Blending Technique Research Articles

In Vitro Assessment of Chitosan-PEG Hydrogels Enriched with MSCs-Exosomes for Enhancing Wound Healing.

Regenerating skin tissue remains a major challenge in medical science, especially due to the risk of scarring and prolonged healing, which becomes even more complicated in people with diabetes. Recent advancements have led to the creation of therapeutic dressings incorporating drug-delivery systems to tackle these issues. Exosomes (Exos) derived from mesenchymal stem cells (MSCs) have gained significant attention for mediating therapy without directly using cells, thanks to their natural anti-inflammatory and tissue repair properties mirroring those of MSCs. In this study, an advanced wound dressing combines chitosan (CS) and polyethylene glycol (PEG) hydrogel with adipose MSCs-derived Exos (ADMSCs-Exos). This composite, formed using a straightforward blending technique, is engineered to improve the healing process of severe skin injuries by steadily releasing Exos as the hydrogel degrades. The in vitro studies demonstrate that this hydrogel-exosome dressing greatly enhances endothelial cell migration, reduces oxidative stress, and promotes angiogenesis, crucial for effective wound healing. Additionally, real time-polymerase chain reaction (RT-PCR) analysis revealed significant upregulation of key genes involved in these processes, supporting the therapeutic potential of the hydrogel-Exo combination. These findings emphasize the potential of this hydrogel-Exos combination as an innovative and promising solution for advanced wound care.

Improvement in Compatibility and Drug Release Performance of Hot-Melt Pressure-Sensitive Adhesives by Physical Blending Technique.

Hot-melt Pressure-sensitive Adhesives (HMPSA) are eco-friendly pressuresensitive adhesives, with the potential of being used as substrates for transdermal patches. However, due to the low hydrophilicity of HMPSA, the application is limited in the field of Traditional Chinese Medicine (TCM) plasters. Three modified HMPSA were prepared with acrylic resin EPO, acrylic resin RL100, and Polyvinylpyrrolidone (PVP) as the modifying materials. The physical compatibility between HMPSA and the modifying materials was investigated through in vitro release performance, viscosity, softening point, cohesion, and fluidity, so as to determine the most effective modifying material. The impact of the modified HMPSA on the release properties of different TCM ingredients was elucidated by the performance of water absorption and contact angle behavior. With the addition of the modifying materials, both the viscosity and the softening point of HMPSA were improved, with the flowability reduced and the cohesion maintained. The morphological and structural changes reflected the physical compatibility between HMPSA and the three modifying materials. According to the results of in vitro release experiments, PVP effectively improved the release performance of paeoniflorin, ephedrine hydrochloride, and cinnamaldehyde in HMPSA, with no significant impact on the release performance of eugenol. The changes in the drug release performance of HMPSA may be attributed to the improved hydrophilicity of HMPSA after physical modification. The compatibility and the drug release performance of HMPSA were effectively enhanced after the addition of the modifying materials by the physical blending technique. Among the three modifying materials, PVP has been found to be an ideal modifying material for HMPSA in the field of TCM plasters due to its effects on drug release performance.

Hierarchical curing mechanism in epoxy/bismaleimide composites: Enhancing mechanical properties without compromising thermal stabilities

It remains a considerable challenge to enhance mechanical properties without sacrifice of thermal properties in epoxy-based materials. In this study, epoxy/bismaleimide (EP/BMI) composites were synthesized via a melting blend technique using 4,4′-diaminodiphenylsulfone as the curing agent. It is found that there is a unique hierarchical curing mechanism in EP/BMI systems, including (i) respective curing of EP and BMI monomer, (ii) copolymerization between EP prepolymer and BMI monomer, (iii) homopolymerization of EP prepolymer and BMI oligomer. Via the hierarchical copolymerization, EP and BMI are compatible without phase separation phenomenon. The copolymerization concurrently improved the composites’ resistance to deformation under stress and restricted the thermal mobility of the polymer chains upon heat. Compared to pure EP resin, EP/BMI composite (4:1) shows a flexural strength of 133.6 MPa, tensile strength of 106.4 MPa and toughness of 3.6 MJ/m3 respectively, increased by 20.7 %, 36.4 % and 45.0 %. Meanwhile, Tg and Yc were also improved by 10.3 ℃ and 12.3 % in EP/BMI composite. Furthermore, the mechanical and thermal properties of the EP/BMI composites can be efficiently tailored by curing conditions and EP:BMI molar ratios. This discovery provides a significant benchmark for the design of composite material and to expand the application horizons for both EP and BMI materials.

