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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.

Recognizing the need for a method to filter microplastics from polluted water the authors sought to use nonpolar solvents, palm oil and palm kernel oil, to filter microplastics out of model seawater. By relying on the separation of polar and nonpolar solvents followed by freezing the nonpolar solvent, they reported that microplastics could be extracted with percentages ranging from 96.2% to 94.2%. They also provided an estimation to use this method as part of container ships to clean the Pacific Ocean of microplastics.

Microbial fuel cells (MFCs) are bio-electrochemical systems that utilize bacteria and are promising forms of alternative energy. Similar to chemical fuel cells, MFCs employ both an anode (accepts electrons) and a cathode (donates electrons), but in these devices the live bacteria donate the electrons necessary for current. In this study, the authors assess the functionality of a photosynthetic MFC that utilizes a purple non-sulfur bacterium. The MFC prototype they constructed was found to function over a range of environmental conditions, suggesting its potential use in industrial models.

The authors looked at the ability of Shewanella oneidensis to generate energy in a microbial fuel cell under varying conditions. They found that the S. Onedensis biofilm was able to produce energy in microgravity and that one of the biggest factors that limited energy production was a decrease in growth medium present.

In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and T_g is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's T_g, providing valuable improvements to this useful platform.

Understanding the techniques used to improve the adhesion strength of the epoxy resin is important especially for consumer applications such as repairing car parts, bonding aluminum sheeting, and repairing furniture or applications within the aviation or civil industry. Selleys Araldite epoxy makes specific strength claims emphasizing that the load or weight that can be supported by the adhesive is 72 kg/cm². Nguyen and Clarke aimed to test the strength claims of Selley’s Araldite Epoxy by gluing two steel adhesion surfaces: a steel tube and bracket. Results showed that there is a lack of consideration by Selleys for adhesion loss mechanisms and environmental factors when accounting for consumer use of the product leading to disputable claims.

Here, the authors sought to investigate the efficiency, cost, and environmental impact of several possible propellants that are or could be used for space flight. By deriving three novel equations, they identified harm, energy, and cost scores for each fuel, suggesting that considering each factor will be essential to the ongoing growth of the space industry.

Saccharomyces cerevisiae yeast is used to produce bioethanol, an alternative to fossil fuels. In this study, authors take advantage of this well studied yeast by genetically engineering them to increase fatty acid biosynthesis and culturing in a cost-effective wastewater based medium; potentially providing a sustainable alternative to petrochemicals.

The authors looked at the development of biodegradable bioplastic and its features compared to PET packaging films. They were able to develop a biodegradable plastic with sodium alginate that dissolved in water and degrade in microbial conditions while also being transparent and flexible similar to current plastic films.

Counterintuitive in nature, the Mpemba effect asserts that hot liquid freezes faster than cold liquid. While noted throughout history by scientific minds like Aristotle, the phenomenon remains in contention with varying hypotheses for the effect proposed alongside the effect’s rise in popularity. Contributing to the research efforts surrounding the Mpemba effect, the authors in this article explore the effect in different liquids ranging in physical properties and intermolecular forces to determine potential parameters attributable to producing the Mpemba effect.