Fungal biosynthesis of lignin-modifying enzymes from pulp wash and Luffa cylindrica for azo dye RB5 biodecolorization using modeling by response surface methodology and artificial neural network

Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm

Given the necessity and urgency in removing organic pollutants such as malachite green (MG) from the environment, it is vital to screen high-capacity adsorbents using artificial neural network (ANN) methods quickly and accurately. In this study, a series of ZIF-67 were synthesized, which adsorption properties for organic pollutants, especially MG, were systematically evaluated and determined as 241.720 mg g-1 (25 ℃, 2 h). The adsorption process was more consistent with pseudo-second-order kinetics and Langmuir adsorption isotherm, which correlation coefficients were 0.995 and 0.997, respectively. The chemisorption mechanism was considered to be π-π stacking interaction between imidazole and aromatic ring. Then, a Python-based neural network model using the Limited-memory BFGS algorithm was constructed by collecting the crucial structural parameters of ZIF-67 and the experimental data of batch adsorption. The model, optimized extensively, outperformed similar Matlab-based ANN with a coefficient of determination of 0.9882 and mean square error of 0.0009 in predicting ZIF-67 adsorption of MG. Furthermore, the model demonstrated a good generalization ability in the predictive training of other organic pollutants. In brief, ANN was successfully separated from the Matlab platform, providing a robust framework for high-precision prediction of organic pollutants and guiding the synthesis of adsorbents.

Thermosensitive polymer-assisted extraction and purification of fungal laccase from citrus pulp wash effluent

BACKGROUND

This study explores the use of liquid-liquid extraction with thermosensitive polymers for producing laccase (Lac) from Pleurotus sajor-caju. This process leverages liquid waste from the citrus industry, specifically pulp wash. The research delves into extractive fermentation and thermoseparation, both processes being facilitated by a polymer exhibiting a lower critical solution temperature transition.

RESULTS

Key factors considered include the choice of polymer, its concentration, pH, separation temperature, and the behavior of the polymer-rich phase post-extractive fermentation concerning the lower critical solution temperature. Notably, under conditions of 45% by weight of Pluronic L-61 and pH 5.0 at 25 °C, the Lac resulted in an enhancement in the purification factor of 28.4-fold, compared with the Lac obtained directly from the fermentation process on the eighth day. There was an 83.6% recovery of the Lac enzyme in the bottom phase of the system. Additionally, the unique properties of Pluronic L-61, which can induce phase separation and also allow for thermoseparation, led to a secondary fraction (aqueous solution) of Lac with purification factor of 2.1 ± 0.1-fold (at 32 ± 0.9 °C and 30 ± 0.3 min without stirring) from the polymeric phase (top phase). Fourier-transform infrared analysis validated the separation data, particularly highlighting the α-helix content in the amide I region (1600-1700 cm-1 ).

CONCLUSION

In summary, the insights from this study pave the way for broader industrial applications of these techniques, underscoring benefits like streamlined process integration, heightened selectivity, and superior separation efficacy. © 2023 Society of Chemical Industry.

Structural insights, biocatalytic characteristics, and application prospects of lignin-modifying enzymes for sustainable biotechnology-A review

Lignin modifying enzymes (LMEs) have gained widespread recognition in depolymerization of lignin polymers by oxidative cleavage. LMEs are a robust class of biocatalysts that include lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), laccase (LAC), and dye-decolorizing peroxidase (DyP). Members of the LMEs family act on phenolic, non-phenolic substrates and have been widely researched for valorization of lignin, oxidative cleavage of xenobiotics and phenolics. LMEs implementation in the biotechnological and industrial sectors has sparked significant attention, although its potential future applications remain underexploited. To understand the mechanism of LMEs in sustainable pollution mitigation, several studies have been undertaken to assess the feasibility of LMEs in correlating to diverse pollutants for binding and intermolecular interactions at the molecular level. However, further investigation is required to fully comprehend the underlying mechanism. In this review we presented the key structural and functional features of LMEs, including the computational aspects, as well as the advanced applications in biotechnology and industrial research. Furthermore, concluding remarks and a look ahead, the use of LMEs coupled with computational frameworks, built upon artificial intelligence (AI) and machine learning (ML), has been emphasized as a recent milestone in environmental research.

