This study investigated a ternary CdS/TiO₂/g-C₃N₄ heterojunction for degrading synthetic dyes and hydrogen production from aqueous media through visible light-initiated photocatalytic reactions. CdS, TiO₂, and g-C₃N₄ were combined in different mass ratios through a simple hydrothermal method to create CdS/TiO₂/g-C₃N₄ composite photocatalysts. The prepared heterojunction catalysts were investigated by using FTIR, XRD, EDX, SEM, and UV-visible spectroscopy analysis for their crystal structures, functional groups, elemental composition, microtopography, and optical properties. The rhodamine B dye was then degraded by using fully characterized photocatalysts. The maximum dye degradation efficiency of 99.4% was noted in these experiments. The evolution rate of hydrogen from the aqueous solution with the CdS/TiO₂/g-C₃N₄ photocatalyst remained 2910 μmol·h^-1·g^-1, which is considerably higher than those of g-C₃N₄, CdS, CdS/g-C₃N₄, and g-C₃N₄/TiO₂-catalyzed reactions. This study also proposes a photocatalytic activity mechanism for the tested ternary CdS/TiO₂/g-C₃N₄ heterojunctions.

This study performed in-situ microwave pyrolysis of plastic waste into hydrogen, liquid fuel and carbon nanotubes in the presence of Zeolite Socony Mobil ZSM-5 catalyst. In the presented microwave pyrolysis of plastics, activated carbon was used as a heat susceptor. The microwave power of 1 kW was employed to decompose high-density polyethylene (HDPE) and polypropylene (PP) wastes at moderate temperatures of 400–450 °C. The effect of plastic composition, catalyst loading and plastic type on liquid, gas and solid carbon products was quantified. This in-situ CMP reaction resulted in heavy hydrocarbons, hydrogen gas and carbon nanotubes as a solid residue. A relatively better hydrogen yield of 129.6 mmol/g as a green fuel was possible in this process. FTIR and gas chromatography analysis revealed that liquid product consisted of C₁₃₊ fraction hydrocarbons, such as alkanes, alkanes, and aromatics. TEM micrographs showed tubular-like structural morphology of the solid residue, which was identified as carbon nanotubes (CNTs) during X-ray diffraction analysis. The outer diameter of CNTs ranged from 30 to 93 nm from HDPE, 25–93 nm from PP and 30–54 nm for HDPE-PP mixure. The presented CMP process took just 2–4 min to completely pyrolyze the plastic feedstock into valuable products, leaving no polymeric residue.

This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010–2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan’s Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.

The city of Jeddah experienced a severe flood in 2020, resulting in loss of life and damage to property. In such scenarios, a flood forecasting model can play a crucial role in predicting flood events and minimizing their impact on communities. The proposed study aims to evaluate the performance of machine learning algorithms in predicting floods and non-flood regions, including Gradient Boosting, Extreme Gradient Boosting, AdaBoosting Gradient, Random Forest, and the Light Gradient Boosting Machine, using the dataset from Jeddah City, Saudi Arabia. This study identified fourteen continuous parameters and various classification variables to assess the correlation between these variables and flooding incidents in the analyzed region. The performance of the proposed algorithms was measured using classification matrices and regression matrices. The highest accuracy (86%) was achieved by the Random Forest classifier, and the lowest error rate (0.06) was found with the Gradient Boosting regressor machine. The performance of other algorithms was also exceptional compared to existing literature. The results of the study suggest that the application of these machine learning algorithms can significantly enhance flood prediction accuracy, enabling various industries and sectors to make more informed decisions.

ZnO and black TiO₂ have been selected as the most efficient materials for organic pollution abatement due to their increased efficiency when compared to other materials. However, the concept of green chemistry makes it desirable to design green synthesis approaches for their production. In this study, black TiO₂ was synthesized using an environmentally safe synthetic technique with glycerol as a reductant. ZnO was prepared by using ionic-liquid-based microwave-assisted extracts of Polygonum minus. To investigate the materials' potential to photodegrade organic pollutants, methylene blue (MB) and phenol were chosen as model organic pollutants. Both materials were found to exhibit spherical morphologies and a mesoporous structure and were efficient absorbers of visible light. ZnO exhibited electron-hole pair recombination lower than that of black TiO₂. Black TiO₂ was discovered to be an anatase phase, whereas ZnO was found to have a hexagonal wurtzite structure. In contrast to black TiO₂, which had a surface area of 239.99 m²/g and a particle size of 28 nm, ZnO had a surface area of 353.11 m²/g and a particle size of 32 nm. With a degradation time of 60 min, ZnO was able to eliminate 97.50% of the 40 mg/L MB. Black TiO₂, on the other hand, could reduce 90.0% of the same amount of MB in 60 min. When tested for phenol degradation, ZnO and black TiO₂ activities were reduced by nearly 15 and 25%, respectively. A detailed examination of both ZnO and black TiO₂ materials revealed that ZnO has more potential and versatility for the degradation of organic pollutants under visible light irradiation.