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.

Impact of House Price on Economic Stability: Some Lessons from OECD Countries.

Despite having abundant literature blaming a faulty financial system and exuberant price expectations as the primary causes of housing bubbles, there is a lack of research that looks at the impact of house price instability on the economy. This study aims to fill this gap by thoroughly examining the connection between house prices and economic output, and the effect of house price volatility on economic stability. Drawing from long-spanning quarterly data from 17 OECD countries from 1970 to 2019, the study develops and tests economic growth and volatility models to uncover significant insights. The empirical results show that house price returns have a significant asymmetric impact on economic growth, with negative returns having twice the effect of positive ones. Moreover, the results indicate that house price volatility significantly contributes to economic instability. In light of these findings, the paper concludes with valuable policy recommendations to enhance the housing market and improve overall economic stability. This study provides a compelling argument for the importance of closely monitoring and regulating the real estate market in order to maintain a healthy and stable economy.

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation.

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

Analysis of CH4 Uptake over Metal-Organic Frameworks Using Data-Mining Tools.

A database containing 2224 data points for CH4 storage or delivery in metal-organic frameworks (MOFs) was analyzed using machine-learning tools to extract knowledge for generalization. The database was first reviewed to observe the basic trends and patterns. It was then analyzed using decision trees and artificial neural networks (ANN) to extract hidden information and develop rules and heuristics for future studies. Five-fold cross validations were used in each analysis to test the validity of the models with data not seen before. Decision-tree analyses were carried out using six user-defined descriptors and two structural properties, separately. The crystal structure and the total degree of unsaturation were found to be the effective user-defined descriptors, whereas the pore volume and maximum pore diameter, as structural properties, were sufficient to determine the MOFs having high CH4-storage capacity. Moreover, a high pore volume is always required, as expected. In ANN analyses, models were also developed by using user-defined descriptors and structural properties separately. It was observed that the user-defined descriptors were not sufficient to describe the CH4-storage capacities of MOFs, whereas the structural properties in particular led to accurate CH4-storage predictions with an RMSE of 26.8 and an R2 of 0.92 for testing.

Structure and activity relationship for CO and O2 adsorption over gold nanoparticles using density functional theory and artificial neural networks.

In this work, the structure and activity relationship for CO and O(2) adsorption over Au(2) to Au(10) clusters was investigated using density functional theory (DFT) and artificial neural networks as a part of ongoing studies in the literature to understand CO oxidation over gold nanoparticles. The optimum structures for the anionic, neutral, and cationic clusters were determined first using DFT. The structural properties such as binding energy, highest occupied molecular orbital-lowest unoccupied molecular orbital gap, ionization potential, and electron affinity as well as the adsorption energies of CO and O(2) were calculated using the same method at various values of user defined descriptors such as the size and charge of the cluster, the presence or absence of unpaired electron, and the coordination number of the adsorption site. Then, artificial neural network models were constructed to establish the relationship between these descriptors and the structural properties, as well as between the structural properties and the adsorption energies. It was concluded that the neural network models can successfully predict the adsorption energies calculated using DFT. The statistically determined relative significances of user defined descriptors and the structural properties on the adsorption energies were also found to be in good agreement with the literature indicating that this approach may be used for the other catalytic systems as well.