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Summary The progress of bio‐hydrogen technology has led to the development of new energy technologies and is significant for the sustainable use of energy. After summarizing current research results, this study discusses that the key to increasing the hydrogen production rate is to improve the activity of hydrogen producing bacteria under the conditions of anaerobic fermentation. Using waste to prepare hydrogen producing bacteria is the developmental trend. The primary factors influencing bio‐hydrogen production from plant straw fermentation are also pointed out, indicating the method to improve the hydrogen production rate from plant straw. In addition, application of artificial intelligence technology to a bio‐hydrogen production reactor is helpful to achieve automatic control of continuous bio‐hydrogen production and improve the rate of hydrogen production.

Livestock and poultry breeding industry is one of the main economic pillars of northeastern China. However, the amount of pollutants produced is much higher than that in other parts of China. Through a questionnaire survey, indoor experiment, and outdoor experiment, it was found that the resource utilization rate of livestock and poultry manure in the northeastern region is low, with the pollution of livestock and poultry breeding mainly including air and water pollution. The alarm level of cultivated land and manure is II. While the livestock and poultry breeding is relatively concentrated area, its level is higher than grade II. Based on the pollution status of small farms, biogas can be produced through fermentation, along with the preparation of organic fertilizer, to completely utilize the manure and straw, while obtaining higher economic value, and effectively controlling the pollution from livestock and poultry breeding.

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Cascade catalysis that combines chemical catalysis and biocatalysis has received extensive attention in recent years, especially the integration of metal nanoparticles (MNPs) with enzymes. However, the compatibility between MNPs and enzymes, and the stability of the integrated nanocatalyst should be improved to promote the application. Therefore, in this study, we proposed a strategy to space-separately co-immobilize MNPs and enzymes to the pores and surface of a highly stable covalent organic framework (COF), respectively. Typically, Pd NPs that were prepared by in situ reduction with triazinyl as the nucleation site were distributed in COF (Tz-Da), and organophosphorus hydrolase (OPH) was immobilized on the surface of Tz-Da by a covalent method to improve its stability. The obtained integrated nanocatalyst Pd@Tz-Da@OPH showed high catalytic efficiency and reusability in the cascade degradation of organophosphate nerve agents. Furthermore, the versatility of the preparation strategy of COF-based integrated nanocatalyst has been preliminarily expanded: (1) Pd NPs and OPH were immobilized in the triazinyl COF (TTB-DHBD) with different pore sizes for cascade degradation of organophosphate nerve agent and the particle size of MNPs can be regulated. (2) Pt NPs and glucose oxidase were immobilized in COF (Tz-Da) to obtain an integrated nanocatalyst for efficient colorimetric detection of phenol.

Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red–green–blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture.