Machine learning-based exploration of biochar for environmental management and remediation.

Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.

Halide Perovskites for Photoelectrochemical Water Splitting and CO2 Reduction: Challenges and Opportunities.

Photoelectrochemical water splitting and CO2 reduction provide an attractive route to produce solar fuels while reducing the level of CO2 emissions. Metal halide perovskites (MHPs) have been extensively studied for this purpose in recent years due to their suitable optoelectronic properties. In this review, we survey the recent achievements in the field. After a brief introduction to photoelectrochemical (PEC) processes, we discussed the properties, synthesis, and application of MHPs in this context. We also survey the state-of-the-art findings regarding significant achievements in performance, and developments in addressing the major challenges of toxicity and instability toward water. Efforts have been made to replace the toxic Pb with less toxic materials like Sn, Ge, Sb, and Bi. The stability toward water has been also improved by using various methods such as compositional engineering, 2D/3D perovskite structures, surface passivation, the use of protective layers, and encapsulation. In the last part, considering the experience gained in photovoltaic applications, we provided our perspective for the future challenges and opportunities. We place special emphasis on the improvement of stability as the major challenge and the potential contribution of machine learning to identify the most suitable formulation for halide perovskites with desired properties.

Phytochemicals in Pancreatic Cancer Treatment: A Machine Learning Study.

The discovery of new strategies and novel therapeutic agents is crucial to improving the current treatment methods and increasing the efficacy of cancer therapy. Phytochemicals, naturally occurring bioactive constituents derived from plants, have great potential in preventing and treating various diseases, including cancer. This study reviewed 74 literature studies published between 2006 and 2022 that conducted in vitro cytotoxicity and cell apoptosis analyses of the different concentrations of phytochemicals and their combinations with conventional drugs or supplementary phytochemicals on human pancreatic cell lines. From 34 plant-derived phytochemicals on 20 human pancreatic cancer cell lines, a total of 11 input and 2 output variables have been used to construct the data set that contained 2161 different instances. The machine learning approach has been implemented using random forest for regression, whereas association rule mining has been used to determine the effects of individual phytochemicals. The random forest models developed are generally good, indicating that the phytochemical type, its concentration, and the type of cell line are the most important descriptors for predicting the cell viability. However, for predicting cell apoptosis the primary phytochemical type is the most significant descriptor . Among the studied phytochemicals, catechin and indole-3-carbinol were found to be non-cytotoxic at all concentrations irrespective of the treatment time. On the other hand, berbamine and resveratrol were strongly cytotoxic with cell viabilities of less than 40% at a concentration range between 10 and 100 µM and above 100 µM, respectively, which brings them forward as potential therapeutic agents in the treatment of pancreatic cancer.