Harnessing artificial intelligence-driven approach for enhanced indole-3-acetic acid from the newly isolated Streptomyces rutgersensis AW08.

Indole-3-acetic acid (IAA) derived from Actinobacteria fermentations on agro-wastes constitutes a safer and low-cost alternative to synthetic IAA. This study aims to select a high IAA-producing Streptomyces-like strain isolated from Lake Oubeira sediments (El Kala, Algeria) for further investigations (i.e., 16S rRNA gene barcoding and process optimization). Subsequently, artificial intelligence-based approaches were employed to maximize IAA bioproduction on spent coffee grounds as high-value-added feedstock. The specificity was the novel application of the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Box (L-BFGS-B) optimization algorithm. The new strain AW08 was a significant producer of IAA (26.116 ± 0.61 µg/mL) and was identified as Streptomyces rutgersensis by 16S rRNA gene barcoding and phylogenetic inquiry. The empirical data involved the inoculation of AW08 in various cultural conditions according to a four-factor Box Behnken Design matrix (BBD) of Response surface methodology (RSM). The input parameters and regression equation extracted from the RSM-BBD were the basis for implementing and training the L-BFGS-B algorithm. Upon training the model, the optimal conditions suggested by the BBD and L-BFGS-B algorithm were, respectively, L-Trp (X1) = 0.58 %; 0.57 %; T° (X2) = 26.37 °C; 28.19 °C; pH (X3) = 7.75; 8.59; and carbon source (X4) = 30 %; 33.29 %, with the predicted response IAA (Y) = 152.8; 169.18 µg/mL). Our findings emphasize the potential of the multifunctional S. rutgersensis AW08, isolated and reported for the first time in Algeria, as a robust producer of IAA. Validation investigations using the bioprocess parameters provided by the L-BFGS-B and the BBD-RSM models demonstrate the effectiveness of AI-driven optimization in maximizing IAA output by 5.43-fold and 4.2-fold, respectively. This study constitutes the first paper reporting a novel interdisciplinary approach and providing insights into biotechnological advancements. These results support for the first time a reasonable approach for valorizing spent coffee grounds as feedstock for sustainable and economic IAA production from S. rutgersensis AW08.

Waste valorization as low-cost media engineering for auxin production from the newly isolated Streptomyces rubrogriseus AW22: Model development.

Indole-3-acetic acid (IAA) represents a crucial phytohormone regulating specific tropic responses in plants and functions as a chemical signal between plant hosts and their symbionts. The Actinobacteria strain of AW22 with high IAA production ability was isolated in Algeria for the first time and was characterized as Streptomyces rubrogriseus through chemotaxonomic analysis and 16 S rDNA sequence alignment. The suitable medium for a maximum IAA yield was engineered in vitro and in silico using machine learning-assisted modeling. The primary low-cost feedstocks comprised various concentrations of spent coffee grounds (SCGs) and carob bean grounds (CBGs) extracts. Further, we combined the Box-Behnken design from response surface methodology (BBD-RSM) with artificial neural networks (ANNs) coupled with the genetic algorithm (GA). The critical process parameters screened via Plackett-Burman design (PBD) served as BBD and ANN-GA inputs, with IAA yield as the output variable. Analysis of the putative IAA using thin-layer chromatography (TLC) and (HPLC) revealed Rf values equal to 0.69 and a retention time of 3.711 min, equivalent to the authentic IAA. AW 22 achieved a maximum IAA yield of 188.290 ± 0.38 µg/mL using the process parameters generated by the ANN-GA model, consisting of L-Trp, 0.6%; SCG, 30%; T°, 25.8 °C; and pH 9, after eight days of incubation. An R2 of 99.98%, adding to an MSE of 1.86 × 10-5 at 129 epochs, postulated higher reliability of ANN-GA-approach in predicting responses, compared with BBD-RSM modeling exhibiting an R2 of 76.28%. The validation experiments resulted in a 4.55-fold and 4.46-fold increase in IAA secretion, corresponding to ANN-GA and BBD-RSM models, respectively, confirming the validity of both models.

Synthesis of novel PANI/PVA-NiCu composite material for efficient removal of organic dyes.

The present work aims the synthesis of a novel, low cost, and environmentally friendly PANI/PVA-CuNi composite by chemical oxidative polymerization of aniline monomer and polyvinyl alcohol (PVA) as film matrix; several percentages of copper (Cu) and Nickel (Ni) were used. UV-Visible spectroscopy, FTIR, SEM-EDX, and TGA were used to characterize the nanocomposites. While PANI/PVA-CuNi nanocomposites were investigated in adsorption experiments of methylene blue (MB) under different controlled conditions (time reaction, adsorbent dosage, initial dye concentration, stirring speed, temperature, and pH of the medium) also various kinetic models were employed to evaluate the efficiency of the adsorption. The results revealed that the10 mg of PANI/PVA-Cu50Ni50 and PANI/PVA-Ni composites Catalyst removed (94% and 93% of methylene blue in 180 min respectively at 10-5 M initial concentration of dye, pH of 13, stirring speed of 150 rpm, the temperature of 301 k. the kinetics data were properly fitted with the pseudo second-order model with a correlation coefficient of 0.98262 and 0.95881 using PANI/PVA-Cu50Ni50 and PANI/PVA-Ni, respectively.

