Second-order Model Research Articles

Comparison of the Three Types of Central Composite Designs Over Subsets of Reduced Models by Design Optimality Criteria

The purpose of this article is to compare the 3 important types of central composite designs (CCDs) consisting of central composite circumscribed design (CCCD), central composite inscribed design (CCID), and central composite face-centroid design (CCFD) in response surface methodology (RSM). The difference among these designs is the distance from the center design to the axial points. The comparison was performed across the full second-order response surface model and across a set of reduced models for 3, 4, and 5 design factors ( 3, 4, and 5) including 1, 3, and 5 center runs ( 1, 3, and 5). This study used D-, A-, and G- optimality criteria to evaluate the performance of CCDs by presenting the design optimality criteria comparison ranking throughout the reduced-model subsets of 43, 224, and 839 models for 3, 4, and 5 design variables, respectively. The results showed that CCCD was superior to CCID and CCFD according to A- and D- optimality criteria, while CCCD and CCID performed better than CCFD based on G- optimality criterion over a set of reduced models for 3, 4, and 5 design factors. It was observed that D-, A-, and G- optimality efficiencies were robust to changes in the linear and cross-product terms and sensitive to deviation in the square terms. The study will provide recommendations to assist the experimenters in the choice of the best design among the candidate designs for practice applications when some model effects may be insignificant. HIGHLIGHTS Comparison of 3 classifications of CCD. Assessment by using D-, A-, and G- optimality criteria over the reduced-model subsets for the full second-order response surface model. Generated the reduced models by using weak heredity. CCCD was superior to CCID and CCFD according to A- and D- optimality criteria, while CCCD and CCID performed better than CCFD based on G- optimality criterion over a set of reduced models for 3, 4, and 5 design factors. GRAPHICAL ABSTRACT

Optimization methodology combining Taguchi design and response surface method to maximize a compression ignition engine efficiency fueled with oxygenated synthetic fuel

This research examines the potential of a synthetic e-diesel and oxymethylene dimethyl ether blend in a heavy-duty single-cylinder engine. The main purpose is to find the optimal engine settings that maximize engine efficiency and minimize emissions. As the first step, a methodology was developed and applied to simplify the engine’s map into five characteristic points to be representative of the World Harmonized Transient Cycle, assigning weights to each load region. This simplification reported low error estimations for fuel consumption and NOx emissions over the cycle. After obtaining the five points, a drop-in analysis was assessed, where the fuel was evaluated with the same settings as diesel, revealing more advanced and shorter combustion with great reductions in soot emissions. Then, a group of three air management and three injection parameters was selected for optimization. A novel methodology combining Taguchi Design and Response Surface Methodology is developed and applied to find the best engine settings. By integrating these two methods, the research reduced the number of required experiments if only one method were used and maintained the goal of achieving higher thermal efficiencies with the new fuel blend compared to the diesel baseline. Significant statistical results were obtained in the Taguchi design, revealing the air management factors that most affected the responses; meanwhile, second-order models with high accuracy helped in finding the best injection parameters. Subsequently, the best settings were experimentally evaluated, and optimal conditions determined by the two methods were validated, confirming the effectiveness of the methodology. Optimization results revealed an improvement of around 2% higher efficiencies for the five tested points while achieving important average reductions of 28% less in NOx with additional decreases in CO, HC, and soot levels, all outperforming traditional diesel benchmarks. Finally, a well-to-wheel analysis using the simplified cycle confirmed a substantial reduction in carbon impact when replacing diesel with this alternative fuel. The successful application of this methodology to recalibrate an engine with an alternative fuel blend shows the potential of using these fuels in existing engines and offers a novel methodology that can be escalated and applied under the engine calibration context.

Multi-objective optimization for connected and automated truck platoon control with improved CACC model

Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.

Optimising nanoparticles: harnessing quality by design through 3-level factorial design and box-behnken methodology

