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The Cohesiveness of the Oscillating Belousov-Zhabotinsky Reaction

Gottlieb et al. | Dec 18, 2018

The Cohesiveness of the Oscillating Belousov-Zhabotinsky Reaction

In this study the author undertakes a careful characterization of a special type of chemical reaction, called an oscillating Belousov-Zhabotinsky (or B-Z) reaction, which has a number of existing applications in biomedical engineering as well as the potential to be useful in future developments in other fields of science and engineering. Specifically, she uses experimental measurements in combination with computational analysis to investigate whether the reaction is cohesive – that is, whether the oscillations between chemical states will remain consistent or change over time as the reaction progresses. Her results indicate that the reaction is not cohesive, providing an important foundation for the development of future technologies using B-Z reactions.

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Automated classification of nebulae using deep learning & machine learning for enhanced discovery

Nair et al. | Feb 01, 2024

Automated classification of nebulae using deep learning & machine learning for enhanced discovery

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.

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Prediction of molecular energy using Coulomb matrix and Graph Neural Network

Hazra et al. | Feb 01, 2022

Prediction of molecular energy using Coulomb matrix and Graph Neural Network

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.

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Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

Chang et al. | Apr 29, 2022

Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

With monitoring of climate change and the evolving properties of the atmosphere more critical than ever, the authors of this study take sea salt aerosols into consideration. These sea salt aerosols, sourced from the bubbles found at the surface of the sea, serve as cloud condensation nuclei (CCN) and are effective for the formation of clouds, light scattering in the atmosphere, and cooling of the climate. With amines being involved in the process of CCN formation, the authors explore the effects of alkylamines on the properties of sea salt aerosols and their potential relevance to climate change.

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The Effect of Common Cations on DNA Degradation

Larina et al. | Nov 06, 2016

The Effect of Common Cations on DNA Degradation

Heating of DNA-containing solutions is a part of many experiment protocols, but it can also cause damage and degradation of the DNA molecules, potentially leading to error in the experimental results. The authors of this paper investigate whether the presence of certain cations during heating can stabilize the DNA polymer and aid the preservation of the molecule.

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The effect of molecular weights of chitosan on the synthesis and antifungal effect of copper chitosan

Byakod et al. | Apr 07, 2024

The effect of molecular weights of chitosan on the synthesis and antifungal effect of copper chitosan

Pathogenic fungi such as Alternaria alternata (A. alternata) can decimate crop yields and severely limit food supplies when left untreated. Copper chitosan (CuCts) is a promising alternative fungicide for developing agricultural areas due to being inexpensive and nontoxic. We hypothesized that LMWc CuCts would exhibit greater fungal inhibition due to the beneficial properties of LMWc.

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Determining the Habitable Zone Around a Star

Lee et al. | May 29, 2013

Determining the Habitable Zone Around a Star

Life requires many things, including a hospitable temperature, elements, and energy. Here the authors utilize Newton's laws of physics and information relating a star's luminosity and temperature to determine the minimum and maximum masses and luminosities of planets and stars that would support life as we know it. This work can be used to determine the likelihood of a planet being able to support life based on attributes we can measure from here on Earth.

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