Wenjin Zhao's research while affiliated with Jilin University and other places

Antioxidants which are indispensable functional additives used in rubber tires, are released into aquatic habitats from tire wear particles (TWP), collected in water bodies, and threaten the aquatic ecosystem. This study aimed to design eco-friendly derivatives of 2,2,4-trimethyl-1,2-dihydroquinoline (TMQ) with increased antioxidant activity to use as tire antioxidants. Initially, seventy highly efficient derivatives of TMQ were designed by hydroxylation modifications at multiple sites. The antioxidant activity of hydroxyl derivatives was characterized based on DFT method and compared with TMQ. Twenty derivatives showing a significant (greater than 9%) increase in antioxidant activity compared to TMQ were selected for the next stage. The toxicity risk of these twenty TMQ derivatives was assessed using various toxicokinetic methods. Finally, six TMQ derivatives with significantly lower toxicity risk compared to that of TMQ were evaluated for potential developmental toxicity. They were characterized using molecular docking and molecular dynamics techniques to assess the developmental toxicity risk in silver salmon by absorption of their ROO·, HO·, O2·– and O3 derivatives. TMQ-6 and TMQ-48 showed the lowest toxicity among all TMQ derivatives by a rather large margin. The study throws light on the path of future endeavors to develop highly efficient and greener tire antioxidants.

Tire antioxidants are essential functional chemical additives in tire rubber production. Because of the characteristic easy precipitation in the water environment, the environmental pollution problem caused by tire antioxidants is concerning. To reveal the mechanism by which tire antioxidants reduce common oxidative factors (free radicals) in the environment and to control the potential biological thyroid hormone disorder risk of tire antioxidant derivatives, eight commonly used antioxidants in tire production were selected for analysis. Firstly, the ability of tire antioxidants to reduce three different free radicals was quantitatively characterized based on Gaussian calculation method and inferring the radical reduction mechanism of tire antioxidants. Moreover, using the PaDEL-Descriptor software and random forest algorithm found that the N-octanol/water partition coefficient, a structure descriptor of tire antioxidant molecules, significantly correlated with their reducing ability. Second, molecular docking and molecular dynamics methods were used to assess the thyroid hormone disorder risk to aquatic organisms of eight antioxidants after reducing three free radicals. And this is the first study to construct an assessment score list of potential thyroid hormone disorder risk of the derivatives of tire antioxidants after reducing free radicals to marine and freshwater aquatic organisms based on the risk entropy method. Through the screening of this list, it was found that the derivative of the antioxidant 2,2,4-trimethyl-1,2-dihydroquinoline oxidized by free radicals had the highest risk of thyroid hormone disorder. In addition, the top organism in the aquatic food chain was the most affected. This study also revealed that van der Waals interactions and hydrogen bonding were the main influencing factors of thyroid hormone disorder risk to aquatic organisms of the derivatives of tire antioxidants that reduce free radicals based on amino acid residue analysis. Overall, the results provide theoretical support for the selection of antioxidants and the avoidance and control of environmental risks in the tire rubber production process.

Bisphenols (BPs) have gained significant attention due to their extensive use in the production of medical equipment, packaging materials, and everyday commodities. Urgent attention is required for assessing and identifying the risks associated with BP exposure to the environment and human health, as well as developing regulatory strategies. In this paper, 29 common BPs were selected as the research object, high-performance BP substitutes with environmental and human health friendliness characteristics were designed and screened. The above eight BP substitutes were considered as examples, and the first-level evaluation indicators of BPs and their substitutes were predicted using a random forest classification/regression model. Subsequently, the key indicators affecting the first-level evaluation indicators were ranked. The ranking results were environmental friendliness (64.30%) > human health risk (18.00%) > functionality (17.69%), indicating that environmental friendliness was the main influencing factor for the first-level evaluation indicators of BPs and their substitutes. Therefore, the study employed density functional theory (DFT) to simulate the biodegradation pathways of BPs and their substitutes in contaminated soil and landfill leachate, using Derivative-50 as an example. Furthermore, the environmental risk associated with the degradation products was evaluated, and regulatory recommendations based on risk identification were proposed.

