Efficient removal of cationic dyes using lemon peel-chitosan hydrogel composite: RSM-CCD optimization and adsorption studies.

The most prominent and easily identifiable factor of water purity is its colour, which may be both physically undesirable, and act as an alert towards potential environmental contamination. The current study describes the optimum synthesis technique for Lemon Peel-Chitosan hydrogel using the Response Surface Methodology integrated Central composite Design (RSM-CCD). This adsorbent is both environmentally friendly and cost-effective. The hydrogel exhibited a maximal dye removal capacity of 24.984, 24.788, 24.862, 23.483, 24.409, and 24.726 mg g-1, for 10 mg L-1 aqueous medium of Safranin O, Methylene blue, Basic fuchsin, Toluidine blue, Brilliant green and Crystal violet, respectively. The adsorption kinetics and isotherm data suggest that the Pseudo second-order kinetic and Freundlich adsorption isotherm models precisely represent the respective behaviour of all the dyes. The thermodynamic viability of the process is determined by the values of ΔG, ΔH, and ΔS. The probable mechanism of adsorption was the electrostatic interaction between the dye molecules and the hydrogel. The regenerated hydrogel had removal efficiencies of over 80 % even after enduring six cycles. Hence, the exceptional recyclability and utility of the adsorbent show their sustainability for wastewater treatment in textile factories.

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network.

This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg-Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e-05 for the hydrogen mole fraction and 4.88e-05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

Response surface methodology and machine learning optimisations comparisons of recycled AA6061-B4C-ZrO2 hybrid metal matrix composites via hot forging forming process.

The optimal conditions of applied factors to reuse Aluminium AA6061 scraps are (450, 500, and 550) °C preheating temperature, (1-15) % Boron Carbide (B4C), and Zirconium (ZrO2) hybrid reinforced particles at 120 min forging time via Hot Forging (HF) process. The response surface methodology (RSM) and machine learning (ML) were established for the optimisations and comparisons towards materials strength structure. The Ultimate Tensile Strength (UTS) strength and Microhardness (MH) were significantly increased by increasing the processed temperature and reinforced particles because of the material dispersion strengthening. The high melting point of particles caused impedance movements of aluminium ceramics dislocations which need higher plastic deformation force and hence increased the material's mechanical and physical properties. But, beyond Al/10 % B4C + 10 % ZrO2 the strength and hardness were decreased due to more particle agglomeration distribution. The optimisation tools of both RSM and ML show high agreement between the reported results of applied parameters towards the materials' strength characterisation. The microstructure analysis of Field Emission Scanning Electron Microscopy (FE-SEM) and Atomic Force Microscope (AFM) provides insights mapping behavioural characterisation supports related to strength and hardness properties. The distribution of different volumes of ceramic particle proportion was highlighted. The environmental impacts were also analysed by employing a life cycle assessment (LCA) to identify energy savings because of its fewer processing steps and produce excellent hybrid materials properties.

Optimization of process conditions for ionic liquid-based ultrasound-enzyme-assisted extraction of resveratrol from Polygonum Cuspidatum.

This work offered a productive technique for resveratrol extraction from Polygonum Cuspidatum (P. Cuspidatum) using ionic liquids in synergy with ultrasound-enzyme-assisted extraction (UEAE). Firstly, ionic liquids with different carbon chains and anions were evaluated. Subsequently, a comprehensive investigation was carried out to evaluate the effect of seven crucial parameters on the resveratrol yield: pH value, enzyme concentration, extraction temperature, extraction time, ultrasonic power, concentration of ionic liquid (IL concentration) and the liquid-solid ratio. Employing the Plackett-Burman Design (PBD), the critical factors were effectively identified. Building upon this foundation, the process was further optimized through the application of Response Surface Methodology (RSM) and an Artificial Neural Network-Genetic Algorithm (ANN-GA). The following criteria were determined to be the ideal extraction conditions: an enzyme concentration of 2.18%, extraction temperature of 58 °C, a liquid-solid ratio of 29 mL/g, pH value of 5.5, extraction time of 30 min, ultrasonic power of 250 W, and extraction solvent of 0.5 mol/L 1-butyl-3-methylimidazolium bromide. Under these conditions, the resveratrol yield was determined to be 2.90 ± 0.15 mg/g. Comparative analysis revealed that the ANN-GA model provided a better fit to the experimental data of resveratrol yield than the RSM model, suggesting superior predictive capabilities of the ANN-GA approach. The introduction of a novel green solvent system in this experiment not only simplifies the extraction process but also enhances safety and feasibility. This research paves the way for innovative approaches to extracting resveratrol from botanical sources, showcasing its significant potential for a wide range of applications.