Optimization of laccase enzyme extraction from spent mushroom waste of Pleurotus florida through ANN-PSO modeling: An ecofriendly and economical approach

The cardinal focus of this study is to optimize the best reaction conditions for maximizing laccase activity from spent mushroom waste (SMW) of Pleurotus florida. Optimization process parameters were studied by the modeling techniques, artificial neural networking (ANN) embedded in particle swarm optimization (PSO), and response surface model (RSM). The best topology of ANN-PSO architecture was obtained on 4-10-1. The R², IOA, MSE, and MAE values of the ANN model were obtained as 0.98785, 0.9939, 0.0023, and 0.0251 while, that of the RSM model were obtained as 0.74290, 0.9210, 0.0244, and 0.1110 respectively. The higher values of R², IOA, and lower values of MSE and MAE of the ANN-PSO model depict that ANN-PSO outperformed compared to RSM and also verified the effectiveness of the ANN-PSO model. The ANN-PSO model performance demonstrates the robustness of the technique in optimizing laccase activity in SMW of P. florida. The optimization results revealed that pH 4.5, time 3 h, solid: solution ratio 1:5, and ABTS concentration of 1 mM was optimal for achieving maximum laccase activity at temperature 30 °C. The enzymatic activity of crude laccase enzyme was obtained as 1.185 U ml^-1 without loss of enzyme activity. Additionally, crude laccase enzyme was 1.74 fold partially purified, and 83.54% of the enzyme was yielded. Out of all the independent process variables, ABTS and pH had an influence on laccase activity. Therefore, we anticipate that the findings of this investigation will reduce the ambiguity in maximizing laccase activity and ease the screening process. This study also highlights the comparative cost evaluation of crude laccase enzyme extracted from P. florida and commercial enzymes. There is a great potential for the utilization of the laccase enzyme extracted from SMW and using it for the degradation of recalcitrant micropollutants. Thus, SMW promises a cost-effective and sustainable approach leading towards circular economy.

Modeling of methylene blue removal on Fe3O4 modified activated carbon with artificial neural network (ANN)

In this study, AC/Fe3O4 adsorbent was first synthesized by modifying activated carbon with Fe3O4. The structure of the adsorbent was then characterized using analysis techniques specific surface area (BET), Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDX), and Fourier Transform Infrared Spectroscopy (FTIR). Equilibrium, thermodynamic and kinetic studies were carried out on the removal of methylene blue (MB) dyestuff from aqueous solutions AC/Fe3O4 adsorbent. The Langmuir maximum adsorption capacity of AC/Fe3O4 was 312.8 mg g-1, and the best fitness was observed with the pseudo-second-order kinetics model, with an endothermic adsorption process. In the final stage of the study, the adsorption process of MB on AC/Fe3O4 was modeled using artificial neural network modeling (ANN). Considering the smallest mean square error (MSE), The backpropagation neural network was configured as a three-layer ANN with a tangent sigmoid transfer function (Tansig) at the hidden layer with 10 neurons, linear transfer function (Purelin) the at output layer and Levenberg-Marquardt backpropagation training algorithm (LMA). Input parameters included initial solution pH (2.0-9.0), amount (0.05-0.5 g L-1), temperature (298-318 K), contact time (5-180 min), and concentration (50-500 mg L-1). The effect of each parameter on the removal and adsorption percentages was evaluated. The performance of the ANN model was adjusted by changing parameters such as the number of neurons in the middle layer, the number of inputs, and the learning coefficient. The mean absolute percentage error (MAPE) was used to evaluate the model's accuracy for the removal and adsorption percentage output parameters. The absolute fraction of variance (R2) values were 99.83, 99.36, and 98.26% for the dyestuff training, validation, and test sets, respectively.

