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Biochar is a carbon-rich material produced through the pyrolysis of various feedstocks. It can be further modified to enhance its properties and is referred to as modified biochar (MB). The research interest in MB application in soil has been on the surge over the past decade. However, the potential benefits of MB are considerable, and its efficiency can be subject to various influencing factors. For instance, unknown physicochemical characteristics, outdated analytical techniques, and a limited understanding of soil factors that could impact its effectiveness after application. This paper reviewed the recent literature pertaining to MB and its evolved physicochemical characteristics to provide a comprehensive understanding beyond synthesis techniques. These include surface area, porosity, alkalinity, pH, elemental composition, and functional groups. Furthermore, it explored innovative analytical methods for characterizing these properties and evaluating their effectiveness in soil applications. In addition to exploring the potential benefits and limitations of utilizing MB as a soil amendment, this article delved into the soil factors that influence its efficacy, along with the latest research findings and advancements in MB technology. Overall, this study will facilitate the synthesis of current knowledge and the identification of gaps in our understanding of MB. Highlights: The application of modified biochar significantly increased over the past decade. Modified biochar demonstrated promising properties for specific applications, particularly in soil. However, outdated analytical techniques and limited understanding of soil factors could affect its efficiency. The potential benefits, along with limitations in commercialization and future approaches, were highlighted. [ABSTRACT FROM AUTHOR]

In the current study, three machine learning (ML) models, i.e. Gaussian process regression (GPR), generalized regression neural network (GRNN), and multigene genetic programming (MGGP), were developed for predicting the discharge coefficient (Cd) of a radial gate under two different flow conditions, i.e. free and submerged. The modeling development of the flow and geometry input variables for the Cd was determined based on statistical correlations. We also performed a sensitivity analysis of the input variables for the Cd. The modeling results indicated that the developed ML models attained acceptable predictable performance; however, the prediction accuracy of the models was better under the free flow condition. In quantitative terms, the minimum root mean square error (RMSE) value was 0.010 using the GPR model and 0.019 using the MGGP model for the submerged and free flow conditions, respectively. The sensitivity analysis evidenced that the ratio of the gate opening height to the depth of water in the upstream (W:Yo) was the influential variable for the Cd under the free flow condition, whereas the ratio of the depth of water in the upstream to the depth of water in downstream (Yo:YT) was the influential variable for the Cd under the submerged flow condition. [ABSTRACT FROM AUTHOR]

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Hydrogen power and electric vehicle rollout are among the global mitigation efforts for net-zero emission targets. Hydrogen fuel cell vehicle (HFCV) is a promising embodiment of these two climate-neutral levers. Nevertheless, insufficient investigation of the life cycle of hydrogen production pathways and supply-demand mismatching jeopardize the optimal implementation of HFCV. A novel integrated framework combining life cycle greenhouse gas assessment and intra-regional supply-demand optimization method is developed to (1) evaluate the emissions performance for six typical HFCV hydrogen production pathways and (2) map out the optimal intra-regional hydrogen supply-demand allocation based on the trade-off between resource flow and geographical proximity. As one of the world's largest HFCV producers and consumers, China is chosen as the case study. The life cycle emissions assessment reveals that the Natural Gas Steam Methane Reforming (NG_SMR) pathway emits the most greenhouse gas (GHG) emissions (i.e., 0.215 g CO 2 -eq/kJ H 2) while the Clean Energy_Water Electrolysis (CE_WE) pathway emits the least GHG emissions (i.e., 0.02 g CO 2 -eq/kJ H 2), stretching a 10-times emission difference. For intra-regional supply-demand optimization, provinces like Shaanxi, Shanxi, and Shandong act as the main suppliers (>50% of the total hydrogen source in China) to provinces like Guangdong, Zhejiang, and Sichuan, suggesting a north-to-south (N-S) regional hydrogen transmission. A sensitivity analysis is conducted based on the variation in production efficiency among different hydrogen production technologies in the HFCV value chain. The results inferred that the Coal Gasification & Carbon Capture and Storage (C l G & CCS) and CE_WE pathways are insensitive to efficiency improvement. In the scenario analysis, the outcome suggested urgently phasing out grey hydrogen pathways and accelerating the transition of grey-to-green hydrogen production pathways with the buffering of blue hydrogen pathways between 2015 and 2040 for the net-zero emission ambition by 2060. This study enlightens the decision-makers to endorse green hydrogen production for HFCV while securing hydrogen energy security. [Display omitted] • Hydrogen fuel cell vehicles accelerate low carbon transition in transportation sector. • Hydrogen resource allocation is one of the challenges developing fuel cell vehicle. • Life cycle assessment and intra-regional supply-chain optimization are coupled. • The integrated framework suggests a north-to-south hydrogen delivery in China. • Blue hydrogen pathway serves as an intermediate between grey-to-green transition. [ABSTRACT FROM AUTHOR]