The release of wastewater especially organic dyes from numerous industries is a big problem of the modern era due to its carcinogenic and other health hazard effects. Therefore, ternary ferrites chitosan microspheres (TFCM) were synthesized by a facile approach as an efficient visible-light photocatalyst for the removal of these toxic dyes. The crystallite size of synthesized ternary ferrite nanoparticles (TFNPs) was 10 nm confirmed through XRD using Scherer’s equation. The EDS spectrum clearly showed the elemental composition of TFNPs. The morphology of TFCM showed a smooth surface with a microspheres size of $923\mathrm{\mu }\mathrm{m}$ based on SEM micrographs. The mass loss in thermogravimetric analysis supports the presence of TFNPs in the chitosan matrix and the DSC showed different crystalline endothermic ranges for the TFNPs and the chitosan matrix. The bandgap of the photocatalyst was found in the visible region (2.36 eV) obtained from Tauc’s plot. The TFCM showed 93% photocatalytic efficiency for Methylene Blue (MB) dye at optimum conditions of pH 7.0, catalyst dosage 0.7 g, and initial dye concentration of 50 ppm in 150 min of sunlight irradiation. The experiment was carried out using preliminary screening followed by the optimization process using response surface methodology. The reduced quadratic model was considered as the most suitable model for degradation, showing highly significant p values and significant interaction among the different variable factors. In conclusion, the newly developed photocatalyst ought to be a better candidate for the decontamination of dyes from wastewater.

由于其致癌作用和其他健康危害作用，从众多行业中释放废水，尤其是有机染料是现代的一个大问题。因此，通过简便的方法合成了三价铁氧体壳聚糖微球（TFCM），作为去除这些有毒染料的有效可见光光催化剂。合成的三价铁氧体纳米颗粒（TFNPs）的微晶尺寸为10 nm，通过XRD使用Scherer方程确定。EDS光谱清楚地显示了TFNP的元素组成。TFCM的形态显示表面光滑，微球尺寸为$923\mathrm{\mu }\mathrm{米}$根据SEM显微照片。热重分析中的质量损失支持在壳聚糖基质中存在TFNP，而DSC显示TFNP和壳聚糖基质的晶体吸热范围不同。从Tauc曲线获得的可见区（2.36 eV）中发现了光催化剂的带隙。TFCM对亚甲基蓝（MB）染料的最佳催化条件为pH 7.0，催化剂用量0.7 g和在150分钟的阳光照射下初始染料浓度为50 ppm时显示出93％的光催化效率。实验使用初步筛选进行，然后使用响应面方法进行优化。简化的二次模型被认为是最适合的退化模型，显示出非常显着的p值和不同变量因子之间的显着相互作用。