Water Treatment Market to Develop as Researchers Create Peptide Sensor that can identify a large number of Pollutants from the Wastewater

Posted On December 22, 2021     

Marine pollution occurring due to the rise of plastic in water bodies is a global concern as it results in dying coral reefs while also decreasing fish population. Most of the solutions found to curb the problem are related to plastic pollution caused by microplastics, which are minuscule plastics that are highly challenging to remove from water. However, water-soluble synthetic polymers have started coming into the limelight as well. They are also responsible for marine pollution, particularly for their disadvantages to soil and water environments. As they are water-soluble, it is hard to recover them through standard filtration techniques. As a result, researching alternate techniques to remove these pollutants is critical. This can be done by understanding the properties of water-soluble polymer pollutant and analysing the amount at which it is present within Wastewater.

A newly published research study might have been successful in accomplishing this. They have unveiled a machine learning algorithm that can identify a vast amount of pollutants in one solution. The researchers trained the novel technique to take advantage of the bonding between different polymers and peptides. The research is highly relevant for Water Treatment Market as it demonstrates a peptide sensor to detect water-soluble polymers, i.e. microplastics from the Wastewater.

Polymers refer to a big chain of chemicals comprising smaller, repeating units. Even though they are hardly ever linked with proteins, they are still made up of thousands of sub-units referred to as amino acids. Short chains of these amino acids are known as peptides. Peptides can go through specific and non-specific interactions with molecules like polymers in distinct ways with different affinity states.

It was precisely such interactions that were exploited by the team to create a new peptide sensor for the recognition of water-soluble polymers that were added to solutions. The technology primarily depends upon a machine learning pattern analysis that imitates mammalian odor and also taste discrimination. Thus, the sensor is handy for the detection of numerous polymers and other molecules.

Furthermore, the technique is beneficial for identifying submerged macromolecular pollutants such as polymer within the water. In addition, it can also be used to analyse how these pollutants enter the environment as well.

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