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Developing a Method to Remove Inorganic Arsenic from Rice with Natural Substances

Mukai et al. | Oct 27, 2020

Developing a Method to Remove Inorganic Arsenic from Rice with Natural Substances

In this study, the authors tested different approaches for removing arsenic from rice. Due to higher arsenic levels in water, some areas grow rice with higher levels as well. This is a health hazard and so developing methods to remove arsenic from the rice will be helpful to many. Using a rapid arsenic kit, the authors found that activated charcoal was the most effective at removing arsenic from rice.

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Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation

Gupta et al. | Jan 31, 2023

 Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation
Image credit: Markus Spiske

Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

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The Effects of Barley Straw (Hordeum vulgare) Extract and Barley Straw Pellets on Algal Growth and Water Quality

McHargue et al. | Oct 06, 2020

The Effects of Barley Straw (Hordeum vulgare) Extract and Barley Straw Pellets on Algal Growth and Water Quality

Algal overgrowth often threatens to clog irrigation pipes and drinking water lines when left unchecked, as well as releasing possible toxins that threaten plant and human health. It is thus important to find natural, non-harmful agents that can decrease algal growth without threatening the health of plants and humans. In this paper, the authors test the efficacy of barely extract in either liquid or pellet form in decreasing algal growth. While their results were inconclusive, the experimental set-up allows them to investigate a wider range of agents as anti-algal treatments that could potentially be adopted on a wider scale.

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Artificial Intelligence-Based Smart Solution to Reduce Respiratory Problems Caused by Air Pollution

Bhardwaj et al. | Dec 14, 2021

Artificial Intelligence-Based Smart Solution to Reduce Respiratory Problems Caused by Air Pollution

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

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The Effect of Cobalt Biomineralization on Power Density in a Microbial Fuel Cell

Bandyopadhyay et al. | Sep 07, 2015

The Effect of Cobalt Biomineralization on Power Density in a Microbial Fuel Cell

A microbial fuel cell is a system to produce electric current using biochemical products from bacteria. In this project authors operated a microbial fuel cell in which glucose was oxidized by Shewanella oneidensis in the anodic compartment. We compared the power output from biomineralized manganese or cobalt oxides, reduced by Leptothrix cholodnii in the cathodic compartment.

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