Meta-Analysis of Cytotoxicity Studies Using Machine Learning Models on Physical Properties of Plant Extract-Derived Silver Nanoparticles.

Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules' mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R2. The obtained high value of R2 and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments.

Emerging Surface, Bulk, and Interface Engineering Strategies on BiVO4 for Photoelectrochemical Water Splitting.

The photoelectrochemical (PEC) cell that collects and stores abundant sunlight to hydrogen fuel promises a clean and renewable pathway for future energy needs and challenges. Monoclinic bismuth vanadate (BiVO4 ), having an earth-abundancy, nontoxicity, suitable optical absorption, and an ideal n-type band position, has been in the limelight for decades. BiVO4 is a potential photoanode candidate due to its favorable outstanding features like moderate bandgap, visible light activity, better chemical stability, and cost-effective synthesis methods. However, BiVO4 suffers from rapid recombination of photogenerated charge carriers that have impeded further improvements of its PEC performances and stability. This review presents a close look at the emerging surface, bulk, and interface engineering strategies on BiVO4 photoanode. First, an effective approach of surface functionalization via different cocatalysts to improve the surface kinetics of BiVO4 is discussed. Second, state-of-the-art methodologies such as nanostructuring, defect engineering, and doping to further enhance light absorption and photogenerated charge transport in bulk BiVO4 are reviewed. Third, interface engineering via heterostructuring to improve charge separation is introduced. Lastly, perspectives on the foremost challenges and some motivating outlooks to encourage the future research progress in this emerging frontier are offered.

Multi-Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning.

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF-IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF-IDF unigram model confirming 0.67 F1 score. The proposed multi-ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.

Rapid Synthesis of Ultrathin Ni:FeOOH with In Situ-Induced Oxygen Vacancies for Enhanced Water Oxidation Activity and Stability of BiVO4 Photoanodes.

The coupling of oxygen evolution reaction (OER) catalysts with photoanodes is a promising strategy for enhancing the photoelectrochemical (PEC) performance by passivating photoanode's surface defect states and facilitating charge transfer at the photoanode/electrolyte interface. However, a serious interface recombination issue caused by poor interface and OER catalysts coating quality often limits further performance improvement of photoanodes. Herein, a rapid Fenton-like reaction method is demonstrated to produce ultrathin amorphous Ni:FeOOH catalysts with in situ-induced oxygen vacancies (Vo) to improve the water oxidation activity and stability of BiVO4 photoanodes. The combined physical characterizations, PEC studies, and density functional theory calculations revealed that the reductive environment in a Fenton-like reaction in situ produces abundant Vo in Ni:FeOOH catalysts, which significantly improves charge separation and charge transfer efficiency of BiVO4 while also offering abundant active sites and a reduced energy barrier for OER. As a result, Ni:FeOOH-Vo catalysts yielded a more than 2-fold increased photocurrent density in the BiVO4 photoanode (from 1.54 to 4.15 mA cm-2 at 1.23 VRHE), accompanied by high stability for 5 h. This work not only highlights the significance of abundant Vo in catalysts but also provides new insights into the rational design and fabrication of efficient and stable solar water-splitting systems.