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Oil spills are one of the most devastating events for marine life. Finding ways to clean up oil spills without the need for harsh chemicals could help decrease the negative impact of such spills. Here the authors demonstrate that using a combination of several biodegradable substances can effectively adsorb oil in seawater in a laboratory setting. They suggest further exploring the potential of such a combination as a possible alternative to commonly-used non-biodegradable substances in oil spill management.

One largely untapped source of clean energy is the use of osmotic gradients where freshwater and saltwater are mixed, for example at estuaries. To harness such energy, charge-selective membranes are needed to separate the anions and cations in saltwater, establishing an electric potential like a battery. The objective of this study was twofold: to investigate the creation of the polymer matrix and test the properties of boron nitride nanotubes, as both are essential in the creation of an ion-selective membrane. Out of three polymer samples tested in this study, the mixture known as Soltech 704 showed the best resistance to etching, as well as the highest UV cure rate.

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

Lithium-ion batteries, a breakthrough in chemistry that enabled the electronic revolution we live today have become an essential part of our day-to-day life. A phone battery running out after a heavy day of use with limited opportunities for recharging is a well-known and resented experience by almost everyone. How then can we make batteries more efficient? This paper proposes the use of a different type of separator, that improves the charging and discharging capacities of lithium ions compared to the classical separator. This and similar attempts to improve Lithium-ion battery function could facilitate the development of higher-performance batteries that work longer and withstand harsher use.

Dye-sensitized solar cells (DSSC) use dye as the photoactive material, which capture the incoming photon of light and use the energy to excite electrons. Research in DSSCs has centered around improving the efficacy of photosensitive dyes. A fruit's color is defined by a unique set of molecules, known as a pigment profile, which changes as a fruit progresses from ripe to rotten. This project investigates the use of fresh and rotten fruit extracts as the photoactive dye in a DSSC.

The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.

The authors looked at developing a PMMA nanoparticle fabric dye that would be more sustainable compared to traditional fabric dyes. They were able to create PMMA based dyes in different colors that were also durable (i.e., did not fade quickly on fabric).

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

The energy conservation in a system of objects in collision depends on the elasticity of the objects and environmental factors such as air resistance. One system that relies heavily on elasticity is the Newton’s Cradle. We aimed to determine the extent to which these adhesives serve to mitigate or worsen the chaotic movements and elastic collisions.

With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.