Screening the Protective Agents Able to Improve the Survival of Lactic Acid Bacteria Strains Subjected to Spray Drying Using Several Key Enzymes Responsible for Carbohydrate Utilization.

The aim of this study was to identify the most effective protectants for enhancing the viability of specific lactic acid bacteria (LAB) strains (Lactobacillus delbrueckii subsp. bulgaricus CICC 6097, Lactiplantibacillus plantarum CICC 21839, Lactobacillus acidophilus NCFM) by assessing their enzymatic activity when exposed to spray drying (inlet/outlet temperature: 135 °C/90 °C). Firstly, it was found that the live cell counts of the selected LAB cells from the 10% (w/v) recovered skim milk (RSM) group remained above 107 CFU/g after spray drying. Among all the three groups (1% w/v RSM group, 10% w/v RSM group, and control group), the two enzymes pyruvate kinase (PK) and lactate dehydrogenase (LDH) were more sensitive to spray drying than hexokinase (HK) and ß-galactosidase (ß-GAL). Next, transcriptome data of Lb. acidophilus NCFM showed that 10% (w/v) RSM improved the down-regulated expressions of genes encoding PK (pyk) and LDH (ldh) after spray drying compared to 1% (w/v) RSM. Finally, four composite protectants were created, each consisting of 10% (w/v) RSM plus a different additive-sodium glutamate (CP-A group), sucrose (CP-B group), trehalose (CP-C group), or a combination of sodium glutamate, sucrose, and trehalose (CP-D group)-to encapsulate Lb. acidophilus NCFM. It was observed that the viable counts of strain NCFM (8.56 log CFU/g) and enzymatic activity of PK and LDH in the CP-D group were best preserved compared to the other three groups. Therefore, our study suggested that measuring the LDH and PK activity could be used as a promising tool to screen the effective spray-dried protective agent for LAB cells.

Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies.

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soils contaminated with heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving an improvement of nearly 2.45 times the initial yield under specific conditions (Bennet's medium supplemented with glycerol [5 g/L] and casamino-acid [3 g/L] at 30 °C for 72 h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using energy-dispersive X-ray spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0.2 W/cm2), with temperatures increasing to 41.7 °C, after 30 min. In addition, treatment with light irradiation of E. coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5 µg/mL, thereby enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12.

Optimization of Cohesive Parameters in the Interfacial Transition Zone of Rubberized Concrete Based on the Response Surface Method.

Rubber concrete has been applied to a certain extent in fatigue-resistant structures due to its good durability. Based on a cohesive model of rubber composed of a five-phase material containing mortar, aggregate, rubber, aggregate-mortar interfacial transition zone (ITZ), and rubber-mortar ITZ, this paper studies the influence of the cohesive parameters in the rubber-mortar ITZ on the fatigue problem of rubber concrete on the mesoscopic scale. As the weak part of cement-based composite materials, the ITZ has a great influence on the mechanical properties and durability of concrete, but the performance of the ITZ is difficult to test in macro experiments, resulting in difficulties in determining its simulation parameters. Based on the cohesive model with a rubber content of 5%, this study uses Monofactor analysis and the Plackett-Burman test to quickly and effectively determine the primary and secondary influences of the cohesive model parameters in the rubber-mortar ITZ; further, the response surface method is used to optimize the cohesive parameters in the rubber-mortar ITZ, and the numerical simulation results after optimizing the cohesive parameters are compared and analyzed with the simulation results before optimization. The results show that, under the setting of the optimized parameters, the simulation results of each item of the optimal cohesive model parameters in the rubber-mortar ITZ are in line with the reality and closer to the experimental data, and they are also applicable to rubber concrete models with different rubber dosing.