High-efficiency electrochemical degradation of phenol in aqueous solutions using Ni-PPy and Cu-PPy composite materials.

Conductive crystalline polypyrrole (Cryst-PPy), Nickel-polypyrrole (Ni-PPy), and copper- polypyrole (Cu-PPy) hybrid materials were prepared using a chemical polymerization method in an aqueous solution. Part I was focused on the Chemical synthesis of Cryst-PPy powder from an organic medium. Cryst-PPy powder was successfully synthesized by chemical route from an organic medium of acetonitrile with polyethylene oxide as a stabilizing agent and oxidizing agent like potassium peroxydisulfate. The morphological study was showed the presence of spherical nanoparticles and cubic microparticles giving rise to a denser structure of PPy. In the second part, the based electrodes composites were examined in the oxidation of phenol by an electrochemical process in an alkaline medium. To follow the yield of phenol degradation at the alkaline solution, UV-visible analysis was performed at the following operating conditions: current density of 0.58 mA cm-2, phenol initial concentration of 0.150 M and for 3 h processing; the rate of phenol elimination was 56%, 38% and 28% for Cu-PPy, Ni-PPy, and pure PPy electrodes respectively. Thus, can be found that the doped Cu-PPy electrodes electrode is a new material with high electrochemical oxidation ability for phenol degradation in aqueous solutions.

Antimicrobial and antifungal properties of NiCu-PANI/PVA quaternary nanocomposite synthesized by chemical oxidative polymerization of polyaniline.

Increasing antimicrobial resistance has led to use of novel technologies such as nanomaterials and nanocomposites that have shown effective antimicrobial and/or antifungal activities against several gram-positive and gram-negative bacteria. There have been limited studies on antimicrobial properties of the combined polymer nanocomposites with transitional bimetallic nanoparticles such as nickel (Ni) and copper (Cu). Thus, the main objective of this study was to synthesis, characterize and investigate the antibacterial and antifungal properties of NiCu-PANI/PVA nanocomposite. The nanocomposite films with different amount of Ni and Cu salts were synthesized by chemical oxidative polymerization of polyaniline using HCl as oxidant and PVA as a stabilizer. Optical, chemical composition, and morphological characteristics as well as thermal stability were evaluated using UV-Visible, FTIR, SEM-EDX, and TGA analyses. Antimicrobial properties were then determined using the disc diffusion assay against gram-negative bacteria (i.e., Escherichia coli ATCC 25922, Klebsiella pneumonia ATCC 700603, Proteus sp.,) and gram-positive bacteria (i.e., Staphylococcus aureus ATCC 2593). Fungal plant pathogens including Aspergillus niger and Fusarium oxysporum f. sp. pisi were also evaluated for determination of antifungal activity of NiCu-PANI/PVA films. Among the synthesized films, Ni65Cu35-PANI/PVA showed excellent antibacterial activity against all the bacteria strains examined in this study. The diameters of inhibition zones for Escherichia coli ATCC 25922, Klebsiella pneumoniae ATCC 700603, Proteus sp., and Staphylococcus aureus ATCC 2593 were 23, 23, 17, and 18 mm, respectively indicating good antibacterial activities. Additionally, NiCu-PANI/PVA, particularly the films with higher Cu intake, showed better antifungal activity against Fusarium oxysporum f. sp. pisi. However, NiCu-PANI/PVA was ineffective against Aspergillus niger.

A novel non-enzymatic glucose sensor based on NiFe(NPs)-polyaniline hybrid materials.

This article was focused on the elaboration of NiFe-Polyaniline glucose sensors via electrochemical technique. Firstly, the PANi (polyaniline) fibers were synthesized by oxidation of the monomer aniline on FTO (fluorine tin oxide) substrate. Secondly, the Nickel-Iron nanoparticles (NiFe (NPs)) were obtained by the Chronoamperometry method on the Polyaniline surface. The NiFe-PANi hybrid electrode was characterized by scanning electron microscopy (SEM), force atomic microscopy (AFM), Fourier-transformed infrared (FTIR), and X-ray diffraction (XRD). The electrochemical glucose sensing performance of the NiFe alloy nanoparticle was studied by cyclic voltammetry and amperometry. The fabricated glucose sensor Ni-Fe hybrid material exhibited many remarkable sensing performances, such as low-response time (4 s), sensitivity (1050 µA mM-1 cm-2), broad linear range (from 10 µM -1 mM), and low limit of detection (LOD) (0.5 µM, S/N = 3). The selectivity, reliability, and stability of the NiFe hybrid material for glucose oxidation were also investigated. All the results demonstrated that the NiFe-PANi/FTO hybrid electrode is very promising for application in electrochemical glucose sensing.