Optimized preparation of gangue waste-based geopolymer adsorbent based on improved response surface methodology for Cd(II) removal from wastewater

Resource utilization of gangue solid waste has become an essential research direction for green development. This study prepared a novel gangue based geopolymer adsorbent (GPA) for the removal of Cd(II) from wastewater using pretreatment gangue (PG) as the main raw material. The ANOVA indicated that the obtained quadratic model of fitness function (R² > 0.99, P-value <0.0001) was significant and adequate, and the contribution of the three preparation conditions to the removal of Cd(II) was: calcination temperature > Na₂CO₃:PG ratio > water-glass solid content. The hybrid response surface method and gray wolf optimization (RSM-GWO) algorithm were adopted to acquire the optimum conditions: Na₂CO₃:PG ratio = 1.05, calcination temperature of 701 °C, solid content of water glass of 22.42%, and the removal efficiency of Cd(II) by GPA obtained under the optimized conditions (GPAC) was 97.84%. Adsorption kinetics, adsorption isotherms and characterization by XRD, FTIR, Zeta potential, FSEM-EDS and BET were utilized to investigate the adsorption mechanism of GPAC on Cd(II). The results showed that the adsorption of Cd(II) from GPAC was consistent with the pseudo-second-order model (R² = 0.9936) and the Langmuir model (R² = 0.9988), the adsorption was a monolayer adsorption process and the computed maximum Cd(II) adsorption (50.76 mg g^-1) was approximate to experimental results (51.47 mg g^-1). Moreover, the surface morphology of GPAC was rough and porous with a specific surface area (SSA) of 18.54 m² g^-1, which provided abundant active sites, and the internal kaolinite was destroyed to produce a zeolite-like structure where surface complexation and ion exchange with Cd(II) through hydroxyl (-OH) and oxygen-containing groups (-SiOH and -AlOH) were the main adsorption mechanisms. Thus, GPAC is a lucrative adsorbent material for effective Cd(II) wastewater treatment, complying with the "high value-added" usage of solid wastes and "waste to cure poison" green sustainable development direction.

Bioprospecting of novel peroxidase from Streptomyces coelicolor strain SPR7 for carcinogenic azo dyes decolorization

Peroxidase (POX) is a heme-containing oxidoreductase, its voluminous immuno-diagnostic and bioremediatory intuitions have incited optimization and large scale-generation from novel microbial repertoires. Azo dyes are the most detrimental classes of synthetic dyes and they are the common ecotoxic industrial pollutants in wastewater. In addition, azo dyes are refractory to degradation owing to their chemical nature, comprising of azoic linkages, amino moieties with recalcitrant traits. Moreover, they are major carcinogenic and mutagenic on humans and animals, whereby emphasizing the need for decolorization. In the present study, a novel POX from Streptomyces coelicolor strain SPR7 was investigated for the deterioration of ecotoxic dyestuffs. The initial medium component screening for POX production was achieved using, One Factor at a Time and Placket-Burman methodologies with starch, casein and temperature as essential parameters. In auxiliary, Response Surface Methodology (RSM) was recruited and followed by model validation using Back propagation algorithm (BPA). RSM-BPA composite approach prophesied that combination of starch, casein, and temperature at optimal values 2.5%, 0.035% and 35 °C respectively, has resulted in 7 folds enhancement of POX outturn (2.52 U/mL) compared to the unoptimized media (0.36 U/mL). The concentrated enzyme decolorized 75.4% and 90% of the two azo dyes with lignin (10 mM), respectively. Hence, this investigation confirms the potentiality of mangrove actinomycete derived POX for elimination of noxious azo dyes to overcome their carcinogenic, mutagenic and teratogenic effects on humans and aquatic organisms.

