Quantum dots (QDs) with unique properties have been widely employed for energy, environmental, and health care applications. Green natural resources, because of their renewability, sustainability, abundance, low cost, and environmental-friendliness, can be utilized for the synthesis of diverse nanoarchitectures. This critical review highlights the recent advancements in the greener and sustainable synthesis of carbon, graphene and metal-based QDs, as well as their significant environmental applications such as photocatalytic hydrogen production, degradation of hazardous contaminants/pollutants and reduction of CO₂, with emphasis on the important challenges and future perspectives.

Tetracyclines (TCs) have been extensively used for humans and animal diseases treatment and livestock growth promotion. The consumption of such antibiotics has been ever-growing nowadays due to various bacterial infections and other pathologic conditions, resulting in more discharge into the aquatic environments. This brings threats to ecosystems and human bodies. Up to now, several attempts have been made to reduce TC amounts in the wastewater, among which photocatalysis, an advanced oxidation process, is known as an eco-friendly and efficient technology. In this regard, metal organic frameworks (MOFs) have been known as the promising materials as photocatalysts. Thus, studying TC photocatalytic degradation by MOFs would help scientists and engineers optimize the process in terms of effective parameters. Nevertheless, the costly and time-consuming experimental methods, having instrumental errors, encouraged the authors to use the computational method for a more comprehensive assessment. In doing so, a wide-ranging databank including 374 experimental data points was gathered from the literature. A powerful machine learning method of Gaussian process regression (GPR) model with four kernel functions was proposed to estimate the TC degradation in terms of MOFs features (surface area and pore volume) and operational parameters (illumination time, catalyst dosage, TC concentration, pH). The GPR models performed quite well, among which GPR-Matern model shows the most accurate performance with R², MRE, MSE, RMSE, and STD of 0.981, 12.29, 18.03, 4.25, and 3.33, respectively. In addition, an analysis of sensitivity was carried out to assess the effect of the inputs on the TC photodegradation by MOFs. It revealed that the illumination time and the surface area play a significant role in the decomposition activity.

In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO₂ into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO₂. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO₂ adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO₂ adsorption will open new doors for their further application in CO₂ separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO₂ adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO₂ uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R² value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO₂ adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO₂ adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.

In this research work, the photochemical impact of Fe(III)–nitrilotriacetic acid complex (FeNTA) on the fate of an organic pollutant (4-chlorophenol (4-CP)) was investigated in natural waters. The quantum yields of the photodecomposition of the FeNTA complex and of Fe(II) formation, by an intra-molecular photoredox process (the first stage of the reaction) are high. This photoredox reaction represents the first step of the process leading to 4-CP disappearance. Whereas oxygen does not affect FeNTA photodegradation, 4-CP depletion requires the presence of oxygen. The radical species HO˙ and CO₃˙⁻ responsible of the degradation were identified by ESR spectroscopy under irradiation. Two different wavelength-dependent mechanisms of 4-chlorophenol degradation are proposed. It clearly appears that under solar irradiation, iron organic complexes like FeNTA can play a significant role on the fate of the organic compounds present in natural waters.