Degradation of cefixime by photocatalysis via Ba-doped BiFeO3 nanomaterial using RSM analysis under LED light source.

In the current work, Response Surface Methodology (RSM)-a statistical method-is used to optimize procedures like photocatalysis with the least amount of laboratory testing. However, to determine the most effective model for achieving the maximum rate of removal efficiency, the Response Surface Methodology was employed. The Ba-doped BiFeO3 photocatalyst was synthesized by the co-precipitation method, and its intrinsic properties were investigated by utilizing a range of spectroscopic techniques, such as FESEM, EDX, XRD, FTIR, and UV-vis. Herein, four independent factors such as, pH, contact time, pollutant concentration, and catalyst dosage were chosen. The results revealed that under acidic conditions with a contact duration of 2 min, a moderate catalyst dosage, and higher pollutant concentration, a degradation rate of 89.8% was achieved. The regression coefficient (R2) and probability value (P) were determined to be 0.99551 and 0.0301, respectively, therefore confirming the excellent fit of the RSM model. Furthermore, this research investigated the potential photocatalytic degradation mechanisms of cefixime, demonstrating that the removal efficiency of cefixime is greatly influenced by the functional parameters.

A response surface modeling and optimization of photocatalytic degradation of 2,4-dichlorophenol in water using hierarchical nano-assemblages of CuBi2O4 particles.

Photocatalytic degradation, as an advanced oxidation process (AOPs), offers a great advantage to target persistent organic pollutants (POPs) in water. RSM in the present study which is statistical means for optimizing processes like photocatalysis with minimum laboratory experimentation. RSM has a history of being a potent design experiment tool for creating new processes, modifying their designs, and optimizing their performances. Herein, a highly sought-after, easily preparable, visible-light active, copper bismuth oxide (CuBi2O4) is applied against a toxic emerging contaminant, 2,4-dichlorophenol (2,4-DCP) under an LED light source (viible light λ > 420 nm). A simple coprecipitation method was adopted to synthesize CuBi2O4 and later analyzed with FESEM, EDX, XRD, FTIR, and spectroscopy to determine its intrinsic properties. Principally, the photocatalytic degradation investigations were based on response surface methodology (RSM), which is a commanding tool in the optimization of the processes. The 2,4-DCP concentration (pollutant loading), CuBi2O4 dosage (catalyst dosge), contact time, and pH were the chosen as dependent factors, that were optimized. However, under optimal conditions, the CuBi2O4 nanoparticle showed a remarkable photocatalytic performance of 91.6% at pH = 11.0 with a pollutant concentration of 0.5 mg/L and a catalyst dose of 5 mg/L within 8 h. The obtained RSM model showed a satisfactory correlation between experimental and predicted values of 2,4-DCP removal, with an agreeable probability value (p) of 0.0069 and coefficient of regression (R2) of 0.990. It is therefore anticipated that the study may open up new possibilities for formulating a plan to specifically target these organic pollutants. In addition, CuBi2O4 possessed fair reusability for three-consequent cycles. Hence, the as-synthesized nanoparticles applied for photocatalysis foster a fit-for-purpose and reliable system in the decontamination of 2,4 DCP in environmental samples, and also the study highlights the efficient use of RSM for environmental remediation, particularly in AOP implementation.

Coprecipitation aided synthesis of bimetallic silver tungstate: a response surface simulation of sunlight-driven photocatalytic removal of 2,4-dichlorophenol.

In the present study, the response surface methodology (RSM) model was used to investigate the photocatalytic performance of silver tungstate (Ag2WO4) in the removal of 2,4-dichlorophenol (2,4-DCP) under natural sunlight. The Ag2WO4 which has nanoflower-like structure was synthesized by a coprecipitation method. The synthesized photocatalyst was characterized for FESEM, TEM, EDX, XRD, FTIR, and UV-Vis spectroscopy. RSM was employed to scrutinize the suitable model to yield a profound pollutant removal rate. The four independent factors such as pollutant concentration, catalyst dosage, pH, and contact time are simulated using RSM. A total of 91% of 2,4-DCP degradation was achieved at a higher catalyst dosage and lower pollutant concentration with a contact duration of 8 h in an alkaline pH condition. The coefficient of regression (R2) and probability value (P) were 0.98 and 0.0472, respectively, which confirmed the ideality of RSM modeling. The study discusses on the possible photocatalytic degradation mechanisms of 2,4-DCP. The results showed a significant dependence of the photocatalytic removal of 2,4-DCP on the functional parameters.