Optimization, kinetics, and thermodynamics aspects in the biodegradation of reactive black 5 (RB5) dye from textile wastewater using isolated bacterial strain, Bacillus albus DD1

This study is aimed to model and optimize the decolorization of reactive black 5 (RB5) dye using Bacillus albus DD1. The response surface methodology (RSM) along with rotatable central composite design (rCCD) is used to optimize the response, % decolorization with four input variables: (i) pH (5-9), initial dye concentration (50-500 ppm), the composition of yeast extract as nitrogen source (0.2-1%) and amount of bacterial inoculum (5-25% v/v). The % decolorization is predicted to be ≈ 98% at the optimized condition (pH = 7.6, dye concentration = 200 ppm, bacterial inoculum = 20 v/v% and yeast extract = 0.4%). Furthermore, the kinetics and thermodynamics of RB5 degradation are also determined. The kinetic order of biodegradation of RB5 is found to follow first-order kinetics with a kinetic rate constant = 0.0384. The activation energy, E_a and frequency factor, A values are calculated as 34.46 kJ/mol and 24,343 (1/Day). A thermodynamic study is also carried out at different temperatures (298 K, 308 K, 310 K, 313 K, and 318 K) using optimized conditions. The values of the ΔH and ΔS are found to be +30.79 kJ/mol, and -0.1 kJ/mol/K, respectively using the Eyring-Polanyi equation. The values of ΔG are also calculated at all temperatures.

Optimization of the Decolorization of the Reactive Black 5 by a Laccase-like Active Cell-Free Supernatant from Coriolopsis gallica

The textile industry generates huge volumes of colored wastewater that require multiple treatments to remove persistent toxic and carcinogenic dyes. Here we studied the decolorization of a recalcitrant azo dye, Reactive Black 5, using laccase-like active cell-free supernatant from Coriolopsis gallica. Decolorization was optimized in a 1 mL reaction mixture using the response surface methodology (RSM) to test the influence of five variables, i.e., laccase-like activity, dye concentration, redox mediator (HBT) concentration, pH, and temperature, on dye decolorization. Statistical tests were used to determine regression coefficients and the quality of the models used, as well as significant factors and/or factor interactions. Maximum decolorization was achieved at 120 min (82 ± 0.6%) with the optimized protocol, i.e., laccase-like activity at 0.5 U mL−1, dye at 25 mg L−1, HBT at 4.5 mM, pH at 4.2 and temperature at 55 °C. The model proved significant (ANOVA test with p < 0.001): coefficient of determination (R²) was 89.78%, adjusted coefficient of determination (R²A) was 87.85%, and root mean square error (RMSE) was 10.48%. The reaction conditions yielding maximum decolorization were tested in a larger volume of 500 mL reaction mixture. Under these conditions, the decolorization rate reached 77.6 ± 0.4%, which was in good agreement with the value found on the 1 mL scale. RB5 decolorization was further evaluated using the UV-visible spectra of the treated and untreated dyes.

Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks

Removing decolorizing acid blue 113 (AB113) dye from textile wastewater is challenging due to its high stability and resistance to removal. In this study, we used an artificial neural network (ANN) model to estimate the effect of five different variables on AB113 dye removal in the sonophotocatalytic process. The five variables considered were reaction time (5-25 min), pH (3-11), ZnO dosage (0.2-1.0 g/L), ultrasonic power (100-300 W/L), and persulphate dosage (0.2-3 mmol/L). The most effective model had a 5-7-1 architecture, with an average deviation of 0.44 and R² of 0.99. A sensitivity analysis was used to analyze the impact of different process variables on removal efficiency and to identify the most effective variable settings for maximum dye removal. Then, an imaginary sonophotocatalytic system was created to measure the quantitative impact of other process parameters on AB113 dye removal. The optimum process parameters for maximum AB 113 removal were identified as 6.2 pH, 25 min reaction time, 300 W/L ultrasonic power, 1.0 g/L ZnO dosage, and 2.54 mmol/L persulfate dosage. The model created was able to identify trends in dye removal and can contribute to future experiments.

