RESUMEN
In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization.
RESUMEN
The development of red emission carbon dots with bright solid-state fluorescence would significantly broaden their application in optoelectronic devices and sensors. Herein, a red-emissive carbon dot-based nanocomposite has been synthesized through chemical bonding with cellulose films. The red emission originating from the surface states of carbon dots was maintained in the cellulose films. Due to the stable chemical bonding, the photoluminescence intensity and emission wavelength remained unchanged for 12 months, and the quantum yield of the composite was enhanced over 4 times. It also showed outstanding stability in water or weak acid-base environments under pHs ranging from 2 to 11. Therefore, the mechanism of chemical bonding that eliminated the defects and preserved the efficient radiative process through surface states was proposed.
RESUMEN
Carbonized polymer dots (CPDs) have received tremendous attention during the last decade due to their excellent fluorescent properties and catalytic performance. Doping CPDs with transition metal atoms accelerates the local electron flow in CPDs and improves the fluorescent properties and catalytic performance of the CPDs. However, the binding sites and the formation mechanisms of the transition-metal-atom-doped CPDs remain inconclusive. In this work, Mn2+ -ion-doped CPDs (Mn-CPDs) are synthesized by the hydrothermal method. The Mn2+ ions form MnO bonds that bridge the sp2 domains of carbon cores and increases the effective sp2 domains in the Mn-CPDs, which redshifts the fluorescence emission peak of the Mn-CPDs slightly. The Mn2+ ions form covalent bonds in the CPDs and remedy the oxygen vacancies of the CPDs, which cuts off the non-radiative-recombination process of the Mn-CPDs and increases the quantum yield of the Mn-CPDs to 70%. Furthermore, the MnO bonds accelerate the electron flow between adjacent sp2 domains and enhances the electron transport in the Mn-CPDs. Thus, the Mn-CPDs demonstrate excellent catalytic performance to activate hydrogen peroxide (H2 O2 ) and produce hydroxyl radicals (â¢OH) to degrade methylene blue (MB) and rhodamine B (RhB).