Researchers from the Tokyo Institute of Technology have come up with a peptide sensor that has the ability to detect water-soluble polymers in wastewater. Water-soluble polymers are considered to be a significant contributor to pollution at the same level as microplastics.
Researchers from Tokyo Tech have developed a new peptide sensor to detect dissolved polymer pollutants in wastewater, using polymer classification through supervised machine learning. This research achievement will be featured in the supplementary cover of the journal issue. Image Credit: Toshiki Sawada from Tokyo Institute of Technology.
The new method takes advantage of the bonding that takes place between peptides and various polymers to train a machine learning algorithm that is capable of both determining and measuring a huge number of pollutants in an individual solution.
Right from dying coral reefs to decreasing fish populations, marine pollution caused by plastics is a burgeoning global concern. The majority of the recent conversation on plastic pollution has mainly dealt with microplastics — small bits of plastic that are very hard to eliminate from the water.
However, there is an increasing interest in water-soluble synthetic polymers known to be a source of marine pollution, particularly concerning the threats they pose to soil and water surroundings.
As the polymers are water-soluble, they cannot be recovered with the help of normal filtration methods. Developing alternative methods to eliminate these pollutants is key. Thus, comprehending the precise nature of the water-soluble polymer pollutant, as well as measuring its amount in wastewater, has turned out to be a central point for scientists.
Polymers are long chains of chemicals composed of much smaller and repeating units. Even though they are scarcely associated with the term, proteins too can be considered polymers since they are made up of thousands of subunits known as “amino acids.” Short chains of such amino acids are named peptides.
Peptides can involve in specific and non-specific interactions with molecules, like polymers, in various methods with different levels of affinity. In a new study reported in the journal ACS Applied Materials & Interfaces, scientists from the Tokyo Institute of Technology (Tokyo Tech) have used such interactions to design a new peptide sensor for the identification of water-soluble polymers present in mixed solutions.
Our technique depends on a machine learning pattern analysis that mimics mammalian odor and taste discrimination. Just like how our noses and tongues can distinguish between myriad odors and tastes using a limited number of receptor proteins, so too can our single peptide senor be used to detect multiple polymers and other molecules.
Takeshi Serizawa, Study Lead and Professor, Tokyo Institute of Technology
The research group developed the method using a peptide that attaches to a synthetic polymer known as poly (N-isopropylacrylamide) (PNIPAM). Furthermore, they initiated a fluorescent “tag” named N-(1-anilinonaphthyl-4)maleimide (ANM) into the peptide to help achieve signals for its different interactions.
The fluorescence of ANM differed depending on the interaction of the protein, thus providing a detectable signal. The scientists quantified the signals from ANM in well-known solution concentrations of various polymers and utilized it to train a so-called “linear discriminant analysis” algorithm. This is considered to be one of monitored machine learning.
The researchers validated their method with unfamiliar samples and discovered that the sensor and algorithm can determine polymers in mixed solutions.
Furthermore, following the addition of small amounts of sodium chloride or ethanol to the solutions to slightly alter the chemical interactions, the machine learning algorithm could differentiate polymers with similar properties. Lastly, the new peptide sensor and algorithm were tested by the researchers on actual wastewater, and the potential to detect various water-soluble polymers was verified.
Our technique can be used to not only detect dissolved macromolecular pollutants like polymer in water, but also will be used to analyze how they enter into the environment.
Takeshi Serizawa, Study Lead and Professor, Tokyo Institute of Technology
The research team plans to extend the technique to other polymers and peptides. Such effective study could help remediate and safeguard marine environments.
Journal Reference:
Suzuki, S., et al. (2021) Identification of Water-Soluble Polymers through Discrimination of Multiple Optical Signals from a Single Peptide Sensor. ACS Applied Materials & Interfaces. doi.org/10.1021/acsami.1c11794.