This study demonstrates the capability of noble metal nanoparticles immobilized on Al2O3 or TiO2 support to effectively activate peroxymonosulfate (PMS) and degrade select organic compounds in water. The noble metals outperformed a benchmark PMS activator such as Co(2+) (water-soluble) for PMS activation and organic compound degradation at acidic pH and showed the comparable activation capacity at neutral pH. The efficiency was found to depend on the type of noble metal (following the order of Pd > Pt ≈ Au ≫ Ag), the amount of noble metal deposited onto the support, solution pH, and the type of target organic substrate. In contrast to common PMS-activated oxidation processes that involve sulfate radical as a main oxidant, the organic compound degradation kinetics were not affected by sulfate radical scavengers and exhibited substrate dependency that resembled the PMS activated by carbon nanotubes. The results presented herein suggest that noble metals can mediate electron transfer from organic compounds to PMS to achieve persulfate-driven oxidation, rather than through reductive conversion of PMS to reactive sulfate radical.

Given our aging society and the prevalence of age-related hearing loss that often develops during adulthood, hearing loss is a common public health issue affecting almost all older adults. Moderate-to-moderately severe hearing loss can usually be corrected with hearing aids; however, severe-to-profound hearing loss often requires a cochlear implant (CI). However, post-operative CI results vary, and the performance of the previous prediction models is limited, indicating that a new approach is needed. For postlingually deaf adults (n de120) who received CI with full insertion, we predicted CI outcomes using a Random-Forest Regression (RFR) model and investigated the effect of preoperative factors on CI outcomes. Postoperative word recognition scores (WRS) served as the dependent variable to predict. Predictors included duration of deafness (DoD), age at CI operation (ageCI), duration of hearing-aid use (DoHA), preoperative hearing threshold and sentence recognition score. Prediction accuracy was evaluated using mean absolute error (MAE) and Pearson’s correlation coefficient r between the true WRS and predicted WRS. The fitting using a linear model resulted in prediction of WRS with r = 0.7 and MAE = 15.6 ± 9. RFR outperformed the linear model (r = 0.96, MAE = 6.1 ± 4.7, p < 0.00001). Cross-hospital data validation showed reliable performance using RFR (r = 0.91, MAE = 9.6 ± 5.2). The contribution of DoD to prediction was the highest (MAE increase when omitted: 14.8), followed by ageCI (8.9) and DoHA (7.5). After CI, patients with DoD < 10 years presented better WRSs and smaller variations (p < 0.01) than those with longer DoD. Better WRS was also explained by younger age at CI and longer-term DoHA. Machine learning demonstrated a robust prediction performance for CI outcomes in postlingually deaf adults across different institutes, providing a reference value for counseling patients considering CI. Health care providers should be aware that the patients with severe-to-profound hearing loss who cannot have benefit from hearing aids need to proceed with CI as soon as possible and should continue using hearing aids until after CI operation.

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