Surfactant-modified coconut coir powder (SMCCP) as a low-cost adsorbent for the treatment of dye-contaminated wastewater: parameters and adsorption mechanism.

The dye-contaminated wastewater discharged from various industries such as dye manufacturing, paint, textile, paper, and cosmetic is a prime source of surface water pollution having serious detrimental effects on both the environment and human beings. These hazardous dyes when exposed to water obstruct the penetration of sunlight into the water and thus restrain aquatic plants from generating photosynthetic compounds. Moreover, some dyes are potential cancer-causing and also negatively impact the human nervous and respiratory systems. In this current study, modification of coconut coir powder (CCP) was carried out through cationic surfactant treatment and was successively utilized as the adsorbent for decoloring anionic dye (acid blue 185 (AB 185)) containing waste stream. Further, a comparative investigation of the dye removal efficiency of raw CCP and surfactant-modified coconut coir powder (SMCCP) as the adsorbent was studied. On surfactant treatment, using a very minimal SMCCP dosage of 8.3 g/L, a very high percentage dye removal of 98.4% is possible, whereas with raw CCP, even after using a higher dosage of 14 g/L, only 70.1% dye removal can be achieved. Characterization of SMCCP adsorbent was done by Fourier transform infrared, thermogravimetric, X-ray, and scanning electron microscope analyses. Furthermore, the optimization of critical operating parameters was investigated for the effective adsorption of AB 185 dye in batch mode. The adsorption of AB 185 onto SMCCP was a thermodynamically spontaneous endothermic process, following the Langmuir isotherm and pseudo-second-order kinetic model. Moreover, regeneration of exhausted SMCCP by 0.1 (M) NaOH was achieved with a satisfactorily high recovery of 97% in the first cycle. Subsequently, SMCCP can be successfully reutilized for five consecutive cycles with a loss of 17.6% in the total adsorption capacity. With all such advantages, the present study delivers a new paradigm to utilize the novel adsorbent SMCCP as a promising eco-friendly adsorbent aided by its advantage of regeneration and reusability for the treatment of dye-contaminated wastewater.

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools.

A methodology combining physical experiments with simulation was employed to acquire contact parameters of sandy soil precisely for planting tiger nuts in the desert area of Xinjiang. The stacking angle under different parameter combinations was applied as a response value. Through the Plackett-Burman test, several factors that have a significant influence were determined. The steepest ascent test was conducted to establish the finest scope of values for these parameters. The stacking angle was considered the response variable, and non-linear tools were used to optimize these parameters for simulation. The findings showed that applying response surface methodology (RSM) resulted in a relative error of 1.24%. In the case of BP-GA, the relative error compared to the physical test value was 0.34%, while for BP, it was 2.18%. After optimization using Wavelet Neural Network (WNN), the relative error was reduced to only 0.15%. Results suggest that WNN outperforms the RSM model, and the sandy soil model and parameters generated using WNN can be effectively utilized for discrete element simulation research.

Development and Evaluation of Calcium-Fortified Multi-Millet Biscuits: A Nutritious Alternative to Refined Wheat Flour.

Millet products have garnered global recognition for their exceptional nutritional profile, appealing to various age demographics, and, therefore, fortifying such products with minerals can ensure nutritional security. This research explores the feasibility of utilizing millet as a substitute for refined wheat flour in biscuit production. Three distinct millet varieties were investigated: finger, pearl, and buckwheat. Employing response surface methodology (RSM), the optimal ratio of these flours was determined, resulting in a blend of 1.5:1:1, respectively. The optimized multi-millet biscuits were further enhanced with calcium fortification and subjected to comprehensive physico-chemical analysis. Proximate composition analysis revealed favorable levels of protein (5.472 ± 0.31%), ash (2.80 ± 0.57 g/100 g), and energy density (5.8015 ± 0.004 kcal/g), indicating a significantly higher protein content, enriched mineral profile, and high energy density as compared to refined wheat flour products. Sensory evaluation encompassing attributes such as color and texture and organoleptic assessment using a nine-point hedonic scale demonstrated favorable acceptance. Additionally, the overall acceptability of the biscuits remained consistently high throughout storage, ranging from 8.263 ± 0.65 (day 0) to 8.053 ± 0.85 (day 14). This study underscores the potential of multi-millet biscuits as a nutritious and palatable alternative to traditional wheat-based snacks, offering an avenue for diversifying dietary options and promoting healthier food choices.