Synthesis of ternary-based visible light nano-photocatalyst for decontamination of organic dyes-loaded wastewater

The release of dyes-loaded wastewater from various industries is a major threat to human beings due to their health hazard effects. Ternary ferrites-based visible light photocatalyst Fe₂Zn_0.5Cu_0.5 O₄-CM (CZF-CM) was formed via the co-precipitation method. These prepared ternary ferrites nanoparticles Fe₂Zn_0.5Cu_0.5O₄ (CZF-NPs) and photocatalyst (CZF-CM) were analyzed using different spectroscopic techniques. The average crystallite size of CZF-NPs was calculated from XRD data using Scherer's equation and found to be 12 nm. The elemental composition of the synthesized ternary ferrites nanoparticles (CZF-NPs) was defined by the EDX images. The morphology of CZF-CM photocatalyst is spherical, having a smooth surface and average microspheres size of 810 μm based on SEM micrographs. The photocatalyst has bandgap of 2.57 eV, which lies in the visible range of the electromagnetic spectrum derived by extrapolating Tauc's plot. Photocatalyst CZF-CM showed 94% degradation efficiency for Rhodamine B (RB) dye at optimized conditions of initial dye concentration, catalyst dosage, pH and sunlight irradiation contact time as 40 ppm, 0.7 g, pH 8 and 125 min, respectively. Maximum degradation (96%) of methyl orange (MO) dye occurred at pH 6, at similar optimized conditions as the RB dye. The binary ferrites photocatalyst Fe₂CuO₄-CM (CF-CM) and Fe₂ZnO₄-CM (ZF-CM) of the selected metals showed lesser photocatalytic efficiency than ternary ferrites. An artificial neural network in addition to the response surface methodology was used for the optimization process. The artificial neural network is highly in agreement with the experimental results obtained for the selected dyes. The corresponding predicted response for each data set from ANOVA showed high R², R²_adj, and R²_pred values for the proposed model. It also indicates that contributing parameters in the model are significant due to having very high F-values and low p-values. It is concluded that the synthesized photocatalysts are considered an efficient entrant for the decolorization of industrial wastewater.

Biochemical characterization of a novel azo reductase named BVU5 from the bacterial flora DDMZ1: application for decolorization of azo dyes

One of the main mechanisms of bacterial decolorization and degradation of azo dyes is the use of biological enzymes to catalyze the breaking of azo bonds. This paper shows the expression and properties of a novel azo reductase (hybrid-cluster NAD(P)-dependent oxidoreductase, accession no. A0A1S1BVU5, named BVU5) from the bacterial flora DDMZ1 for degradation of azo dyes. The molecular weight of BVU5 is about 40.1 kDa, and it contains the prosthetic group flavin mononucleotide (FMN). It has the decolorization ability of 80.1 ± 2.5% within 3 min for a dye concentration of 20 mg L⁻¹, and 53.5 ± 1.8% even for a dye concentration of 200 mg L⁻¹ after 30 min. The optimum temperature of enzyme BVU5 is 30 °C and the optimum pH is 6. It is insensitive to salt concentration up to a salinity level of 10%. Furthermore, enzyme BVU5 has good tolerance toward some metal ions (2 mM) such as Mn²⁺, Ca²⁺, Mg²⁺ and Cu²⁺ and some organic solvents (20%) such as DMSO, methanol, isopentyl, ethylene glycol and N-hexane. However, the enzyme BVU5 has a low tolerance to high concentrations of denaturants. In particular, it is sensitive to the denaturants guanidine hydrochloride (GdmCl) (2 M) and urea (2 M). Analysis of the dye substrate specificity shows that enzyme BVU5 decolorizes most azo dyes, which is indicating that the enzyme is not strictly substrate specific, it is a functional enzyme for breaking the azo structure. Liquid chromatography/time-of-flight/mass spectrometry (LC-TOF-MS) revealed after the action of enzyme BVU5 that some intermediate products with relatively large molecular weights were produced; this illustrates a symmetric or an asymmetric rapid cleavage of the azo bonds by this enzyme. The potential degradation pathways and the enzyme-catalyzed degradation mechanism are deduced in the end of this paper. The results give insight into the potential of a rapid bio-pretreatment by enzyme BVU5 for processing azo dye wastewater.

