Synthesis and characterizations of super adsorbent hydrogel based on biopolymer, Guar Gum-grafted-Poly (hydroxyethyl methacrylate) (Gg-g-Poly (HEMA)) for the removal of Bismarck brown Y dye from aqueous solution

Chemical modification of guar gum was done by graft copolymerization of monomer hydroxyethyl methacrylate (HEMA) using azobisisobutyronitrile (AIBN) as initiator. Optimal reaction parameters were settled by varying one reaction condition and keeping the other constant. The optimum reaction conditions worked out were solvent system: binary, [H2O] = 15.00 mL, [acetone] = 5.00 mL, [HEMA] = 82.217× 10-2 mol/L, [AIBN] = 3.333 × 10-2 mol/L, reaction time = 3 h, reaction temperature = 60 °C on to 1.00 g guar gum with Pg = 1694.6 and %GE = 68,704.152. Pure guar gum polymer and grafts were analyzed by several physicochemical investigation techniques like FTIR, SEM, XRD, EDX, and swelling studies. Percent swelling of the guar gum polymer and grafts was investigated at pH 2.2, 7.0, 7.4 and 9.4 concerning time. The finest yield of Ps was recorded at pH 9.4 with time 24 h for graft copolymer. Guar gum and grafted samples were explored for the sorption of toxic dye Bismarck brown Y from the aqueous solution with respect to variable contact time, pH, temperature and dye concentration so as to investigate the stimuli responsive sorption behaviour. Graft copolymers showed better results than guar gum with percent dye uptake (Du) of 97.588 % in 24 h contact time, 35 °C temperature, 9.4 pH at 150.00 ppm dye feed concentration as compared to Guar gum which only showed 85.260 % dye uptake at alike dye fed concentration. The kinetic behaviour of the polymeric samples was evaluated by applying many adsorption isotherms and kinetic models. The value of 1/n was between 0 → 1 showing that there was physisorption of the BB dye that took place on the surface of the polymers. Thermodynamics of BB Y adsorption onto hydrogels was investigated concerning the Van't Hoff equation. -∆G° values obtained from the curve proved the spontanity of the process. Within the context of adsorption efficiency, an investigation was conducted to examine the process of sorption of Bismarck brown Y dye from aqueous solutions. The graft copolymers demonstrated remarkable adsorption abilities, achieving a dye uptake (Du) of 97.588 % over a 24-h period at a temperature of 35 °C, pH level of 9.4, and a dye concentration of 150.00 ppm. The raised adsorption capacity was additionally corroborated by the application of several adsorption isotherms and kinetic models, which indicated that physisorption is the prevailing process/mechanism. Additionally, the thermodynamic research, utilising the Van't Hoff equation, validated the spontaneity of the adsorption phenomenon, as evidenced by the presence of a negative ∆G° values. The thermodynamic analysis revealed herein establishes a strong scientific foundation for the effectiveness of adsorbent composed of graft copolymers based on guar gum. The research conclude the efficiency of the guar gum based grafted copolymers for the water remediation as efficient adsorbents. The captured dye can be re-utilised and the hydrogels can be used for the same purpose in number of cycles.

Coconut husk-derived nanocellulose as reinforcing additives in thermal-responsive hydrogels

Nanocellulose has been widely used as a reinforcing agent for hydrogel systems, but its functions on thermal responsive hydrogels are rarely investigated. In this study, we extracted cellulose nanofibers (CNFs) from coconut biomass (coir fibers and piths, respectively) and aimed to study their effects on the material properties on a new class of thermogel (poly(PCL/PEG/PPG urethane). The CNFs extracted from fiber (FF) and piths (FP) showed different morphology and fiber lengths. FF are uniformed individual fibrous networks with a fiber length of 664 ± 416 nm, while FP display a hybrid structure consisting of individual fiber and large bundles with a relative shorter fiber length of 443 ± 184 nm. Integrating both CNFs into thermogels remained the thermal-responsive characteristics with an enhanced rheological property. The results showed that gels with FF resulted in a higher storage modulus and lower Tan δ value compared to those with FP, indicating that the CNFs with a longer length could form a more intertwined network interacting with the thermogel matrix. Furthermore, we demonstrated the improved capabilities of the nanocomposite thermogels for sustained drug delivery in vitro. This study not only value-adds lignocellulose valorization but also elevates the versatility of thermogels.

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization-least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

Mixed convection and activation energy impacts on MHD bioconvective flow of nanofluid with irreversibility assessment

In this communication irreversibility minimization in bio convective Walter's-B nanofluid flow by stretching sheet is studied. Suspended nanoparticles in Walter's-B fluid are stabilized by utilizing microorganisms. Total irreversibility is obtained via thermodynamics second law. The influences of applied magnetic field, radiation, Joule heating and activation energy are accounted in momentum, temperature and concentration equations. Furthermore thermophoresis and Brownian movement impacts are also accounted in concentration and temperature expressions. The flow governing dimensional equations are altered into dimensionless ones adopting transformation procedure. Homotopy Analysis Method (HAM) code in Mathematica is implemented to get the convergent series solution. The influences of important flow variables on temperature, velocity, motile density, irreversibility, mass concentration, Bejan number and physical quantities are analyzed graphically. The obtained results revel that the velocity profile decreases for escalating magnetic parameter and Forchheimer number. Entropy generation is increased for higher Brinkman variable while Bejan number declines versus Brinkman variable. The important observations are given at the end.

The digital economy and the green and high-quality development of the industry-a study on the mechanism of action and regional heterogeneity

Consistently rising carbon emissions in the global economy make it more challenging to meet the goals of the Paris climate agreement. Knowing what factors play a role is essential to help shape strategies to reduce carbon emissions. While there is a wealth of material on how GDP expansion correlates with increases in carbon emissions, little is known about how democracy and renewable energy could improve environmental conditions in developing nations. The purpose of this article was to use fair data to assess the effect of renewable energy and green technology advances on carbon neutrality in 23 provinces across China from 2005 to 2020. The research used the dynamic ordinary least square, the fully modified ordinary least square, and the two-step GMM to determine that digitalization, industrial development, and health expenditures result in lower carbon emissions. Urbanization, tourism, and per capita income in certain Chinese provinces also drove carbon emissions. The study also showed that the impact of these factors on carbon emissions varies depending on the amount of economic growth. Environmental pollution is reduced due to the digitalization of tourist and healthcare costs, industrial development, and urbanization. According to the study's findings, we advise these nations to seek economic growth and invest in health care and renewable energy initiatives.

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

Strategy of Integrating Ultraviolet Absorption and Antimicrobial Activity in a Single Molecule: DFT Calculation and Experiment

In the present study, (3,5-benzamide-2,4-dihydroxyphenyl)(phenyl) methanone (UV-CB) was synthesized and investigated as an ultraviolet (UV) absorber and a bacteriostatic agent. The optimized geometry, energy levels, charges, and UV electronic absorption bands of UV-CB in the singlet were calculated by density functional theory (DFT) calculations. The quantum chemical method was used to investigate the geometry and natural bond orbital (NBO) parameters. And the computational studies indicated that the intramolecular hydrogen bond (IMHB) was formed between the 2,4-dihydroxybenzophenone (UV-C) group and the N-(hydroxymethyl)benzamide (NBA) group, which was beneficial to the stability after the combination. The results of the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) tests illustrated that UV-CB is a promising antibacterial agent. The successful synthesis of UV-CB with anti-UV performance and antibacterial ability evidences that DFT calculation is an available approach to design and analyze novel compounds.