Dimensionally Intact Construction of Ultrathin S-Scheme CuFe2O4/ZnIn2S4 Heterojunctional Photocatalysts for CO2 Photoreduction.

The conversion of CO2 into carbon-neutral fuels such as methane (CH4) through selective photoreduction is highly sought after yet remains challenging due to the slow multistep proton-electron transfer processes and the formation of various C1 intermediates. This research highlights the cooperative interaction between Fe3+ and Cu2+ ions transitioning to Fe2+ and Cu+ ions, enhancing the photocatalytic conversion of CO2 to methane. We introduce an S-scheme heterojunction photocatalyst, CuFe2O4/ZnIn2S4, which demonstrates significant efficiency in CO2 methanation under light irradiation. The CuFe2O4/ZnIn2S4 heterojunction forms an internal electric field that aids in the mobility and separation of exciton carriers under a wide solar spectrum for exceptional photocatalytic performance. Remarkably, the optimal CuFe2O4/ZnIn2S4 heterojunction system achieved an approximately 68-time increase in CO2 conversion compared with ZnIn2S4 and CuFe2O4 nanoparticles using only pure water, with nearly complete CO selectivity and yields of CH4 and CO reaching 172.5 and 202.4 µmol g-1 h-1, respectively, via a 2-electron oxygen reduction reaction (ORR) process. The optimally designed CuFe2O4/ZnIn2S4 heterojunctional system achieved approximately 96% conversion of BA and 98.5% selectivity toward benzaldehyde (BAD). Additionally, this photocatalytic system demonstrated excellent cyclic stability and practical applicability. The photogenerated electrons in the CuFe2O4 conduction band enhance the reduction of Fe3+/Cu2+ to Fe2+/Cu+, creating a microenvironment conducive to CO2 reduction to CO and CH4. Simultaneously, the appearance of holes in the ZnIn2S4 valence band facilitates water oxidation to O2. The synergistic function within the CuFe2O4/ZnIn2S4 heterojunction plays a pivotal role in facilitating charge transfer, accelerating water oxidation, and thereby enhancing CO2 reduction kinetics. This study offers valuable insights and a strategic framework for designing efficient S-scheme heterojunctions aimed at achieving carbon neutrality through solar fuel production.

Engineering MPC-Assisted Heterojunctional Photo-Oxidation Tailored by Interfacial Design of a P-Modulated C3N4 Heterojunction for Improved Aerobic Alcohol Oxidation.

The creation of two-dimensional van der Waals (VDW) heterostructures is a sophisticated approach to enhancing photocatalytic efficiency. However, challenges in electron transfer at the interfaces often arise in these heterostructures due to the varied structures and energy barriers of the components involved. This study presents a novel method for constructing a VDW heterostructure by inserting a phosphate group between copper phthalocyanine (CuPc) and boron-doped, nitrogen-deficient graphitic carbon nitride (BCN), referred to as Cu/PO4-BCN. This phosphate group serves as a charge mediator, enabling effective charge transfer within the heterostructure, thus facilitating electron flow from BCN to CuPc upon activation. As a result, the photogenerated electrons are effectively utilized by the catalytic Cu2+ core in CuPc, achieving a conversion efficiency of 96% for benzyl alcohol (BA) and a selectivity of 98.8% for benzyl aldehyde (BAD) in the presence of oxygen as the sole oxidant and under illumination. Notably, the production rate of BAD is almost 8 times higher than that observed with BCN alone and remains stable over five cycles. The introduction of interfacial mediators to enhance electron transfer represents a pioneering and efficient strategy in the design of photocatalysts, enabling the proficient transformation of BA into valuable derivatives.

Sustainable landfill sites selection using geospatial information and AHP-GDM approach: A case study of Abha-Khamis in Saudi Arabia.

Social, environmental, and technical factors must be combined to solve the complex problem of ever-growing municipal solid waste (MSW) and minimize its negative impact on the environment. Saudi Arabia has launched a US$13 billion tourism strategy to transform the Asir region into a year-round tourist destination and has pledged to welcome 10 million local and foreign visitors by 2030. The estimated share of Abha-Khamis will increase to 7.18 million tons of household waste per year. With a gross domestic product (GDP) of USD 820.00 billion by the end of 2022, Saudi Arabia can no longer afford to neglect the issue of waste production and its safe disposal. In this study, to account for all factors and evaluation criteria, a combination of remote sensing, geographic information systems and an analytical hierarchy process (AHP) was used to determine the best locations for municipal solid waste (MSW) disposal in Abha-Khamis. The analysis revealed that 60% of the study area consists of faults (14.28%), drainage networks (12.80%), urban (11.43%), land use (11.41%) and roads (8.35%), while 40% of the suitable area for landfill. Of these, a total of 20 sites ranging in size from 100 to 595 ha are distributed at reasonable distances from the cities of Abha-Khamis, which meet all the critical criteria for suitable landfill sites mentioned in the literature. Current research shows that the use of integrated remote sensing, GIS and the AHP-GDM approach significantly improves the identification of land suitability for MSW management.

The dynamics of novel corona virus disease via stochastic epidemiological model with vaccination.

During the past two years, the novel coronavirus pandemic has dramatically affected the world by producing 4.8 million deaths. Mathematical modeling is one of the useful mathematical tools which has been used frequently to investigate the dynamics of various infectious diseases. It has been observed that the nature of the novel disease of coronavirus transmission differs everywhere, implying that it is not deterministic while having stochastic nature. In this paper, a stochastic mathematical model has been investigated to study the transmission dynamics of novel coronavirus disease under the effect of fluctuated disease propagation and vaccination because effective vaccination programs and interaction of humans play a significant role in every infectious disease prevention. We develop the epidemic problem by taking into account the extended version of the susceptible-infected-recovered model and with the aid of a stochastic differential equation. We then study the fundamental axioms for existence and uniqueness to show that the problem is mathematically and biologically feasible. The extinction of novel coronavirus and persistency are examined, and sufficient conditions resulted from our investigation. In the end, some graphical representations support the analytical findings and present the effect of vaccination and fluctuated environmental variation.

Ni(II) removal using date seed powder biosorbent: Process parameters classification and RSM modeling.

The effects of pH, particle size, adsorbent mass and stirring time on the adsorption efficiency were investigated. The univariate linear regression algorithm was applied on experimental data to rank the most effective parameters on the Ni(II) removal percentage. Response surface method (RSM) was then applied to model and optimize the operating conditions of the removal process. Results revealed that the most effective operation parameters on Ni(II) removal is the solution's pH. It has been concluded that the highest removal of 94.13% is obtained with stirring time of 29.15 min, particle size 137.81 µm, added mass absorbent of 0.346 g and pH of 12.04. Experimental verification showed removal percentage of 93.5% concluding agreement with that obtained by model prediction.Implications: The removal of Ni(II) ions from wastewater utilizing the agricultural waste of date seed powder is dominated by many parameters; solution pH, initial Ni(II) concentration, adsorbent mass, particle size, operational temperature and contact time. This research classifies these parameters to define the ones that significantly impacts the removal process. Modeling of these parameters was then conducted to study the impact of every set on the removal efficiency thus defining the optimum operating conditions. The findings of this study can be used to create optimal operating conditions that are capable of achieving higher removal percentages than are currently available.