Simultaneous synthesis of super-paramagnetic hydrochar in a one-pot using subcritical water medium and evaluation of its photocatalytic activity.

The unregulated release of chemical dyes into the environment presents considerable environmental hazards when left untreated. Photocatalytic degradation, acknowledged as an eco-friendly and cost-effective method, has garnered attention for its efficacy in eliminating organic pollutants like dye. Consequently, the development of multifunctional materials with different applications in environmental and catalytic fields emerges as a promising avenue. Recognizing the significance of integrating catalysts and porous materials for enhancing interactions between pollutants and photo-sensitive substances, magnetic hydrochar emerges as a solution offering heightened efficiency, scalability, recyclability, and broad applicability in various environmental processes, notably wastewater treatment, due to its facile separation capability. In this study, Fe3O4-based, super-paramagnetic hydrochar (SMHC) was simultaneously synthesized in a single step using a coconut shell in the subcritical water medium. A thorough analysis was conducted on both raw hydrochar (RHC) and SMHC to unravel the mechanism of interaction between Fe3O4 nanoparticles and the hydrochar matrix. The synthesized hydrochar exhibited super-paramagnetic characteristics, with a saturation magnetization of 23.7 emu/g and a magnetic hysteresis loop. SMHC displayed a BET surface area of 42.6 m2/g and an average pore size of 26.3 nm, indicating a mesoporous structure according to nitrogen adsorption-desorption isotherms. XRD analysis revealed magnetic crystal sizes in the obtained SMHC to be 13.7 nm. The photocatalytic performance of SMHC was evaluated under visible light exposure in the presence of H2O2 for Astrazon yellow (AY) dye degradation, with optimization conducted using response surface methodology (RSM). The most substantial dye removal, reaching 92.83%, was achieved with 0.4% H2O2 at a 20 mg/L dye concentration and an 80-min reaction duration.

Eco-friendly engineering of micro composite-based hydroxyapatite bio crystal and polyaniline for high removal of OG dye from wastewater: Adsorption mechanism and RSM@BBD optimization.

The presence of harmful substances such as dyes in water systems poses a direct threat to the quality of people's lives and other organisms living in the ecosystem. Orange G (OG) is considered a hazardous dye. The existing paper attempts to evaluate a low-cost adsorbent for the effective removal of OG dye. The developed adsorbent Polyaniline@Hydroxyapatite extracted from Cilus Gilberti fish Scale (PANI@FHAP) was elaborated through the application of the in situ chemical polymerization method to incorporate PANI on the surface of naturally extracted hydroxyapatite FHAP. The good synthesis of PANI@FHAP was evaluated through multiple techniques including X-ray diffraction (XRD), Scanning electron microscopy coupled with energy dispersive X-ray spectrometry (SEM/EDS), Fourier Transforms Infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) coupled with thermal differential analysis (DTA) analysis. The results reveal a highly ordered disposition of PANI chains on FHAP, resulting in a well-coated FHAP in the PANI matrix. Furthermore, the presence of functional groups on the surface of PANI such as amine (-NH2) and imine (=NH) groups would facilitate the removal of OG dye from contaminated water. The adsorption of OG onto PANI@FHAP was conducted in batch mode and optimized through response surface methodology coupled with box-Behnken design (RSM/BBD) to investigate the effect of time, adsorbent dose, and initial concentration. The outcomes proved that OG adsorption follows a quadratic model (R2 = 0.989). The kinetic study revealed that the adsorption of OG fits the pseudo-second-order model. On the other hand, the isotherm study declared that the Freundlich model is best suited to the description of OG adsorption. For thermodynamic study, the adsorption of OG is spontaneous in nature and exothermic. Furthermore, the regeneration-reusability study indicates that PANI@FHAP could be regenerated and reused up to five successive cycles. Based on the FTIR spectrum of PANI@FHAP after OG adsorption, the mechanism governing OG adsorption is predominantly driven by π-π interaction, electrostatic interaction, and hydrogen bonding interactions. The obtained results suppose that PANI@FHAP adsorbent can be a competitive material in large-scale applications.

