Direct Determination of Absolute Radical Quantum Yields in Hydroxyl and Sulfate Radical-Based Treatment Processes.

The absolute radical quantum yield (Φ) is a critical parameter to evaluate the efficiency of radical-based processes in engineered water treatment. However, measuring Φ is fraught with challenges, as current quantification methods lack selectivity, specificity, and anti-interference capabilities, resulting in significant error propagation. Herein, we report a direct and reliable time-resolved technique to determine Φ at pH 7.0 for commonly used radical precursors in advanced oxidation processes. For H2O2 and peroxydisulfate (PDS), the values of Φ•OH and ΦSO4•- at 266 nm were measured to be 1.10 ± 0.01 and 1.46 ± 0.05, respectively. For peroxymonosulfate (PMS), we developed a new approach to determine Φ•OHPMS with terephthalic acid as a trap-and-trigger probe in the nonsteady state system. For the first time, the Φ•OHPMS value was measured to be 0.56 by the direct method, which is stoichiometrically equal to ΦSO4•-PMS (0.57 ± 0.02). Additionally, radical formation mechanisms were elucidated by density functional theory (DFT) calculations. The theoretical results showed that the highest occupied molecular orbitals of the radical precursors are O-O antibonding orbitals, facilitating the destabilization of the peroxy bond for radical formation. Electronic structures of these precursors were compared, aiming to rationalize the tendency of the Φ values we observed. Overall, this time-resolved technique with specific probes can be used as a reliable tool to determine Φ, serving as a scientific basis for the accurate performance evaluation of diverse radical-based treatment processes.

A screened predictive model for esophageal squamous cell carcinoma based on salivary flora data.

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor of the digestive system in the esophageal squamous epithelium. Many studies have linked esophageal cancer (EC) to the imbalance of oral microecology. In this work, different machine learning (ML) models including Random Forest (RF), Gaussian mixture model (GMM), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM) and extreme gradient boosting (XGBoost) based on Genetic Algorithm (GA) optimization was developed to predict the relationship between salivary flora and ESCC by combining the relative abundance data of Bacteroides, Firmicutes, Proteobacteria, Fusobacteria and Actinobacteria in the saliva of patients with ESCC and healthy control. The results showed that the XGBoost model without parameter optimization performed best on the entire dataset for ESCC diagnosis by cross-validation (Accuracy = 73.50%). Accuracy and the other evaluation indicators, including Precision, Recall, F1-score and the area under curve (AUC) of the receiver operating characteristic (ROC), revealed XGBoost optimized by the GA (GA-XGBoost) achieved the best outcome on the testing set (Accuracy = 89.88%, Precision = 89.43%, Recall = 90.75%, F1-score = 90.09%, AUC = 0.97). The predictive ability of GA-XGBoost was validated in phylum-level salivary microbiota data from ESCC patients and controls in an external cohort. The results obtained in this validation (Accuracy = 70.60%, Precision = 46.00%, Recall = 90.55%, F1-score = 61.01%) illustrate the reliability of the predictive performance of the model. The feature importance rankings obtained by XGBoost indicate that Bacteroides and Actinobacteria are the two most important factors in predicting ESCC. Based on these results, GA-XGBoost can predict and diagnose ESCC according to the relative abundance of salivary flora, providing an effective tool for the non-invasive prediction of esophageal malignancies.