Biochar production and its environmental applications: Recent developments and machine learning insights.

Biochar production through thermochemical processing is a sustainable biomass conversion and waste management approach. However, commercializing biochar faces challenges requiring further research and development to maximize its potential for addressing environmental concerns and promoting sustainable resource management. This comprehensive review presents the state-of-the-art in biochar production, emphasizing quantitative yield and qualitative properties with varying feedstocks. It discusses the technology readiness level and commercialization status of different production strategies, highlighting their environmental and economic impacts. The review focuses on integrating machine learning algorithms for process control and optimization in biochar production, improving efficiency. Additionally, it explores biochar's environmental applications, including soil amendment, carbon sequestration, and wastewater treatment, showcasing recent advancements and case studies. Advances in biochar technologies and their environmental benefits in various sectors are discussed herein.

Application of machine learning on understanding biomolecule interactions in cellular machinery.

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Optimization of process variables on two-step microwave-assisted transesterification of waste cooking oil.

Scale-up and commercialization of biodiesel is often delimited by costly feedstock that adds up to the process costs. These underlying issues demand the exploration of unconventional cheap feed to improve the process economics. Conversion of waste cooking oil (WCO) into biodiesel could reduce the process costs by 60-70%. However, the continuous exposure to heat during frying leads to oxidation as well increase in the free fatty acid (FFA) content which intensifies the time and energy required for transesterification. The present study analyzes the effect of parameters over the conversion of WCO (with 8.17% FFA) into biodiesel via two-step acid-alkali-based microwave-assisted transesterification. Response surface methodology (RSM) was used to optimize the oil:methanol volume ratio, microwave power, and reaction time during the acid-catalyzed esterification to bring down the FFA below 1%. Microwave irradiation of 250 W, with methanol:oil molar ratio of 19.57:1 [oil:methanol volume ratio of 1.31 (expressed as decimal)] and reaction time of 35 s, resulted in 0.082% of FFA. Alkali-catalyzed transesterification with methanol:oil molar ratio of 5:1 with 2% sodium hydroxide at 65 °C thereby produced fatty acid methyl esters (FAMEs) with the volumetric biodiesel yield of 94.6% in 30 min. Physiochemical properties of the transesterified WCO were well comparable with the biodiesel standards. The study highlights the essentiality of multivariate optimization for the esterification process that could aid in understanding the interactive effects of variables over FFA content. Such studies would benefit in scaling up of the transesterification process at industrial level by improving the economics of the overall bioprocess.