Enhanced production of lactic acid from pretreated rice straw using co-cultivation of Bacillus licheniformis and Bacillus sonorenesis.

Lactic acid (LA) production from sugar mixture derived from lignocellulosic rice straw employing co- culture system of thermotolerant and inhibitor tolerant Bacillus licheniformis DGB and Bacillus sonorenesis DGS15 was carried out. In minimal media, both the strains of Bacillus DGB and DGS15 worked together by efficiently utilising glucose and xylose respectively. Response Surface Methodology (RSM) was used for optimisation of pretreatment of rice straw to achieve maximum yield of 50.852 g/L total reducing sugar (TRS) from 100 gm of rice straw biomass. Pretreatment of rice straw resulted in its delignification, as confirmed by FTIR spectroscopy, since the peak at 1668 cm-1 disappeared due to removal of lignin and scanning electron microscopy (SEM) revealed disruption in structural and morphological features. Crystallinity index (CrI) of treated rice straw increased by 15.54% in comparison to native biomass. DGB and DGS15 individually yielded 0.64 g/g and 0.82 g/g lactic acid respectively, where as their co-cultivation led to effective utilisation of both glucose and xylose within 15 h (70%) and complete utilisation in 48 h, producing 49.75 g/L LA with a yield of 0.98 g/g and productivity of 1.036 g/L/h, and resulting in reduction in fermentation time. Separate hydrolysis of rice straw and co-fermentation (SHCF) of hydrolysates by Bacillus spp. enhanced the production of lactic acid, can circumvent challenges in biorefining of lignocellulosic biomass.

Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools.

Ultra-precision turning is a crucial process in the manufacturing industry as it helps to produce parts with high dimensional accuracy, surface finish, and tolerance. The process is similar to traditional turning but is carried out under special circumstances to achieve greater precision and surface finish. The process can be applied to conventional structural materials, but the demand for machining hardened steels is increasing. The optimization of ultra-precision turning of AISI D2 using cubic boron nitride (CBN) tools is a crucial aspect in the field of high-quality machining. This study aims to evaluate the performance of the process and identify the optimal parameters that result in the best quality components while using a CBN tool's ultra-precision turning of AISI D2. Ultra-precision turning process factors such as cutting speed, feed, and depth of cut were experimentally investigated to enhance the response output, such as surface roughness and cutting force components. The full factorial experimental design was used for determining the process characteristics under different conditions, and experimental results were applied to search for the optimum response of machining performance. The optimization process was done by combining the hybrid genetic algorithm-response surface methodology (GA-RSM) and the Taguchi-grey relational analysis (GRA) statistical tools. These methods are useful in situations where the relationship between the input variables and the output responses is complex and non-linear. The results showed that a hybrid GA-RSM approach, combined with Taguchi-GRA statistical analysis, can effectively find optimal process parameters, leading to the best combination of surface roughness and cutting force. In hybrid Taguchi - GRA, the optimal cutting conditions were found to be a cutting speed of 175 m/min, a feed of 0.025 mm, and a depth of cut of 0.06 mm. The findings of this study provide valuable insights for the optimization of ultra-precision CBN turning operations, contribute to the development of precision manufacturing technology, and can be used as a reference for similar machining processes.

Optimization of antimicrobial nanocomposite films based on carboxymethyl cellulose incorporating chitosan nanofibers and Guggul gum polysaccharide.