Nanoparticles (NPs) have become essential elements in a number of scientific and industrial domains, such as materials research, drug delivery, and diagnostics, because of their special qualities and potential for focused uses. An ideal formulation procedure is essential to maximising the effectiveness and efficiency of nanoparticles. In order to optimise the formulation and production processes for nanoparticles, this research investigates the application of Quality by Design (QbD) principles in conjunction with sophisticated statistical approaches, namely the 3-level factorial design and the Box-Behnken methodology. The Quality by Design methodology is a methodical approach that prioritises predetermined goals, in-depth process comprehension, and careful control grounded in reliable science and quality risk management. When QbD is incorporated into nanoparticle optimisation, reliable and repeatable formulations that adhere to specifications with little variation are guaranteed. To look into how different factors interact and affect the properties of nanoparticles, the 3-level factorial design is used. With this design approach, a thorough understanding of the process variables can be obtained by exploring a broad variety of component values. Critical quality attributes (CQAs) include important parameters such drug encapsulation efficiency, zeta potential, polydispersity index (PDI), and particle size. These parameters are methodically examined to determine the ideal amounts of each. To further refine the 3-level factorial design, the Box-Behnken methodology is applied. Without the requirement for extensive combination testing, this response surface methodology (RSM) is especially useful for building second-order polynomial models and investigating quadratic response surfaces. By enabling a more accurate comprehension of the interactions between inputs and responses, the Box-Behnken design improves the optimisation process and makes it easier to identify the ideal circumstances for nanoparticle synthesis. By utilising both approaches in tandem, this work methodically determines ideal formulation parameters that minimise nanoparticle size and polydispersity while optimising stability and drug loading efficiency. The outcomes highlight how important a QbD framework is for directing the creation of reliable nanoparticle systems that function consistently and predictably.

Colour Analysis of Sausages Stuffed with Modified Casings Added with Citrus Peel Extracts Using Hyperspectral Imaging Combined with Multivariate Analysis

Recycling citrus peel waste offers several significant contributions to sustainability, transforming what would otherwise be discarded into valuable resources. In this study, the colour of sausages stored for 16 days, with varying amounts of orange extract added to the modified casing solution, was evaluated using response surface methodology (RSM) and a hyperspectral imaging system within the spectral range of 350–1100 nm for the first time. To enhance model performance, spectral pre-treatments such as normalisation, first derivative, standard normal variate (SNV), second derivative, and multiplicative scatter correction (MSC) were applied. Both raw and pre-treated spectral data, along with colour attributes, were fitted to a partial least squares regression model. The RSM results indicated that the highest R2 value, 80.61%, was achieved for the b* (yellowness) parameter using a second-order polynomial model. The interactive effects of soy oil and orange extracts on b* were found to be significant (p < 0.05), and the square effects of soy oil on b* were significant at the 1% level. The identified key wavelengths for colour parameters can simplify the model, making it more suitable for practical industrial applications.

Shelf life and storage stability of cold plasma treated kiwifruit juice: kinetic models

ABSTRACT This study examined the quality changes and shelf life of cold plasma (Cold plasma-optimized, S2: 30 kV/5 mm/6.7 min and Cold plasma-extreme, S3: 30 kV/2 mm/10 min) and thermally-treated (S4: 90°C/5 min) kiwifruit juice packed in glass and polyethylene terephthalate bottles stored at 5, 15, and 25°C. Cold plasma and thermally treated juice samples were analyzed for peroxidase activity, microbial enumeration, bioactive compounds degradation, and sensory quality for up to 120 days. The residual activity of peroxidase enzyme in S2 sample after cold plasma treatment was 23.85%, decreasing with increase in storage time and temperature. After cold plasma and thermal treatment, the aerobic mesophiles and yeasts and molds count were below detectable limits in kiwifruit juice. Aerobic mesophiles and yeasts & molds emerged in S2, S3, and S4 samples stored at 5°C after 70, 70, and 90 days, respectively. The total color change increased with increase in storage time and temperature, whereas ascorbic acid, total phenolic content, total antioxidant capacity, and overall acceptability decreased. The shelf life of S2 in glass bottles was 100, 80, and 60 days at 5, 15, and 25°C based on total color change ≥ 12, ascorbic acid loss ≥ 50%, microbial count ≥ 6 Log CFU/mL, or overall acceptability < 5. Quality indices, including total color change, overall sensory acceptability, and ascorbic acid, were modeled, and zero-order model performed better than first and second-order reaction models. Furthermore, Arrhenius, Eyring, and Ball models were employed to model temperature-dependent reaction rates, and the Ball model performed better. So, the zero-order reaction and Ball model were combined to predict kiwifruit juice’s shelf life.

