Optimization of pullulan production by Aureobasidium pullulans using semi-solid-state fermentation and artificial neural networks: Characterization and antibacterial activity of pullulan impregnated with Ag-TiO2 nanocomposite.

This study presents a novel and efficient approach for pullulan production using artificial neural networks (ANNs) to optimize semi-solid-state fermentation (S-SSF) on faba bean biomass (FBB). This method achieved a record-breaking pullulan yield of 36.81 mg/g within 10.82 days, significantly exceeding previous results. Furthermore, the study goes beyond yield optimization by characterizing the purified pullulan, revealing its unique properties including thermal stability, amorphous structure, and antioxidant activity. Energy-dispersive X-ray spectroscopy and scanning electron microscopy confirmed its chemical composition and distinct morphology. This research introduces a groundbreaking combination of ANNs and comprehensive characterization, paving the way for sustainable and cost-effective pullulan production on FBB under S-SSF conditions. Additionally, the study demonstrates the successful integration of pullulan with Ag@TiO2 nanoparticles during synthesis using Fusarium oxysporum. This novel approach significantly enhances the stability and efficacy of the nanoparticles by modifying their surface properties, leading to remarkably improved antibacterial activity against various human pathogens. These findings showcase the low-cost production medium, and extensive potential of pullulan not only for its intrinsic properties but also for its ability to significantly improve the performance of nanomaterials. This breakthrough opens doors to diverse applications in various fields.

Turmeric extract-mediated biogenic synthesis of Ag@SeO2 magnetic nanoparticles: characterization, optimization, antibacterial and antioxidant activities.

This study bio-synthesized Ag@SeO2 bmNPs successfully, using turmeric ethanol extract, and characterized them using various techniques. The FT-IR analysis reveals the involvement of these plant-derived compounds, especially phenolics, in the reduction process by acting as electron donors and stabilizing/capping agents. Zeta potential analysis showed a slight negative surface charge for the stability of Ag@SeO2 NPs, where TEM revealed spherical nanoparticles with an average size of 20 nm. The XRD confirmed crystallinity and a core-shell structure, and EDX identified elements consistent with Ag@SeO2 and a 3 : 1 Ag/Se atomic ratio. Further, SEM supported the spherical shape and uniform size. These findings highlight the successful biosynthesis of Ag@SeO2 bmNPs with promising properties for diverse applications. Moreover, the Box-Behnken design (BBD) and artificial neural network (ANN) model were engaged to optimize Ag@SeO2 bmNP biosynthesis. BBD identified significant influences of pH, bioconversion temperature, time, and turmeric concentration on bmNP yield, with adjusted R2 and predictive R2 being 0.9075 and 0.8829, respectively. However, its limitations were revealed by a significant lack of fit. ANN modeling with a 3-5-7-1 topology showed superior predictive accuracy and identified optimal conditions for maximizing yield (pH 9.83, 51.7 °C, 1.0 h, 3.71 mg mL-1 turmeric). Validation experiments confirmed the model's reliability. Turmeric extract exhibited significantly higher amounts of phenolics, and flavonoids compared to the bmNPs, suggesting its potential for strong antioxidant activity. Both turmeric extract and bmNPs displayed antioxidant activity in ABTS and DPPH assays, with turmeric extract being the most potent due to its curcuminoid content. The potential activity of Ag@SeO2 bmNPs against S. aureus, K. pneumonia, E. coli, and B. cereus was investigated, with inhibition zones ranging from 22 to 32 mm. The MIC values of tested NPs towards pathogenic bacteria ranged from 165.625 and 331.25 µg mL-1.

A Comparative Study of Cr(VI) Sorption by Aureobasidium pullulans AKW Biomass and Its Extracellular Melanin: Complementary Modeling with Equilibrium Isotherms, Kinetic Studies, and Decision Tree Modeling.

Melanin as a natural polymer is found in all living organisms, and plays an important role in protecting the body from harmful UV rays from the sun. The efficiency of fungal biomass (Aureobasidium pullulans) and its extracellular melanin as Cr(VI) biosorbents was comparatively considered. The efficiency of Cr(VI) biosorption by the two sorbents used was augmented up to 240 min. The maximum sorption capacities were 485.747 (fungus biomass) and 595.974 (melanin) mg/g. The practical data were merely fitted to both Langmuir and Freundlich isotherms. The kinetics of the biosorption process obeyed the pseudo-first-order. Melanin was superior in Cr(VI) sorption than fungal biomass. Furthermore, four independent variables (contact time, initial concentration of Cr(VI), biosorbent dosage, and pH,) were modeled by the two decision trees (DTs). Conversely, to equilibrium isotherms and kinetic studies, DT of fungal biomass had lower errors compared to DT of melanin. Lately, the DTs improved the efficacy of the Cr(VI) removal process, thus introducing complementary and alternative solutions to equilibrium isotherms and kinetic studies. The Cr(VI) biosorption onto the biosorbents was confirmed and elucidated through FTIR, SEM, and EDX investigations. Conclusively, this is the first report study attaining the biosorption of Cr(VI) by biomass of A. pullulans and its extracellular melanin among equilibrium isotherms, kinetic study, and algorithmic decision tree modeling.

A comparative study using response surface methodology and artificial neural network towards optimized production of melanin by Aureobasidium pullulans AKW.