The present study utilized response surface methodology (RSM) to investigate the impact of varying concentrations of carboxymethyl cellulose (CMC: 0.75-1.75 wt%), Commiphora mukul polysaccharide (CMP: 0-1 wt%), and Chitosan Nanofiber (CHNF: 0-1 wt%) on the physical and antimicrobial characteristics of nanocomposite films based on CMC. The optimization process aimed to enhance ultimate tensile strength (UTS), strain at break (SAB), and antibacterial activity, while minimizing water vapor permeability (WVP), solubility, swelling, moisture content, opacity, and total color difference (ΔE). The results revealed that both CMP and CHNF had a positive influence on reducing moisture content, WVP, and increasing UTS. However, higher concentrations of CMP and CHNF had a divergent effect on SAB, ΔE, and swelling. The incorporation of CMP led to increased opacity and solubility, while the inclusion of CHNF resulted in decreased opacity and solubility. Notably, only CHNF addition significantly improved the antibacterial properties of the films. By applying the optimization procedure utilizing RSM, the formulation containing CMC (1.5 wt%), CMP (0.25 wt%), and CHNF (0.75 wt%) demonstrated superior physical, mechanical, and antibacterial properties in the biodegradable film matrix. These findings highlight the potential of utilizing these components to enhance the performance of CMC-based nanocomposite films.

A comprehensive study on the impact of human hair fiber and millet husk ash on concrete properties: response surface modeling and optimization.

Revolutionizing construction, the concrete blend seamlessly integrates human hair (HH) fibers and millet husk ash (MHA) as a sustainable alternative. By repurposing human hair for enhanced tensile strength and utilizing millet husk ash to replace sand, these materials not only reduce waste but also create a durable, eco-friendly solution. This groundbreaking methodology not only adheres to established structural criteria but also advances the concepts of the circular economy, representing a significant advancement towards environmentally sustainable and resilient building practices. The main purpose of the research is to investigate the fresh and mechanical characteristics of concrete blended with 10-40% MHA as a sand substitute and 0.5-2% HH fibers by applying response surface methodology modeling and optimization. A comprehensive study involved preparing 225 concrete specimens using a mix ratio of 1:1.5:3 with a water-to-cement ratio of 0.52, followed by a 28 day curing period. It was found that a blend of 30% MHA and 1% HH fibers gave the best compressive and splitting tensile strengths at 28 days, which were 33.88 MPa and 3.47 MPa, respectively. Additionally, the incorporation of increased proportions of MHA and HH fibers led to reductions in both the dry density and workability of the concrete. In addition, utilizing analysis of variance (ANOVA), response prediction models were created and verified with a significance level of 95%. The models' R2 values ranged from 72 to 99%. The study validated multi-objective optimization, showing 1% HH fiber and 30% MHA in concrete enhances strength, reduces waste, and promotes environmental sustainability, making it recommended for construction.

Experimental, RSM modelling, and DFT simulation of CO2 adsorption on Modified activated carbon with LiOH.

This research investigates the enhancement of CO2 adsorption capacity through the use of modified activated carbon (AC) with LiOH, focusing on operational conditions and adsorbent properties. Response Surface Methodology (RSM) is employed to optimize process parameters for maximizing CO2 adsorption capacity. The study considers temperature, pressure, LiOH concentration for modification, and adsorbent weight as independent variables across five levels. Analysis of Variance reveals that LiOH concentration, adsorbent quantity, pressure, and temperature significantly influence CO2 adsorption. Optimal values for temperature (30°C), pressure (9 bar), LiOH concentration (0.5 mol/L), and adsorbent weight (0.5 g) result in a maximal CO2 adsorption capacity of 154.90 mg/g. Equilibrium adsorption capacity is utilized for modeling, with the Freundlich model proving suitable for CO2 adsorption on LiOH-AC. Kinetic modeling indicates the second-order model's suitability for temperatures of 30 °C and 50 °C, while the Elovich model fits temperatures of 70 °C and 90 °C. Thermodynamic modeling at the optimized conditions (303 K and 6 bar) yields ∆H, ∆S, and ∆G values of adsorption as 12.258 kJ/mol, - 0.017 kJ/mol·K, and - 7.031 kJ/mol, respectively. Furthermore, structural considerations of AC are discussed alongside modeling and simulation, presenting the adsorption rate of CO2 and the binding energy index based on Density Functional Theory (DFT).

Optimal extraction conditions and quantification of lignan phytoestrogens in cereal grains using targeted LC-MS/MS.

