Catechol-Based Porous Organic Polymers for Effective Removal of Phenolic Pollutants from Water

Prediction of chlorination degradation rate of emerging contaminants based on machine learning models

Assessing the degradation of emerging contaminants in water through chlorination is crucial for regulatory monitoring of these contaminants. In this study, we developed a machine learning model to predict the apparent second-order reaction rate constants for organic pollutants undergoing chlorination. The model was trained using second-order reaction rate constants for 587 organic pollutants, with 314 data points obtained from actual experiments, the other data points 273 came from previous studies. We evaluated ten machine learning algorithms with Modred molecular descriptors and MACCS molecular fingerprints, optimizing the hyperparameters through Bayesian optimization to enhance the predictive capability of the model. The optimized model GPR algorithm combined with molecular fingerprint model achieved R2train = 0.866 and R2test = 0.801. Subsequently, the model was fed with chemical features of four organic pollutants, and the predicted results were compared with experimentally obtained values, the deviations between predicted and experimental values were found to be 2.12%, 0.37%, 0.15%, and 14.8%, respectively, further validating the accuracy of the predictive model. SHAP analysis showed that the amino-methyl group CN(C)C had the highest feature value, demonstrating the interpretability of the model in predicting chlorine-degraded pollutants The model established in this study is more representative of real chlorination environments, providing preliminary guidance for chlorination plants on the degradation of numerous emerging contaminants lacking treatment standards and facilitating the refinement of strategies for the prevention and control of emerging contaminants.

MnO/TiO2/C/N-CNTs derived from Mn-doped Ti MOFs for simultaneous detection of catechol and hydroquinone

Mn-doped Ti-based MOFs (MnTi MOFs) were synthesized by a solvothermal method, and calcined at high temperature after being mixed with pre-prepared PPy nanotubes to give MnO/TiO2/C/N-CNTs composites. The composites were studied by SEM, XRD, XPS and FTIR. Based on these composites, a new electrochemical sensor was prepared, which has good electrocatalytic ability for the redox of catechol (CC) and hydroquinone (HQ), and can detect CC and HQ simultaneously. The results showed that the oxidation peak current of CC and HQ increased linearly in the concentration range of 0.50-120.00 μM. The detection limits were 0.033 μM and 0.019 μM, respectively. The constructed sensor has been successfully used for the simultaneous detection of CC and HQ in lake water and tap water, and has a good recovery rate.

Construction of magnetic azo-linked porous polymer for highly-efficient enrichment and separation of phenolic endocrine disruptors from environmental water and fish

Developing effective methods for highly sensitive detection of phenolic endocrine disruptors (EDCs) is especially urgent. Herein, a magnetic hydroxyl-functional porous organic polymer (M-FH-POP) was facilely synthesized by green diazo-couple reaction using basic fuchsin and hesperetin as monomer for the first time. M-FH-POP delivered superior adsorption performance for phenolic EDCs. The adsorption mechanism was hydrogen bonds, hydrophobic interaction and π-π interplay. With M-FH-POP as adsorbent, a magnetic solid phase extraction method was established for extracting trace phenolic EDCs (bisphenol A, 4-tert-butylphenol, bisphenol F and bisphenol B) in water and fish before ultra-high performance liquid chromatography tandem mass spectrometry analysis. The method displayed low detection limit (S/N = 3) of 0.05-0.15 ng mL-1 for water and 0.08-0.3 ng g-1 for fish. The spiked recoveries were 88.3 %-109.8 % with the relative standard deviations of 2.4 %-6.4 %. The method offers a new strategy for sensitive determination of phenolic EDCs in water and fish samples.

Features of Changes in the Structure and Properties of a Porous Polymer Material with Antibacterial Activity during Biodegradation in an In Vitro Model

Hybrid porous polymers based on poly-EGDMA and polylactide containing vancomycin, the concentration of which in the polymer varied by two orders of magnitude, were synthesized. The processes of polymer biodegradation and vancomycin release were studied in the following model media: phosphate-buffered saline (PBS), trypsin-Versene solution, and trypsin-PBS solution. The maximum antibiotic release was recorded during the first 3 h of extraction. The duration of antibiotic escape from the polymer samples in trypsin-containing media varied from 3 to 22 days, depending on the antibiotic content of the polymer. Keeping samples of the hybrid polymer in trypsin-containing model media resulted in acidification of the solutions-after 45 days, up to a pH of 1.84 in the trypsin-Versene solution and up to pH 1.65 in the trypsin-PBS solution. Here, the time dependences of the vancomycin release from the polymer into the medium and the decrease in pH of the medium correlated. These data are also consistent with the results of a study of the dynamics of sample weight loss during extraction in the examined model media. However, while the polymer porosity increased from ~53 to ~60% the pore size changed insignificantly, over only 10 μm. The polymer samples were characterized by their antibacterial activity against Staphylococcus aureus, and this activity persisted for up to 21 days during biodegradation of the material, regardless of the medium type used in model. Surface-dependent human cells (dermal fibroblasts) adhere well, spread out, and maintain high viability on samples of the functionalized hybrid polymer, thus demonstrating its biocompatibility in vitro.