and water stresses. The maximum decomposition rate was severely increased by HW. CONCLUSION Drought or waterlogging at grain formation stage aggravated the detrimental effects of HS on the starch physicochemical properties of waxy maize. © 2020 Society of Chemical Industry

A simple noncovalent chemical approach and hydrothermal method were used for effectively riveting Co3O4 nanocrystals to branched polyethylenimine (PEI) functional Ti3C2Tx MXene sheets to fabricate Co3O4@PEI/Ti3C2Tx MXene composites. Co3O4 nanoparticles...