Sunlight assisted photocatalytic degradation using the RSM-CCD optimized sustainable photocatalyst synthesized from galvanic wastewater

Central composite design and mechanism of antibiotic ciprofloxacin photodegradation under visible light by green hydrothermal synthesized cobalt-doped zinc oxide nanoparticles

In this research, different Co2+ doped ZnO nanoparticles (NPs) were hydrothermally synthesized by an environmentally friendly, sustainable technique using the extract of P. capillacea for the first time. Co-ZnO was characterized and confirmed by FTIR, XPS, XRD, BET, EDX, SEM, TEM, DRS UV-Vis spectroscopy, and TGA analyses. Dislocation density, micro strains, lattice parameters and volume of the unit cell were measured using XRD results. XRD suggests that the average size of these NPs was between 44.49 and 65.69 nm with a hexagonal wurtzite structure. Tauc plot displayed that the optical energy bandgap of ZnO NPs (3.18) slowly declines with Co doping (2.96 eV). Near complete removal of the ciprofloxacin (CIPF) antibiotic was attained using Green 5% of Hy-Co-ZnO in the existence of visible LED light which exhibited maximum degradation efficiency (99%) within 120 min for 30 ppm CIPF initial concentration. The photodegradation mechanism of CIPF using Green Hy-Co-ZnO NPs followed the Pseudo-first-order kinetics. The Green Hy-Co-ZnO NPs improved photocatalytic performance toward CIPF for 3 cycles. The experiments were designed using the RSM (CCD) method for selected parameters such as catalyst dosage, antibiotic dosage, shaking speed, and pH. The maximal CIPF degradation efficiency (96.4%) was achieved under optimum conditions of 39.45 ppm CIPF dosage, 60.56 mg catalyst dosage, 177.33 rpm shaking speed and pH 7.57.

Develop a hybrid machine learning model for promoting microbe biomass production

Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it's a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid machine learning approach (i.e., ANFIS-NM) to identify the potent factors and optimize the cultivation conditions of A. cinnamomea based on a 32 fractional factorial design with seven factors. The results indicate that the ANFIS-NM approach successfully identified three key factors (i.e., glucose, potato dextrose broth, and agar) and significantly boosted mycelia yield. The interpretability of ANFIS rules made the cultivation conditions visually interpretable. Subsequently, a three-factor five-level central composite design was used to probe the optimal yield. This study demonstrates the proposed hybrid machine learning approach could significantly reduce the time consumption in laboratory cultivation and increase mycelia yield that meets SDGs 7 and 12, hitting a new milestone for biomass production.