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Sustainable methane utilization technology via photocatalytic halogenation with alkali halides.

ABSTRACT: Methyl halides are versatile platform molecules, which have been widely adopted as precursors for producing value-added chemicals and fuels. Despite their high importance, the green and economical synthesis of the methyl halides remains challenging. Here we demonstrate sustainable and efficient photocatalytic methane halogenation for methyl halide production over copper-doped titania using alkali halides as a widely available and noncorrosive halogenation agent. This approach affords a methyl halide production rate of up to 0.61 mmol h^-1 m^-2 for chloromethane or 1.08 mmol h^-1 m^-2 for bromomethane with a stability of 28 h, which are further proven transformable to methanol and pharmaceutical intermediates. Furthermore, we demonstrate that such a reaction can also operate solely using seawater and methane as resources, showing its high practicability as general technology for offshore methane exploitation. This work opens an avenue for the sustainable utilization of methane from various resources and toward designated applications.

SUBMITTER: Ma J

PROVIDER: S-EPMC10014990 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Sustainable methane utilization technology via photocatalytic halogenation with alkali halides.

Ma Jun J Zhu Can C Mao Keke K Jiang Wenbin W Low Jingxiang J Duan Delong D Ju Huanxin H Liu Dong D Wang Kun K Zang Yijing Y Chen Shuangming S Zhang Hui H Qi Zeming Z Long Ran R Liu Zhi Z Song Li L Xiong Yujie Y

Nature communications 20230314 1

Methyl halides are versatile platform molecules, which have been widely adopted as precursors for producing value-added chemicals and fuels. Despite their high importance, the green and economical synthesis of the methyl halides remains challenging. Here we demonstrate sustainable and efficient photocatalytic methane halogenation for methyl halide production over copper-doped titania using alkali halides as a widely available and noncorrosive halogenation agent. This approach affords a methyl ha ...[more]

PMID: 36918590

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Project description:Wheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management. This research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively. Under the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress.

Dataset Information

Sustainable methane utilization technology via photocatalytic halogenation with alkali halides.

Publications

Sustainable methane utilization technology via photocatalytic halogenation with alkali halides.

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