In Situ Detoxification of Venomous Agent X Surrogate Profenofos by Doped Titanium Dioxide Nanoparticles under Illumination at the UV and Visible Ranges

Light harvesting enhancement through band structure engineering in graphite carbon nitride / polydopamine nanocomposite photocatalyst: Addressing persistent organophosphorus pesticide pollution in water systems

Organophosphorus pesticides, particularly profenofos (PF), pose a significant threat to the food supply and human health due to their persistence, toxicity, and resistance to natural breakdown processes. An urgent need exists for an environmentally friendly solution, and photocatalysis emerges as a practical, cost-effective option. However, challenges like poor light responsiveness and difficulties in material separation and reusability persist. To address these issues, we developed a nanocomposite consisting of graphite carbon nitride (g-C3N4) doped with polydopamine (pDA) through a hydrothermal synthesis method. This innovative nanocomposite was employed as a photocatalyst to degrade PF. Various analytical techniques, including UV-DRS, FT-IR, XRD, HR-TEM, and EDAX, were utilized to characterize the synthesized nanocomposite. The strategically modulated band gaps of the nanocomposite enable efficient absorption of UV light, facilitating the robust photocatalytic degradation of PF (96.4%). Our study explored photodegradation using different g-C3N4/pDA catalyst dosages, varied PF concentrations, and pH levels (3, 5, 9, and 11) under UV light. Our findings promise applications in wastewater management, offering an efficient catalyst for PF degradation. This marks a significant stride in addressing challenges related to pesticide pollution in the environment.

Cyanidin-3-O-glucoside mediated photodegradation of profenofos in water

Flavonoids can sensitize and quench the photolysis of pesticides such as profenofos in surface water. Profenofos has been frequently detected in surface and underground water. The present study investigated the photolysis of profenofos under various conditions, including natural and artificial light illumination, with and without cyanidin-3-O-glucoside (Cy3G) and in pure and surface water. The degradation half-lives of profenofos in distilled water with 10 equivalents Cy3G of profenofos were 21.7 min, 9.5 h, 12.5 h and 180 h under high-pressure mercury light, UV, Xenon lamp and solar irradiation, respectively, while those without Cy3G were 8.1 min, 6.1 h, 8.2 h and 89.9 h, respectively. The photolysis rate of profenofos under sunlight and artificial light was reduced by 1.5-2.7 times due to Cy3G, compared to the Cy3G-free control. Under sunlight irradiation, the effects of Cy3G on profenofos photolysis were larger than those under high-pressure mercury lamp irradiation. Cy3G also significantly reduced the photolysis rate of profenofos under different pH conditions and in natural water. In addition, Cy3G exhibited a significant capacity of scavenging hydroxyl radicals and quenching ¹O₂ in water. The effect of Cy3G on profenofos photolysis was demonstrated through their interrelations in the natural environment. These findings can help understanding of the effect of flavonoids on profenofos photolysis and are of significance for predicting the degradation kinetics of profenofos and accurately assessing its potential biological impacts.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Quantitative structure-property relationship of the photoelectrochemical oxidation of phenolic pollutants at modified nanoporous titanium oxide using supervised machine learning

Here we report on an advanced photoelectrochemical (PEC) oxidation of 22 phenolic pollutants based on modified nanoporous TiO2, which was directly grown on a titanium substrate electrochemically. Their degradation rate constants were experimentally determined and their physicochemical properties were computaionally calculated. The quantitative structure-property relationship (QSPR) was elucidated by employing multiple linear regression (MLR) method. A supervised machine learning approach was employed to build QSPR models. The high predictive abilities of the QSPR model were validated via leave-one-out (LOO) method and a strict regimen of statistical validation tests. The significant descriptors identified in the QSPR Model for the phenolic compounds were also assessed using a typical dye pollutant Rhodamine B, further confirming the high effectiveness and predictability of the optimized model. Our study has shown that the integrated effect of the structural, hydrophobic and topological properties along with electronic property should be considered in order to design an efficient PEC catalytic approach for environmental applications.