Identification of Water-Soluble Polymers through Discrimination of Multiple Optical Signals from a Single Peptide Sensor

Identification of Water-Soluble Polymers through Machine Learning of Fluorescence Signals from Multiple Peptide Sensors

Recently, there has been growing concern about the discharge of water-soluble polymers (especially synthetic polymers) into the environment. Therefore, the identification of water-soluble polymers in water samples is becoming increasingly crucial. In this study, a chemical tongue system to simply and precisely identify water-soluble polymers using multiple fluorescently responsive peptide sensors was demonstrated. Fluorescence spectra obtained from the mixture of each peptide sensor and water-soluble polymer were changed depending on the combination of the polymer species and peptide sensors. Water-soluble polymers were successfully identified through the supervised or unsupervised machine learning of multidimensional fluorescence signals from the peptide sensors.

Evaluation of nuclear magnetic resonance spectroscopy for characterisation and quantitation of water-soluble polymers in river water

Water-soluble polymers (WSPs) are commonly used in industrial, commercial, agricultural and pharmaceutical products and their molecular weights and concentrations vary considerably. Methods commonly used in the analysis of WSPs are often for pure products or formulations with only a few other high MW constituents. These methods, like size exclusion chromatography (SEC) or Gel Permeation Chromatography coupled with Mass Spectrometry (MS) can be frustrated by the impact of the necessary separation steps prior to identification and the limitations of MS when identifying and quantifying polymers. To that end, the employment of a Nuclear Magnetic Resonance (NMR) method to identify, characterize and quantify WSPs in the real-world is reported for the first time. Samples were taken from fourteen UK inland river sites, concentrated via air-drying, freeze-drying or vacuum-drying and analyzed using 1D 1H NMR and 2D 1H Diffusion Ordered Spectroscopy (DOSY) NMR analysis. Seven of the river sites showed the presence of polyethylene glycol (PEG) with a range of molecular weights, evidencing the application of these techniques in analysis of WSPs. Soil percolation models evidenced the proof of principle that these techniques can also be used for the detection of polyacrylamide (PAM) and polyacrylic acid (PAA). This work should better enable the evaluation of the biological impact of WSPs on aquatic organisms in future studies.

A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges

Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelligence-based microplastic-imaging field from the Web of Science (2019-2022). By text mining and visualization in the scientific literature we emphasized some opportunities to bring forward further explication and analysis by (i) exploring efficient and low-cost automatic quantification methods in the appearance properties of microplastics, such as shape, size, volume, and topology, (ii) investigating microplastics water-soluble synthetic polymers and interaction with other soil and water ecology environments via artificial intelligence technologies, (iii) advancing efficient artificial intelligence algorithms and models, even including intelligent robot technology, (iv) seeking to create and share robust data sets, such as spectral libraries and toxicity database and co-operation mechanism, (v) optimizing the existing deep learning models based on the readily available data set to balance the related algorithm performance and interpretability, (vi) facilitating Unmanned Aerial Vehicle technology coupled with artificial intelligence technologies and data sets in the mass quantities of microplastics. Our major findings were that the research of artificial intelligence methods to revolutionize environmental science was progressing toward multiple cross-cutting areas, dramatically increasing aspects of the ecology of plastisphere, microplastics toxicity, rapid identification, and volume assessment of microplastics. The above findings can not only determine the characteristics and track of scientific development, but also help to find suitable research opportunities to carry out more in-depth research with many problems remaining.

Peptide probes with high affinity to target protein selection by phage display and characterization using biophysical approaches

Herein, phage display was utilized to screen the affinity of peptides against dihydrofolate reductase and a positive peptide was obtained, and the verification of the affinity was tested by multiple in vitro biophysical methods.