Lignans are phytoestrogens found in various forms such as glycosides, ester-linked oligomers, and aglycones in a variety of foods, including soy products, legumes, grains, nuts, vegetables, and fruits. This study aimed to optimize the extraction of lignans from cereal grains using response surface methodology (RSM). Lignans, including secoisolariciresinol (Seco), matairesinol (Mat), pinoresinol (Pin), lariciresinol (Lar), and syringaresinol (Syr), were quantified using high-performance liquid chromatography-tandem mass spectrometry. A Box-Behnken design was employed to determine the optimal values for three extraction parameters: temperature (X1: 20°C-60°C), methanol concentration (X2: 60%-100%), and extraction time (X3: 30-90 min). The highest lignan contents were obtained at X1 = 44.24°C, X2 = 84.64%, and X3 = 53.63 min. To apply these experimental conditions to the actual experiment, the optimal conditions were slightly adjusted to X1 = 40°C, X2 = 80%, and X3 = 60 min. The predicted results closely matched the experimental results obtained using the modified optimal extraction conditions. The highest lignan content found in barley sprouts (85.930 µg/100 g), however, most grains exhibited relatively low concentrations of lignans. These findings provide valuable insights into the lignan content of grains and contribute to the generation of reliable data in this field.

Infusion of active compound into sliced button mushrooms through vacuum impregnation to improve functionality: Comparing response surface methodology and artificial neural network.

The present study explores the infusion of active compounds (ascorbic acid and calcium lactate) into sliced button mushrooms (Agaricus bisporus) to increase the nutritional value and reduce the browning effect of sliced mushrooms using the vacuum impregnation (VI) technique. The aim was to functionalize the vacuum-infused sliced mushrooms and evaluate the physicochemical properties of button mushrooms for diversifying food use. The central composite design was implemented to determine the optimized condition for the process with four independent factors, that is, immersion time (IT) 30-90 min, solution temperature (ST) 35-55°C, solution concentration (SC) 4%-12%, and vacuum pressure (VP) 50-170 mbar. The optimum VI processes obtained were ST-40°C, SC-8%, VP-140 mbar, and IT-65 min with a desirability function of 0.77. Statistically, two models (response surface methodology [RSM] and artificial neural network [ANN]) were employed to compare the better performance for the prediction of VI operational process parameters. The RSM model showed a better prediction of VI process parameters than the ANN model, with a higher R2 value (0.9228 vs. 0.8160) and lower root mean square error value (1.4004 vs. 2.1751), χ2 (2.4491 vs. 5.2762), mean absolute error (1.1177 vs. 1.1611), and absolute average deviation (4.3532 vs. 5.6746) for water loss. A similar pattern was observed for solute gain, ascorbic acid, titratable acidity, color change, firmness, and pH. Therefore, the VI process was found to be an effective method for enhancing the nutritional properties of sliced mushrooms. These findings concluded that the RSM model is more efficient for better prediction with good accuracy of the VI process than the ANN model.

Investigation and characterization of changes in potato peels by thermochemical acidic pre-treatment for extraction of various compounds.

Potato peel waste (PPW) is an underutilized substrate which is produced in huge amounts by food processing industries. Using PPW a feedstock for production of useful compounds can overcome the problem of waste management as well as cost-effective. In present study, potential of PPW was investigated using chemical and thermochemical treatment processes. Three independent variables i.e., PPW concentration, dilute sulphuric acid concentration and liberation time were selected to optimize the production of fermentable sugars (TS and RS) and phenolic compounds (TP). These three process variables were selected in the range of 5-15 g w/v substrate, 0.8-1.2 v/v acid conc. and 4-6 h. Whole treatment process was optimized by using box-behnken design (BBD) of response surface methodology (RSM). Highest yield of total and reducing sugars and total phenolic compounds obtained after chemical treatment was 188.00, 144.42 and 43.68 mg/gds, respectively. The maximum yield of fermentable sugars attained by acid plus steam treatment were 720.00 and 660.62 mg/gds of TS and RS, respectively w.r.t 5% substrate conc. in 0.8% acid with residence time of 6 h. Results recorded that acid assisted autoclaved treatment could be an effective process for PPW deconstruction. Characterization of substrate before and after treatment was checked by SEM and FTIR. Spectras and micrographs confirmed the topographical variations in treated substrate. The present study was aimed to utilize biowaste and to determine cost-effective conditions for degradation of PWW into value added compounds.