The combination of BVU5 enzyme and coenzyme NADH can quickly degrade the azo dye RB5.

Biological remediation technologies for dyes and heavy metals in wastewater treatment: New insight

The pollution of the environment caused by dyes and heavy metals emitted by industries has become a worldwide problem. The development of efficient, environmentally acceptable, and cost-effective methods of wastewater treatment containing dyes and heavy metals is critical. Biologically based techniques for treating effluents are fascinating since they provide several benefits over standard treatment methods. This review assesses the most recent developments in the use of biological based techniques to remove dyes and heavy metals from wastewater. The remediation of dyes and heavy metals by diverse microorganisms such as algae, bacteria, fungi and enzymes are depicted in detail. Ongoing biological method's advances, scientific prospects, problems, and the future prognosis are all highlighted. This review is useful for gaining a better integrated view of biological based wastewater treatment and for speeding future research on the function of biological methods in water purification applications.

Prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of lignocellulosic biomass using artificial neural network modeling

Dilute inorganic acids hydrolysis is one of the most promising pretreatment strategies with high recovery of fermentable sugars and low cost for sustainable production of biofuels and chemicals from lignocellulosic biomass. The diverse phenolics derived from lignin degradation during pretreatment are the main inhibitors for enzymatic hydrolysis and fermentation. However, the content features of derived phenolics and produced glucose under different conditions are still unclear due to the highly non-linear characteristic of biomass pretreatment. Here, an artificial neural network (ANN) model was developed for simultaneous prediction of the derived phenolic contents (CPhe) and glucose yield (CGlc) in corn stover hydrolysate before microbial fermentation by integrating dilute acid pretreatment and enzymatic hydrolysis. Six processing parameters including inorganic acid concentration (CIA), pretreatment temperature (T), residence time (t), solid-to-liquid ratio (RSL), kinds of inorganic acids (kIA), and enzyme loading dosage (E) were used as input variables. The CPhe and CGlc were set as the two output variables. An optimized topology structure of 6-12-2 in the ANN model was determined by comparing root means square errors, which has a better prediction efficiency for CPhe (R2 = 0.904) and CGlc (R2 = 0.906). Additionally, the relative importance of six input variables on CPhe and CGlc was firstly calculated by the Garson equation with net weight matrixes. The results indicated that CIA had strong effects (22%-23%) on CPhe or CGlc, then followed by E and T. In conclusion, the findings provide new insights into the sustainable development and inverse optimization of biorefinery process from ANN modeling perspectives.

Synergistic role of bacterial consortium to biodegrade toxic dyes containing wastewater and its simultaneous reuse as an added value

