In this article, the authors created CuSbS₂ solar cells. They discovered that the cells' efficiency was affected by the formation of MoS₂. By incorporating a layer of single-walled carbon nanotubes, however, they were able to prevent MoS₂ formation and increase the device's efficiency.

Unprocessed water from hand-pressed wells is still commonly used as a source of drinking water in Chenzhou, the “Nonferrous Metal Village” of China. Long et al. conducted a study to measure the heavy metal contamination levels and potential health effects in this area. Water samples were analyzed through Inductively Coupled Plasma Optical Emission Spectroscopy (ICPOES) and the concentrations of 20 metal elements. Results showed that although none of the samples had dangerous levels of heavy metals, the concentrations of Al, Fe, and Mn in many locations substantially exceeded those suggested in the Chinese Drinking Water Standard and the maximum contaminant levels of Environmental Protection Agency (EPA). The authors have made an important discovery regarding the water safety in HuNan and their suggestions to install water treatment systems would greatly benefit the community.

In this study, the authors investigate whether Moringa Oleifera seeds can serve as material to aid in purifying water. M. oleifera seeds have coagulating properties and the authors hypothesized that including it in a water filtration system would reduce particles, specifically bacteria, in water. Their results show that this system removed the largest percent of bacteria. When used in combination with cilantro, it was actually more efficient than the other techniques! These findings have important implications for creating better and more economical water purification systems.

In our modern age, the burgeoning use of radios and radars has resulted in competition for electromagnetic spectrum resources. With recent research highlighting solutions to radio and radar mutual interference, there is a desperate need for a cost-effective configuration that permits a radar-radio joint system. In this study, the authors have set out to determine the feasibility of using single-tone continuous-wave radars in a radar-joint system. With this system, they aim to facilitate cost-effective near-field target detection by way of the popularized 2.4-GHz industrial, scientific, and medical (ISM) band.

Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

The authors tested environmentally-friendly alternatives to wastewater treatment chemicals, including activated charcoal for filtration and citrus peels for preventing bacterial growth.

Here, recognizing the need to improve the efficiency of the conversion of solar energy to electrical energy, the authors used MATLAB to mathematically simulate a multi-layered thin film with an without an antireflective coating. They found that the use of alternating ZnO-SiO₂ multilayers enhanced the transmission of light into the solar cell, increasing its efficiency and reducing the reflectivity of the Si-Air interface.

The authors survey high school students regarding the frequency of microaggressions such as name mispronunciation.

The authors test the usefulness of zinc oxide microparticles relative to zinc oxide nanoparticles as antibacterial agents.