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.

Chromium is one of the heavy metal pollutants that causing risky health issues when discharged into the aquatic ecosystems. The current investigation focused on the bioremoval of Cr 6+ depending on the bacterial sorption process by using Pseudomonas sp. NEWG-2 which was identified on the basis of morphological, cultural characteristics, 16S rRNA sequencing and phylogenetic analysis as Pseudomonas alcaliphila strain NEWG-2. It is clear from the FCCD experiments that the bacterium can grow normally and remove 96.60% of 200 mg/l of Cr 6+ using yeast extract (5.6 g/l), glucose (4.9 g/l), pH (7) for 48 h incubation period. SEM and EDS analyses proved that the Cr 6+ was biosorbed by P. alcaliphila NEWG-2. FTIR spectra indicated that the phenolic, carbonyl ester, acetyl, carboxylate, alkanes and carbonyl were the main groups involved in the chromium biosorption. Of the equilibrium isotherms models, the Langmuir model was more obedient, with a maximum uptake ( q max ) of 10 mg/g (bacterial-alginate beads), than the Freundlich one. The findings reveal the efficiency of P. alcaliphila NEWG-2 in Cr 6+ biosorption, with feasibility in the treatment of chromium-contaminated water as a green-technology tool. Interestingly, to the best of our knowledge, this is the first report on Cr 6+ biosorption process by P. alcaliphila .

Heavy metals are environmental pollutants affect the integrity and distribution of living organisms in the ecosystem and also humans across the food chain. The study targeted the removal of copper (Cu2+) from aqueous solutions, depending on the biosorption process. The bacterial candidate was identified using 16S rRNA sequencing and phylogenetic analysis, in addition to morphological and cultural properties as Azotobacter nigricans NEWG-1. The Box-Behnken design was applied to optimize copper removal by Azotobacter nigricans NEWG-1 and to study possible interactive effects between incubation periods, pH and initial CuSO4 concentration. The data obtained showed that the maximum copper removal percentage of 80.56% was reached at run no. 12, under the conditions of 200 mg/L CuSO4, 4 days’ incubation period, pH, 8.5. Whereas, the lowest Cu2+ removal (12.12%) was obtained at run no.1. Cells of Azotobacter nigricans NEWG-1 before and after copper biosorption were analyzed using FTIR, EDS and SEM. FTIR analysis indicates that several functional groups have participated in the biosorption of metal ions including hydroxyl, methylene, carbonyl, carboxylate groups. Moreover, the immobilized bacterial cells in sodium alginate-beads removed 82.35 ± 2.81% of copper from the aqueous solution, containing an initial concentration of 200 mg/L after 6 h. Azotobacter nigricans NEWG-1 proved to be an efficient biosorbent in the elimination of copper ions from environmental effluents, with advantages of feasibility, reliability and eco-friendly.

The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the biosorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Factors such as peptone, incubation time, pH, glycerol, glucose, K2HPO4, and initial cobalt had a significant effect on the biosorption process. MgSO4 was the only insignificant factor. The DSD model was invalid and could not forecast the prediction of Co(II) removal, owing to the significant lack-of-fit (P < 0.0001). Decisively, the prediction ability of ANN was accurate with a prominent response for training (R2 = 0.9779) and validation (R2 = 0.9773) and lower errors. Applying the optimal levels of the tested variables obtained by the ANN model led to 96.32 ± 2.1% of cobalt bioremoval. During the biosorption process, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy confirmed the sorption of Co(II) ions by P. alcaliphila. FTIR indicated the appearance of a new stretching vibration band formed with Co(II) ions at wavenumbers of 562, 530, and 531 cm–1. The symmetric amino (NH2) binding was also formed due to Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the biosorption of Co(II) by P. alcaliphila via DSD or ANN paradigm.

Our present study was designed to investigate the role of both Trichoderma harzianum and chamomile (Matricaria chamomilla L.) flower extract in mutual reaction against growth of Pythium ultimum. In vitro, the activity of chamomile extract was found to reduce the radial growth of Pythium ultimum up to 30% compared to the control. Whereas, the radial growth reduction effect of T. harzianum against P. ultimum reached 81.6% after 120 h. Data also showed the productivity of total phenolics and total flavonoids by T. harzianum, was 12.18 and 6.33 mg QE/100 mL culture filtrate, respectively. However, these compounds were determined in chamomile flower extract at concentrations of 75.33 and 24.29 mg QE/100 mL, respectively. The fractionation of aqueous extract of chamomile flower using HPLC provided several polyphenolic compounds such as pyrogallol, myricetin, rosemarinic acid, catechol, p-coumaric acid, benzoic acid, chlorogenic acid and other minor compounds. In vivo, the potentiality of T. harzianum with chamomile flower extract against Pythium pathogen of bean was investigated. Data obtained showed a reduction in the percentage of rotted seed and infected seedling up to 28 and 8%, respectively. Whereas, the survival increased up to 64% compared to other ones. There was also a significant promotion in growth features, total chlorophyll, carotenoids, total polyphenols and flavonoids, polyphenol-oxidase and peroxidase enzymes compared to other ones. To the best of our knowledge, there are no reported studies that included the mutual association of fungus, T. harzianum with the extract taken from the chamomile flower against P. ultimum, either in vitro or in vivo. In conclusion, the application of both T. harzianum and/or M. chamomilla extracts in the control of bean Pythium pathogen showed significant results.