Reduction of 2,4-dichlorophenoxy acetic acid herbicide toxicity from aqueous media by adsorption: Experimental and theoretical study using DFT method

This work studies the possibility of reducing the toxicity of water laden with 2,4-dichlorophenoxy acetic acid (2,4-D) using a commercial activated carbon (AC). Multiple structural and textural characterization techniques including Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM/EDS), point of zero charge (pHzpc) determination, Boehm titration, iodine number, Methylene blue index, and BET analysis measurements were performed on the commercial AC. Various parameters affecting adsorption were studied and well-known isotherms models in their linear and non-linear forms were applied for fitting equilibrium data. The kinetic data was analyzed using intraparticle diffusion, pseudo-first and second-order kinetic models. Density functional modeling (DFT) of 2,4-dichlorophenoxyacetic acid was used to investigate the origin of the reactivity. The AC surface area was found to be 1017 m2⋅g−1. A contact time of 30 min, an AC dose of 4 g⋅L−1 and a pH of 6.2 resulted in a maximum adsorption capacity of 235.55 ± 6.43 mg⋅g−1. The thermodynamic study revealed that the adsorption process was physical, spontaneous and endothermic. The adsorption process was governed by second-order kinetics and controlled by intraparticle diffusion.

Assessing Jealousy: Factor Analyses, Measurement Invariance, Nomological Validity, and Longitudinal APIM Analyses of the Multidimensional Jealousy Scale.

The Multidimensional Jealousy Scale is the standard instrument to assess cognitive, emotional, and behavioral jealousy. We examined competing factor models and external correlations with broad and narrow traits. Across two studies, we analyzed four samples (Ntotal = 2,117). Confirmatory factor analysis supported the measurement model of three correlated factors in comparison to unidimensional, second-order, and bifactor models. Thus, speaking against the use of a total score. Furthermore, we found measurement invariance between romantic partners. We extended the Multidimensional Jealousy Scale (MJS)' nomological net to personality pathology and replicated prior findings of associations with broad and narrow traits. Study 2 examined longitudinal data (5- to 9-month lag) from couples. Actor-Partner Interdependence Model analyses showed that the MJS predicts facets of relationship satisfaction in actors and partners. We discuss potential avenues for revising the MJS (e.g., heteronormative item wordings).

Robust distribution networks reconfiguration considering the improvement of network resilience considering renewable energy resources

The integration of renewable energy sources into smart distribution grids poses substantial challenges in maintaining grid stability, efficiency, and reliability due to their inherent variability and intermittency. This study addresses these challenges by proposing a novel two-level optimization model aimed at enhancing operational efficiency and robustness in smart distribution grids. The model synergistically integrates renewable energy sources, energy storage systems, electric vehicles, and demand-side management through a dynamic reconfiguration approach. It employs a robust optimization framework combined with a two-stage second-order cone optimization model to manage real-time operations and strategic grid reconfiguration. Key findings from simulations on the IEEE 33 and 69-bus networks underscore the model’s effectiveness. In the 33-bus system, implementing the demand response program led to a significant reduction in power losses, from 0.64 MW to 0.52 MW, and improved voltage stability, with the minimum voltage increasing from 0.970 to 0.980 p.u. Similarly, in the 69-bus system, power losses decreased from 0.85 MW to 0.79 MW, and voltage stability improved, with the minimum voltage rising from 0.962 to 0.972 p.u. The model also demonstrated reduced energy procurement needs, showcasing its impact on enhancing grid efficiency and reliability. These results highlight the model’s potential for advancing smart grid management strategies, offering significant improvements in operational performance and stability under varying demand conditions.

Parameter estimation for second-order SPDEs in multiple space dimensions

AbstractWe analyse a second-order SPDE model in multiple space dimensions and develop estimators for the parameters of this model based on discrete observations of a solution in time and space on a bounded domain. While parameter estimation for one and two spatial dimensions was established in recent literature, this is the first work which generalizes the theory to a general, multi-dimensional framework. Our approach builds upon realized volatilities, enabling the construction of an oracle estimator for volatility within the underlying model. Furthermore, we show that the realized volatilities have an asymptotic illustration as response of a log-linear model with spatial explanatory variable. This yields novel and efficient estimators based on realized volatilities with optimal rates of convergence and minimal variances. For proving central limit theorems, we use a high-frequency observation scheme. To showcase our results, we conduct a Monte Carlo simulation.

