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.

Correlated stochastic epidemic model for the dynamics of SARS-CoV-2 with vaccination

In this paper, we propose a mathematical model to describe the influence of the SARS-CoV-2 virus with correlated sources of randomness and with vaccination. The total human population is divided into three groups susceptible, infected, and recovered. Each population group of the model is assumed to be subject to various types of randomness. We develop the correlated stochastic model by considering correlated Brownian motions for the population groups. As the environmental reservoir plays a weighty role in the transmission of the SARS-CoV-2 virus, our model encompasses a fourth stochastic differential equation representing the reservoir. Moreover, the vaccination of susceptible is also considered. Once the correlated stochastic model, the existence and uniqueness of a positive solution are discussed to show the problem’s feasibility. The SARS-CoV-2 extinction, as well as persistency, are also examined, and sufficient conditions resulted from our investigation. The theoretical results are supported through numerical/graphical findings.

Modeling the dynamics of coronavirus with super-spreader class: A fractal-fractional approach

Super-spreaders of the novel coronavirus disease (or COVID-19) are those with greater potential for disease transmission to infect other people. Understanding and isolating the super-spreaders are important for controlling the COVID-19 incidence as well as future infectious disease outbreaks. Many scientific evidences can be found in the literature on reporting and impact of super-spreaders and super-spreading events on the COVID-19 dynamics. This paper deals with the formulation and simulation of a new epidemic model addressing the dynamics of COVID-19 with the presence of super-spreader individuals. In the first step, we formulate the model using classical integer order nonlinear differential system composed of six equations. The individuals responsible for the disease transmission are further categorized into three sub-classes, i.e., the symptomatic, super-spreader and asymptomatic. The model is parameterized using the actual infected cases reported in the kingdom of Saudi Arabia in order to enhance the biological suitability of the study. Moreover, to analyze the impact of memory index, we extend the model to fractional case using the well-known Caputo-Fabrizio derivative. By making use of the Picard-Lindelöf theorem and fixed point approach, we establish the existence and uniqueness criteria for the fractional-order model. Furthermore, we applied the novel fractal-fractional operator in Caputo-Fabrizio sense to obtain a more generalized model. Finally, to simulate the models in both fractional and fractal-fractional cases, efficient iterative schemes are utilized in order to present the impact of the fractional and fractal orders coupled with the key parameters (including transmission rate due to super-spreaders) on the pandemic peaks.

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.

Mathematical assessment of constant and time-dependent control measures on the dynamics of the novel coronavirus: An application of optimal control theory

The coronavirus infectious disease (COVID-19) is a novel respiratory disease reported in 2019 in China. The COVID-19 is one of the deadliest pandemics in history due to its high mortality rate in a short period. Many approaches have been adopted for disease minimization and eradication. In this paper, we studied the impact of various constant and time-dependent variable control measures coupled with vaccination on the dynamics of COVID-19. The optimal control theory is used to optimize the model and set an effective control intervention for the infection. Initially, we formulate the mathematical epidemic model for the COVID-19 without variable controls. The model basic mathematical assessment is presented. The nonlinear least-square procedure is utilized to parameterize the model from actual cases reported in Pakistan. A well-known technique based on statistical tools known as the Latin-hypercube sampling approach (LHS) coupled with the partial rank correlation coefficient (PRCC) is applied to present the model global sensitivity analysis. Based on global sensitivity analysis, the COVID-19 vaccine model is reformulated to obtain a control problem by introducing three time dependent control variables for isolation, vaccine efficacy and treatment enhancement represented byu 1 ( t ) ,u 2 ( t ) andu 3 ( t ) , respectively. The necessary optimality conditions of the control problem are derived via the optimal control theory. Finally, the simulation results are depicted with and without variable controls using the well-known Runge-Kutta numerical scheme. The simulation results revealed that time-dependent control measures play a vital role in disease eradication.