The effect of three independent variables (i.e., tyrosine, sucrose, and incubation time) on melanin production by Aureobasidium pullulans AKW was unraveled by two distinctive approaches: response surface methodology (i.e. Box Behnken design (BBD)) and artificial neural network (ANN) in this study for the first time ever using a simple medium. Regarding BBD, sucrose and incubation intervals did impose a significant influence on the output (melanin levels), however, tyrosine did not. The validation process exhibited a high consistency of BBD and ANN paradigms with the experimental melanin production. Concerning ANN, the predicted values of melanin were highly comparable to the experimental values, with minor errors competing with BBD. Highly comparable experimental values of melanin were achieved upon using BBD (9.295 ± 0.556 g/L) and ANN (10.192 ± 0.782 g/L). ANN accurately predicted melanin production and showed more improvement in melanin production by about 9.7% higher than BBD. The purified melanin structure was verified by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction pattern (XRD), and thermogravimetric analysis (TGA). The results verified the hierarchical architecture of the particles as small compasses by SEM analysis, inter-layer spacing in the XRD analysis, maximal atomic % for carbon, and oxygen atoms in the EDX analysis, and the great thermal stability in the TGA analysis of the purified melanin. Interestingly, the current novel endophytic strain was tyrosine-independent, and the uniquely applied ANN paradigm was more efficient in modeling the melanin production with appreciate amount on a simple medium in a relatively short time (168 h), suggesting additional optimization studies for further maximization of melanin production.

Pseudomonas alcaliphila NEWG-2 as biosorbent agent for methylene blue dye: optimization, equilibrium isotherms, and kinetic processes.

In comparison to physicochemical and chemical methods, microbial dye biosorption is regarded as an eco-effective and economically viable alternative and is a widely applied method due to its high efficiency and compatibility with the environment. Therefore, the idea of this study is to clarify to what extent the viable cells and the dry biomass of Pseudomonas alcaliphila NEWG-2 can improve the biosorption of methylene blue (MB) from a synthetic wastewater sample. The array of Taguchi paradigm has been conducted to ascertain five variables affecting the biosorption of MB by broth forms of P. alcaliphila NEWG. The data of MB biosorption were familiar to the predicted ones, indicating the precision of the Taguchi model's prediction. The maximum biosorption of MB (87.14%) was achieved at pH 8, after 60 h, in a medium containing 15 mg/ml MB, 2.5% glucose, and 2% peptone, with sorting the highest signal-to-noise ratio (38.80). FTIR spectra detected various functional groups (primary alcohol, α, ß-unsaturated ester, symmetric NH2 bending, and strong C-O stretching) on the bacterial cell wall that participated in the biosorption of MB. Furthermore, the spectacular MB biosorption ability was validated by equilibrium isotherms and kinetic studies (the dry biomass form), which were derived from the Langmuir model (qmax = 68.827 mg/g). The equilibrium time was achieved in about 60 min, with 70.5% of MB removal. The biosorption kinetic profile might be adequately represented by pseudo-second order and Elovich models. The changes in the bacterial cells before and after the biosorption of MB were characterized using a scanning electron microscope. As realized from the aforementioned data, the bacterium is a talented, effective, eco-friendly, and low-cost bio-sorbent for the decolorization and remedy of an industrial effluent containing MB from an aqueous environment. The current outcomes in the biosorption of MB molecules promote the use of the bacterial strain as viable cells and/or dry biomass in ecosystem restoration, environmental cleanup, and bioremediation studies.

Innovative binary sorption of Cobalt(II) and methylene blue by Sargassum latifolium using Taguchi and hybrid artificial neural network paradigms.

The present investigation has been designed by Taguchi and hybrid artificial neural network (ANN) paradigms to improve and optimize the binary sorption of Cobalt(II) and methylene blue (MB) from an aqueous solution, depending on modifying physicochemical conditions to generate an appropriate constitution for a highly efficient biosorption by the alga; Sargassum latifolium. Concerning Taguchi's design, the predicted values of the two responses were comparable to actual ones. The biosorption of Cobalt(II) ions was more efficient than MB, the supreme biosorption of Cobalt(II) was verified in run L21 (93.28%), with the highest S/N ratio being 39.40. The highest biosorption of MB was reached in run L22 (74.04%), with a S/N ratio of 37.39. The R2 and adjusted R2 were in reasonable values, indicating the validity of the model. The hybrid ANN model has exclusively emerged herein to optimize the biosorption of both Cobalt(II) and MB simultaneously, therefore, the ANN model was better than the Taguchi design. The predicted values of Cobalt(II) and MB biosorption were more obedience to the ANN model. The SEM analysis of the surface of S. latifolium showed mosaic form with massive particles, as crosslinking of biomolecules of the algal surface in the presence of Cobalt(II) and MB. Viewing FTIR analysis showed active groups e.g., hydroxyl, α, ß-unsaturated ester, α, ß-unsaturated ketone, N-O, and aromatic amine. To the best of our knowledge, there are no reports deeming the binary sorption of Cobalt(II) and MB ions by S. latifolium during Taguchi orthogonal arrays and hybrid ANN.

Optimization of Heavy Metals Biosorption via Artificial Neural Network: A Case Study of Cobalt (II) Sorption by Pseudomonas alcaliphila NEWG-2.

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.