Comammox rather than AOB dominated the efficient autotrophic nitrification-denitrification process in an extremely oxygen-limited environment

The discovery of complete ammonia oxidizer (comammox) has challenged the traditional understanding of the two-step nitrification process. However, their functions in the oxygen-limited autotrophic nitrification-denitrification (OLAND) process remain unclear. In this study, OLAND was achieved using comammox-dominated nitrifying bacteria in an extremely oxygen-limited environment with a dissolved oxygen concentrations of 0.05 mg/L. The ammonia removal efficiency exceeded 97 %, and the total nitrogen removal efficiency reached 71 % when sodium bicarbonate was used as the carbon source. The pseudo-first- and second-order models were found to best fit the ammonia removal processes under low and high loads, respectively, suggesting distinct ammonia removal pathways. Full-length 16S rRNA gene sequencing and metagenomic results revealed that comammox-dominated under different oxygen levels, in conjunction with anammox and heterotrophic denitrifiers. The abundance of enzymes involved in energy metabolism indicates the coexistence of anammox and autotrophic nitrification-heterotrophic denitrification pathways. The binning results showed that comammox bacteria engaged in horizontal gene transfer with nitrifiers, anammox bacteria, and denitrifiers to adapt to an obligate environments. Therefore, this study demonstrated that comammox, anammox, and heterotrophic denitrifiers play important roles in the OLAND process and provide a reference for further reducing aeration energy in the autotrophic nitrogen removal process.

Optimization of Combined Hydrothermal and Mechanical Refining Pretreatment of Forest Residue Biomass for Maximum Sugar Release during Enzymatic Hydrolysis

This study aimed to investigate the effect of chemical-free two-stage hydrothermal and mechanical refining pretreatment on improving the sugar yields during enzymatic hydrolysis of forest residue biomass (FRB) and optimize the pretreatment conditions. Hot-water pretreatment experiments were performed using a central composite design for three variables: temperature (160–200 °C), time (10–20 min), and solid loading (10–20%). Hydrothermally pretreated biomass was subsequently pretreated using three cycles of disk refining. The combined pretreatment was found to be highly effective in enhancing sugar yields during enzymatic hydrolysis, with almost 99% cellulose conversion for biomass pretreated at 213.64 °C, 15 min, and 15% solid loading. However, the xylose concentrations in the hydrolysate were found to be low under these conditions due to sugar degradation. Thus, less severe optimum pretreatment conditions (194.78 °C, 12.90 min, and 13.42% solid loading) were predicted using a second-order polynomial model. The response surface model optimized the hydrothermal pretreatment of FRB and predicted the glucan, xylan, and overall conversions of 94.57%, 79.78%, and 87.84%, respectively, after the enzymatic hydrolysis. The model-predicted biomass conversion values were validated by the experimental results.

Mechanism and Data-Driven Fusion SOC Estimation

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy.

Sctudies on adsorption of Brilliant Green from aqueous solution onto nutraceutical industrial pepper seed spent

The study proposed the removal of Brilliant Green, a cationic dye, by adsorption process from wastewater solution utilizing a low-cost adsorbent such as Nutraceutical Industrial Pepper Seed Spent (NIPSS). The study comprises an investigation of the parametric influence on the adsorption process. The parameters identified are pH, dye concentration, process temperature, quantity of the adsorbent, and particle size. The study of statistics found from experiments was carried out by incorporating Freundlich, Brouers-Sotolongo, Langmuir, Toth, Sips, Jovanovic, and Redlich-Peterson isotherm models. The adsorption kinetics were determined by implementing pseudo-first-order and second-order models, diffusion film models, and Dumwald-Wagner and Weber-Morris models. The experimental adsorption capacity qe was found to be about 130 mg/g. This value was closest to the maximum adsorption of 144.6mg/g predicted by the Brouers-Sotolongo isotherm which had a correlation coefficient (R2) of 0.998. The adsorption kinetics data was confirmed to be a pseudo-second-order model. The change in free energy, enthalpy change, and entropy change were vital thermodynamic factors in concluding that adsorption is almost spontaneous and endothermic process. Change in enthalpy (ΔH°) reduced value indicates the physical nature of the process. The adsorption of BG dye on the adsorbent surface was authenticated by FTIR spectroscopy and SEM imaging. A Central Composite Design (CCD) Quadratic model under Response Surface Methodology (RSM) was implemented for statistical optimization of adsorption capacity for the five parameters studied, namely, time, temperature, concentration of the dye, weight of the adsorbent, and pH. Software Design Expert 7.0 was used to evaluate 3D contour plots. The process of optimization yielded a value of 350 mg/g. Thus, incrementing the adsorption process by 84.2 %. The study provides insights on various dye and adsorbent interaction possibilities and derives that NIPSS is an efficient adsorbent to extract BG dye from wastewater solutions.

