The presence of inorganic pollutants such as Cadmium(II) and Chromium(VI) could destroy our environment and ecosystem. To overcome this problem, much attention was directed to microbial technology, whereas some microorganisms could resist the toxic effects and decrease pollutants concentration while the microbial viability is sustained. Therefore, we built up a complementary strategy to study the biofilm formation of isolated strains under the stress of heavy metals. As target resistive organisms, Rhizobium-MAP7 and Rhodotorula ALT72 were identified. However, Pontoea agglumerans strains were exploited as the susceptible organism to the heavy metal exposure. Among the methods of sensing and analysis, bioelectrochemical measurements showed the most effective tools to study the susceptibility and resistivity to the heavy metals. The tested Rhizobium strain showed higher ability of removal of heavy metals and more resistive to metals ions since its cell viability was not strongly inhibited by the toxic metal ions over various concentrations. On the other hand, electrochemically active biofilm exhibited higher bioelectrochemical signals in presence of heavy metals ions. So by using the two strains, especially Rhizobium-MAP7, the detection and removal of heavy metals Cr(VI) and Cd(II) is highly supported and recommended.

Heavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and severe health concerns. The current study is of supreme priority because there is no previous work that dealt with the modeling of the optimization of the biosorption process by the immobilized cells. The significant parameters (immobilized bacterial cells, contact time, and initial Cr⁶⁺ concentrations), affecting Cr⁶⁺ biosorption by immobilized Pseudomonas alcaliphila, was verified, using the Plackett–Burman matrix. For modeling the maximization of Cr⁶⁺ biosorption, a comparative approach was created between rotatable central composite design (RCCD) and artificial neural network (ANN) to choose the most fitted model that accurately predicts Cr⁶⁺ removal percent by immobilized cells. Experimental data of RCCD was employed to train a feed-forward multilayered perceptron ANN algorithm. The predictive competence of the ANN model was more precise than RCCD when forecasting the best appropriate wastewater treatment. After the biosorption, a new shiny large particle on the bead surface was noticed by the scanning electron microscopy, and an additional peak of Cr⁶⁺ was appeared by the energy dispersive X-ray analysis, confirming the role of the immobilized bacteria in the biosorption of Cr⁶⁺ ions.

Due to increasing concerns about environmental impact and toxicity, developing green and sustainable methods for nanoparticle synthesis is attracting significant interest. This work reports the successful green synthesis of silver (Ag), silver-titanium dioxide (Ag@TiO₂), and silver-selenium dioxide (Ag@SeO₂) nanoparticles (NPs) using Beta vulgaris L. extract. Characterization by XRD, SEM, TEM, and EDX confirmed the successful formation of uniformly distributed spherical NPs with controlled size (25 ± 4.9 nm) and desired elemental composition. All synthesized NPs and the B. vulgaris extract exhibited potent free radical scavenging activity, indicating significant antioxidant potential. However, Ag@SeO₂ displayed lower hemocompatibility compared to other NPs, while Ag@SeO₂ and the extract demonstrated reduced inflammation in a carrageenan-induced paw edema animal model. Interestingly, Ag@TiO₂ and Ag@SeO₂ exhibited strong antifungal activity against Rhizoctonia solani and Sclerotia sclerotium, as evidenced by TEM and FTIR analyses. Generally, the findings suggest that B. vulgaris-derived NPs possess diverse biological activities with potential applications in various fields such as medicine and agriculture. Ag@TiO₂ and Ag@SeO₂, in particular, warrant further investigation for their potential as novel bioactive agents.

Biosynthesis of Ag@SeO₂ NC using turmeric extract with promising antibacterial and antioxidant properties, optimized using AI, and confirmed by various techniques.