Tire polymers (TPs) are the most prevalent type of microplastics and are of great concern due to their potential environmental risks. This study aims to determine the toxicity of TPs with the help of molecular-dynamics simulations of their interactions with receptors and to highlight the differences in the toxicity characteristics of TPs in different environmental media (marine environment, freshwater environment, soil environment). For this purpose, five TPs—natural rubber, styrene–butadiene rubber (SBR), butadiene rubber, nitrile–butadiene rubber, and isobutylene–isoprene rubber—were analyzed. Molecular-dynamics calculations were conducted on their binding energies to neurotoxic, developmental, and reproductive receptors of various organisms to characterize the toxic effects of the five TPs. The organisms included freshwater species (freshwater nematodes, snails, shrimp, and freshwater fish), marine species (marine nematodes, mussels, crab, and marine fish), and soil species (soil nematodes, springtails, earthworms, and spiders). A multilevel empowerment method was used to determine the bio-toxicity of the TPs in various environmental media. A coupled-normalization method–principal-component analysis–factor-analysis weighting method—was used to calculate the weights of the TP toxicity (first level) categories. The results revealed that the TPs were the most biologically neurotoxic to three environmental media (20.79% and 10.57% higher compared with developmental and reproductive toxicity, respectively). Regarding the effects of TPs on organisms in various environmental media (second level), using a subjective empowerment approach, a gradual increase in toxicity was observed with increasing trophic levels due to the enrichment of TPs and the feeding behavior of organisms. TPs had the greatest influence in the freshwater-environment organisms according to the subjective empowerment approach employed to weight the three environmental media (third level). Therefore, using the minimum-value method coupled with the feature-aggregation method, the interval-deflation method coupled with the entropy-weighting method, and the standard-deviation normalization method, the three toxicity characteristics of SBR in three environmental media and four organisms were determined. SBR was found to have the greatest impact on the overall toxicity of the freshwater environment (12.38% and 9.33% higher than the marine and soil environments, respectively). The greatest contribution to neurotoxicity (26.01% and 15.95% higher than developmental and reproductive toxicity, respectively) and the greatest impact on snails and shrimp among organisms in the freshwater environment were observed. The causes of the heterogeneity of SBR’s toxicity were elucidated using amino-acid-residue analysis. SBR primarily interacted with toxic receptors through van der Waals, hydrophobic, π-π, and π-sigma interactions, and the more stable the binding, the more toxic the effect. The toxicity characteristics of TMPs to various organisms in different environments identified in this paper provide a theoretical basis for subsequent studies on the prevention and control of TMPs in the environment.

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

As an effective herbicide, 1, 3, 5-Triazine herbicides (S-THs) are used widely in the pesticide market. However, due to their chemical properties, S-THs severely threaten the environment and human health (e.g., human lung cytotoxicity). In this study, molecular docking, Analytic Hierarchy Process—Technique for Order Preference by Similarity to the Ideal Solution (AHP-TOPSIS), and a three-dimensional quantitative structure-active relationship (3D-QSAR) model were used to design S-TH substitutes with high herbicidal functionality, high microbial degradability, and low human lung cytotoxicity. We discovered a substitute, Derivative-5, with excellent overall performance. Furthermore, Taguchi orthogonal experiments, full factorial design of experiments, and the molecular dynamics method were used to identify three chemicals (namely, the coexistence of aspartic acid, alanine, and glycine) that could promote the degradation of S-THs in maize cropping fields. Finally, density functional theory (DFT), Estimation Programs Interface (EPI), pharmacokinetic, and toxicokinetic methods were used to further verify the high microbial degradability, favorable aquatic environment, and human health friendliness of Derivative 5. This study provided a new direction for further optimizations of novel pesticide chemicals.