Cationic dye remediation in water treatment with Lizardite–Rice Husk composite: A statistical physics approach

This study presents the development of a novel composite adsorbent, Lizardite-Rice Husk Composite (LRHC), synthesized from lizardite, a mineral derived from chromite ore waste, and rice husk, an agricultural byproduct. The primary objective was to evaluate the efficiency of LRHC in the adsorption of cationic dyes, Malachite Green (MG) and Methylene Blue (MB), from aqueous solutions. Systematic investigations were conducted to assess the influence of adsorbent mass, initial dye concentration, stirring speed, and contact time on adsorption performance. The results revealed that LRHC exhibited optimal adsorption capacities at pH values above 5, with MB and MG adsorption capacities reaching 309.75 mg/g and 51.43 mg/g, respectively. Adsorption equilibrium for MB was achieved within 30 min, indicating rapid dye uptake. Isotherm analysis demonstrated that the Temkin model best described MB adsorption, while the Freundlich model was more suitable for MG, with favorable Langmuir constants. Kinetic studies confirmed that the adsorption followed a second-order model, suggesting chemisorption as the rate-determining step. Intraparticle diffusion and mass transfer were identified as significant contributors to the adsorption process. Thermodynamic studies indicated that MB adsorption was spontaneous (ΔG° = −4.69 kJ/mol for the concentration of 50 mg/L at 298 K) and that MG adsorption was non-spontaneous (ΔG° = 1.52 kJ/mol for the concentration of 50 mg/L at 298 K), with spontaneity increasing at higher initial concentrations. Statistical physics modeling, particularly the monolayer model, provided detailed insights into the adsorption mechanisms and geometry. Furthermore, LRHC demonstrated excellent reusability, maintaining over 80% of its initial adsorption capacity after four regeneration cycles via a heterogeneous Fenton-like reaction. The combination of easy availability, cost-effectiveness, and environmental friendliness makes LRHC a superior adsorbent for the removal of cationic dyes compared to conventional alternatives. This research contributes novel insights into the design and application of composite adsorbents, offering a sustainable solution for water treatment and addressing significant environmental challenges.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

Flagella, palmella and cyst Haematococcus lacustris microalgae cells decorated on graphene oxide and graphene nanoplatelets-activated carbon as novel adsorbents for the removal of lead from water

Industrialization has led to generation of large quantities of waste which constitutes various toxic heavy metals such as lead (Pb). In this work, novel bio-nanostructured graphene-based microalgae nanohybrid adsorbents, using three different cell types of Haematococcus lacustris (i.e., flagella (flg-C), palmella (Pal-C) and cyst (Cyst-C)) to introduce more functional moieties and enhance the surface properties of the nanohybrids. The nanostructured graphene oxide-activated carbon modified with algae cells (GO-AC@algae) and graphene nanoplatelets-activated carbon modified with algae cells (GNPs-AC@algae) nanohybrids were characterized and used for the removal of Pb ions. The GO-AC@algae nanohybrids demonstrated a high lead removal efficiency of over 98.0%, whereas the GNPs-AC@algae nanohybrids achieved more than 85.0%. Among the GO-AC@algae nanohybrids, the nanohybrid with cyst cell (GO-AC@Cyst-C) shown remarkable efficacy as an adsorbent for the removal of Pb2+ ions from aqueous solutions due to its high specific area, abundance of oxygen-nitrogen-based functional moieties, hydrophilicity, and pore structure. Chemisorption was found to be a beneficial process for both GO-AC@algae and GNPs-AC@algae samples, where Pb2+ was adsorbed in a single layer onto the uniform material surface. Among the various adsorbents, GO-AC@Cyst-C achieved the highest monolayer adsorption capacity of 25.58 mg/g according to the Langmuir model, making it the most effective nanoadsorbents. Kinetic studies revealed that the sorption mechanism of GO-AC@algae were better described by the second-order kinetic model. Meanwhile, the first-order kinetic model was found to be suited for GNPs-AC@algae samples. The nanohybrids could be employed as greener adsorbents at industrial scale for wastewater treatment without incurring significant costs.

Optimizing the Extraction of Polyphenols from the Bark of Terminalia arjuna and an In-silico Investigation on its Activity in Colorectal Cancer.

