Exploring structure-activity relationships for polymer biodegradability by microorganisms

Kim, Joonrae Roger; Thelusmond, Jean-Rene; Albright, Vurtice C.; Chai, Yunzhou

doi:10.1016/j.scitotenv.2023.164338

Cited by 5 publications

(1 citation statement)

References 134 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…29 Additionally, most chloride substituents are acutely toxic, and could provide a long-term toxicity effect on the environment. 32 There is an ongoing effort to develop more sustainable alternatives to traditional chlorinated polymers while retaining their desirable attributes. Despite the overall trend of toxicity among the by-products of chlorinated compound biodegradation, exceptions exist, and these exceptions warrant a closer examination to discern underlying factors governing the biodegradation process.…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of the Biodegradability of Chlorinated Com-pounds: Application of Quantitative Structure-Biodegradability Relationship Approach

Erickson,

Vasyutyn,

Ngongang

et al. 2024

Preprint

View full text Add to dashboard Cite

Chlorinated compounds are generally known to be non-readily biodegradable. The insight into the structural features that allow chlorinated compounds to readily biodegrade is crucial information that needs to be unveiled. Combined in silico modeling and machine learning approach to predict desirable compound properties has proven to be an effective tool, enabling chemists to save time and resources compared to web lab experimentation. Here we present two machine learning-based quantitative structure – biodegradability relationship (QSBR) models, one for predicting biodegradability values of chlorinated compounds, and the other one for classifying chlorinated compounds as biodegradable or non-biodegradable. The regression models were generated using the Support Vector Regression (SVR) machine learning method. The optimal regression model was a 10 descriptor SVR model with R2 = 0.925 and R2test = 0.881. The optimal classification model was a logistic regression classifier model with 5 descriptors. It has a Matthew’s Correlation Coefficient of 0.59 for training and 0.55 for test, as well as accuracy of 0.79 for training set, 0.77 for test set. For validation purposes the models were tested on an external data set of chlorinated compounds. In addition, models were further applied to an external test set of monomeric units representing polymers to assess the capability of the model to estimate the biodegradability of polymers, where the models showed statistical robustness. The developed SVR model could be used for accurate prediction of biodegradability of various organic molecules, as well as materials based on organic compounds. The analysis of the influence of descriptors on biodegradability is performed. The classification model showed that the biodegradability of chlorinated compounds is heavily correlated with descriptors that relate to electrotopological descriptors, position of oxygen relative to chlorine.

show abstract

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of the Biodegradability of Chlorinated Com-pounds: Application of Quantitative Structure-Biodegradability Relationship Approach

Erickson,

Vasyutyn,

Ngongang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Dynamic Cleavage‐Remodeling of Covalent Organic Networks into Multidimensional Superstructures

Jiang,

Zhang,

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

Superstructures with complex hierarchical spatial configurations exhibit broader structural depth than single hierarchical structures and the associated broader application prospects. However, current preparation methods are greatly constrained by cumbersome steps and harsh conditions. Here, we present, for the first time, a concise and efficient thermally responsive dynamic synthesis strategy for the preparation of multidimensional complex superstructures within soluble covalent organic networks (SCONs) with tunable morphology from 0D hollow supraparticles to 2D films. Mechanism study revealed the thermally responsive dynamic “cleavage‐remodeling” characteristics of SCONs, synthesized based on the unique bilayer structure of [2.2]paracyclophane, and the temperature control facilitates the process from reversible solubility to reorganization and construction of superstructures. Specifically, during the process, the oil‐water‐emulsion two‐phase interface can be generated through droplet jetting, leading to the preparation of 0D hollow supraparticles and other bowl‐like complex superstructures with high yield. Additionally, by modulating the volatility and solubility of exogenous solvents, defect‐free 2D films were prepared relying on an air‐liquid interface. Expanded experiments further confirmed the generalizability and scalability of the proposed dynamic “cleavage‐remodeling” strategy. Research on the enrichment mechanism of guest iodine highlights the superior kinetic mass transfer performance of superstructural products compared to single‐hierarchical materials. This study provides not only an innovative shortcut for the construction of superstructures, but also a scientific basis for the embodiment of the superstructural advantages and their wide applications.This article is protected by copyright. All rights reserved

show abstract

Bioresorbable polymer-based sensors for medical applications

Natesan,

Nangan,

Ramasamy

2024

Bioresorbable Polymers and Their Composites

View full text Add to dashboard Cite

Exploring structure-activity relationships for polymer biodegradability by microorganisms

Cited by 5 publications

References 134 publications

In Silico Prediction of the Biodegradability of Chlorinated Com-pounds: Application of Quantitative Structure-Biodegradability Relationship Approach

In Silico Prediction of the Biodegradability of Chlorinated Com-pounds: Application of Quantitative Structure-Biodegradability Relationship Approach

Dynamic Cleavage‐Remodeling of Covalent Organic Networks into Multidimensional Superstructures

Bioresorbable polymer-based sensors for medical applications

Contact Info

Product

Resources

About