The current environmental research has fascinated the sustainable exploitation of mix bacterial consortium to biodegrade the environmentally-related toxic compounds, including hazardous synthetic dyes. Based on the existing literature evidence, textile and other industrial waste effluents pollute the natural water bodies. Textile effluent contains synthetic dyes which are liberated in the environment without proper treatment. The presence of toxic dyes added to the textile effluents undoubtedly affects the flora and fauna as that untreated water is used for irrigation by local farmers. Many conventional and biological methods are in action for the treatment of wastewater. Physical and chemical processes are expensive as compared to microbial treatments. The use of microbial consortia generates efficient results. Wastewater is a valuable resource, however, up to 80% of wastewater is released to different water matrices. This discernment needs to change for a better tomorrow. In this context, herein, we present a robust microbial-assisted treatment and simultaneously reuse of the treated wastewater as an added value to induce plant growth. Thus, the microbial approach for textile waste treatment release by-product after degradation should be non-toxic for the environment. In the present study, the toxicity of synthetic textile dye named Reactive Red 120, Reactive Orange 122, Reactive Yellow 160, and Reactive Blue 19 was investigated using a bioassay method with plant species namely Sorghum bicolor. Plate and Pot experiment was conducted with respect to untreated Azo dyes, degraded metabolites obtained from single bacteria, and consortium. Efficient Seed germination (89%), shoot length (12.4 cm), root length (15.6 cm) of the plants were observed for bacterial consortium degraded metabolites exposed seeds after comparing with the control. The degraded metabolite also increases protein (45.56 mg/g) and sugar (3.15 mg/g) contents. Bioremediation of various textile industrial effluents saves the ecosystem from the harmful effects of hazardous dyes. The biological decolorization of the textile azo dyes was investigated under co-metabolic conditions. The degraded metabolites can be used to enhance crop productivity and for commercial application. This mandates the current and future research to develop economically feasible and environmentally sustainable wastewater treatment practices.

Valorization and optimization of agro-industrial orange waste for the production of enzyme by halophilic Streptomyces sp

This study underlines the biotechnical valorization of the accumulated and unusable remains of agro-industrial orange fruit peel waste to produce α-amylase under submerged conditions by Streptomyces sp. KP314280 (20r). The response surface methodology based on central composite design (RSM-CCD) and artificial neural network coupled with a genetic algorithm (ANN-GA) were used to model and optimize the conditions for the α-amylase production. Four independent variables were evaluated for α-amylase activity including substrate concentration, inoculum size, sodium chloride powder (NaCl), and pH. A ten-fold cross-validation indicated that the ANN has a greater ability than the RSM to predict the α-amylase activity (R²_ANN = 0.884 and R²_RSM = 0.725). The analysis of variance indicated that the aforementioned four factors significantly affected the α-amylase activity. Additionally, the α-amylase production experiments were conducted according to the optimal conditions generated by the GA. The results indicated that the amylase yield increased by 4-fold. Moreover, the α-amylase production (12.19 U/mL) in the optimized medium was compatible with the predicted conditions outlined by the ANN-GA model (12.62 U/mL). As such, the ANN and GA combination is optimizable for α-amylase production and exhibits an accurate prediction which provides an alternative to other biological applications.

Textile Dye Biodecolorization by Manganese Peroxidase: A Review

Wastewater emissions from textile factories cause serious environmental problems. Manganese peroxidase (MnP) is an oxidoreductase with ligninolytic activity and is a promising biocatalyst for the biodegradation of hazardous environmental contaminants, and especially for dye wastewater decolorization. This article first summarizes the origin, crystal structure, and catalytic cycle of MnP, and then reviews the recent literature on its application to dye wastewater decolorization. In addition, the application of new technologies such as enzyme immobilization and genetic engineering that could improve the stability, durability, adaptability, and operating costs of the enzyme are highlighted. Finally, we discuss and propose future strategies to improve the performance of MnP-assisted dye decolorization in industrial applications.

Synthetic dyes degradation using lignolytic enzymes produced from Halopiger aswanensis strain ABC_IITR by Solid State Fermentation

The present work focuses on studying the degradation of industrial synthetic dyes, which poses serious health hazards and a drastic impact on the environment. Currently available enzymatic processes have higher production and operational costs. However, most enzymes are active at acidic pH, which limits its application in textile dye degradation. This problem can be overcome by lignolytic enzymes obtained from halo-alkaliphile through Solid State Fermentation (SSF) using wheat bran (agro-byproduct) as a substrate. The major lignolytic enzymes studied were Lignin Peroxidase (LiP), Manganese Peroxidase (MnP), and laccase. The results demonstrated the highest activity of 215.4 ± 1.57 of LiP, 36.8 ± 2.38 of MnP, and 8.34 ± 0.21 IU/gds of laccase. Crude enzymes were used to treat synthetic dyes (mainly azo dyes), and their potential for its degradation was confirmed by spectrophotometric, GC-MS, and HPLC analysis. The highest decolorization of 82-93% of Malachite Green (MG) was achieved in LiP and MnP mediated reaction system within 2 hours. The laccase reaction system showed degradation of 53.87% of methyl orange without adding any redox mediator. After obtaining these results, the crude LiP and MnP in the reaction system were further subjected to decolorization at a higher MG concentration of 100-600 mg/L without a redox mediator. As a result, both LiP and MnP decolorized MG by 72-89%. Further, GC-MS analysis of MG biodegradation products confirmed the formation of less toxic low molecular weight products such as benzaldehyde and methanone.