The interconnection between different fields of research has gained interest due to its cutting-edge perspectives in solving scientific problems. Terminalia arjuna is indigenously used in India for curing several diseases, and its pharmacological activities are being revisited in recent drug-repurposing research. Efficient ultrasound-assisted extraction of phytochemicals from the bark of Terminalia arjuna is highlighted in this study. Following the optimization of the extraction process, the crude hydroethanolic extract is subjected to phytochemical profiling and an in-silico investigation of its anti-cancer properties. A three-level four-factor Box-Behnken design is exploited to optimize four operational parameters, namely extraction time, ultrasonic power, ethanol concentration (as the extracting solvent) and solute (in g): solvent (in mL) ratio. At the optimum parametric condition, the crude extract is obtained, and its GC-MS analysis is carried out. An analysis of network pharmacology (by constructing and visualizing biological networks using Cytoscape) combined with molecular docking reveals the potential antineoplastic targets of the crude extract. The ANOVA table exhibits the significance, adequacy and reliability of the proposed second-order polynomial model with the R² value of 0.917 and adjusted R² of 0.865. Experimental results portray the significant antioxidant potential of the prepared extract in its crude form. The GC-MS analysis of the crude extract predicts the extracted phytochemicals, while the constructed biological networks highlight its multi-targeted activity in colorectal cancer. The study identifies three phytochemicals viz. luteolin, β-sitosterol and arjunic acid as potent anti-cancer agents and can be extended with in-vitro and in-vivo experiments to validate the in-silico results, thus establishing lead phytochemicals in multi-targeted colorectal cancer therapies.

One-step green synthesis of melamine-modified cellulose nanofiber composite aerogels for efficient removal of Pb(II) and Cu(II): Experiments and DFT calculations

A composite aerogel (CGMA) with high porosity (98.32 %) and multiple active sites was prepared for the adsorption of Pb(II) and Cu(II) by sol-gel combined with freeze-drying process using melamine and (3-Glycidyloxypropyl)trimethoxysilane as cellulose nanofiber modifying materials. Characterized by SEM-EDS, XPS, FTIR, BET, MIP and TG, CGMA has a hierarchical pore structure and abundant adsorption sites. At pH = 6, the adsorption reached equilibrium within 120 min, following the Pseudo second-order kinetic model and the Langmuir model, with maximum capacities of 268.1 mg/g for Pb(II) and 152.6 mg/g for Cu(II). The process was primarily governed by homogeneous chemisorption. The coexisting ion, organic matter, and water quality experiments confirmed the excellent anti-interference properties of CGMA. Competitive adsorption experiments showed that CGMA has excellent selective adsorption performance for Pb(II). After 5 cycles, Pb(II) and Cu(II) adsorption performance decreased to 79.21 % and 83.40 %, respectively. FTIR, XPS, DFT and RDG analysis showed that amino groups and oxygen-containing groups were the main sites of adsorption. CGMA forms coordination bonds and complexes with Pb(II) and Cu(II) via amine and oxygen groups and adsorbs via electron transfer, hydrogen bonding, and van der Waals forces, with Pb(II) being more selective. CGMA has good prospects for application in heavy metal ion treatment.

Preparation of novel semi-covalent molecularly imprinted polymer for highly improved adsorption performance of tetracycline in aqueous medium

In the present study, a novel tetracycline-imprinted polymer for tetracycline (TC) adsorption in an aqueous medium was synthesized using a semi-covalent imprinting method for the first time. In this approach, the template-monomer complex was synthesized by the reaction of 3-isopropenyl-α,α-dimethylbenzyl isocyanate (IPDMBI) as the functional monomer and TC as a template, forming a thermally reversible urethane bond (covalent bond). The polymer was characterized by Fourier transform infrared (FT-IR) spectroscopy, thermogravimetric analysis (TGA), nitrogen adsorption/desorption measurements, and determination of point zero charge (PCZ). The equilibrium time of TC onto MIP was reached in 15 min, which was described by the non-linear pseud second-order kinetic model. Adsorption isotherm was described as a dual-site Langmuir-Freundlich model and the maximum adsorption capacity, determined at pH 5.0, was found to be 76.74 mg g−1 for MIP, exhibiting higher adsorption capacity when compared with other adsorbent materials previously reported in the literature for TC. Based on the increment relative selectivity coefficient the MIP showed higher selectivity towards TC and some structurally similar compounds belonging to the tetracyclines family (oxytetracycline and chlorotetracycline), when compared with NIP (non-imprinted polymer). The obtained outcomes in terms of selectivity, maximum adsorption capacity, and fast kinetic make this imprinted polymer an outstanding material for future application in molecularly imprinted solid phase extraction for analytical purposes.