Development and characterization of regenerable chitosan-coated nickel selenide nano-photocatalytic system for decontamination of toxic azo dyes

In this investigation, chitosan-coated nickel selenide nano-photocatalyst (CS-NiSe) was successfully prepared through the chemical reduction method. FTIR spectroscopy confirmed the synthesis of CS-NiSe nano-photocatalyst. Further, XRD analysis exhibited a monoclinic crystalline phase of photocatalyst with a crystallite size of 32 nm based on Scherer's equation. The SEM micrographs showed that the photocatalyst has an average particle size of 60 nm. The bandgap of CS-NiSe was (2.85 eV) in the visible region of the spectrum. Due to this reason, the CS-NiSe was applied under solar light illumination for the photocatalytic activity of Erythrosine and Allura red dyes. The CS-NiSe presented the highest degradation efficiency of 99.53% for Erythrosine dye in optimized experimental conditions of 100 min at 30 °C, 30 ppm concentration, pH 5.0, and 0.14 g catalyst dose. For Allura red dye, a high degradation of 96.12% was attained in 120 min at pH 4.0, 100 ppm initial dye concentration, 35 °C temperature, and 0.1 g catalyst dose. The CS-NiSe showed excellent degradation efficiency and reduced to (95% for Erythrosine and 91% for Allura red dye) after five consecutive batches. Moreover, the statistical and neural network modelling analysis showed the significant influence of all studied variables on dyes degradation performance. The results demonstrated that CS-NiSe exhibited excellent photocatalytic performances for Erythrosine and Allura red dyes and could be a better photocatalyst for removing these dyes from industrial effluents.

Valorizing lignin-like dyes and textile dyeing wastewater by a newly constructed lipid-producing and lignin modifying oleaginous yeast consortium valued for biodiesel and bioremediation

Construction of a multipurpose yeast consortium suitable for lipid production, textile dye/effluent removal and lignin valorization is critical for both biorefinery and bioremediation. Therefore, a novel oleaginous consortium, designated as OYC-Y.BC.SH has been developed using three yeast cultures viz. Yarrowia sp. SSA1642, Barnettozyma californica SSA1518 and Sterigmatomyces halophilus SSA1511. The OYC-Y.BC.SH was able to grow on different carbon sources and accumulate lipids, with its highest lipid productivity (1.56 g/L/day) and lipase activity (170.3 U/mL) exhibited in xylose. The total saturated fatty acid content was 36.09 %, while the mono-unsaturated and poly-unsaturated fatty acids were 45.44 and 18.30 %, respectively, making OYC-Y.BC.SH valuable for biodiesel production. The OYC-Y.BC.SH showed its highest decolorization efficiency of Red HE3B dye (above 82 %) in presence of sorghum husk as agricultural co-substrate, suggesting its feasibility for simultaneous lignin valorization. The significant higher performance of OYC-Y.BC.SH on decolorizing the real dyeing effluent sample at pH 8.0 suggests its potential and suitability for degrading most of the wastewater textile effluents. Clearly, toxicological studies underline the additional advantage of using OYC-Y.BC.SH for bioremediation of industrial dyeing effluents in terms of decolorization and detoxification. A possible mechanism of Red HE3B biodegradation and ATP synthesis was also proposed.