Reinforcement of various characteristics of biocompatible PVA/PMMA/N-GO polymer nanocomposite by functionalization of GO

ABSTRACT This article explores the reinforcement of various characteristics of a biocompatible PVA/PMMA polymer blend through the incorporation of functionalized graphene oxide (N-GO). PVA/PMMA/N-GO nanocomposites were synthesized using a melt blending technique with varying N-GO wt.%. Characterization revealed significant improvements: reduced refractive index and optical bandgap, increased extinction coefficient, Urbach energy, and optical dielectric constants. XRD analysis showed enhanced crystallinity and reduced micro-strain, indicating suitability for high-performance applications. FTIR analysis detected minor shifts in functional groups (O-H, C-H, C-O, and C-C), while PL properties were modulated. In addition, the antibacterial activities of the prepared polymer nanocomposites have been investigated against Escherichia coli (gram-negative bacteria) and Bacillus subtilis (gram-positive bacterial). These results demonstrate the potential of PVA/PMMA/N-GO composites for diverse technological applications.

Phase Change Materials for Energy Storage Applications from PEG and PBAT with AlN and CNT as Fillers.

This study investigates the fabrication of phase change material-poly(butylene adipate-co-terephthalate) (PCM-PBAT) composites through melt blending techniques, focusing on the impact of isophorone diisocyanate (IPDI) treatment on carbon nanotubes (CNTs) and (3-aminopropyl)triethoxysilane (APTES) treatment on aluminum nitride (AlN) particles. Analysis of mechanical properties highlights an enhancement in tensile strength with APTES-treated AlN particles, while dynamic mechanical analysis (DMA) reveals an increase in stiffness. Laser flash analysis (LFA) investigation demonstrates a significant increase, up to 325%, in thermal conductivity compared to PCM-PBAT composites without filler. Moreover, the fabricated composites exhibit enhanced latent heat storage capabilities, with the PCM-PBAT composite containing 30% filler (20PCM-PBAT-30F) showing promising latent heat values. These findings underscore the efficacy of selecting an appropriate PCM-PBAT ratio and surface functionalization of the filler particles in enhancing the latent heat storage and thermal and mechanical properties of the polymer composites, suggesting their potential application across diverse fields.

Development of epoxy/flyash composites containing Halloysite nanotubes: Mechanical, morphological, and thermal degradation kinetics

This research article investigates the development of a composite material by the inclusion of halloysite nanotubes (HNTs) into an epoxy/flyash matrix using a solution blending technique. The HNTs were added at different loadings and were effectively dispersed throughout the epoxy/flyash matrix. The thermal properties and degradation kinetics of the epoxy/flyash composites were studied with a thermogravimetric analyzer (TGA) at different heating speeds (10, 15, 20, and 25 °C/min). TGA measurements showed a considerable improvement in the thermal stability of the composites. Incorporating 3phr of HNTs into epoxy/flyash composites increased thermal stability by 13°C, 16°C, and 18°C, with weight losses of 5%, 10%, and 50%, respectively, as compared to composites without HNTs. The thermal degradation activation energy (E) was calculated using the model-free Kissinger Akahir-Sunose (KAS) and Flynn-Wall-Ozawa (FWO), and it was revealed that epoxy/flyash composites containing 3phr HNTs have the highest E, 77.52 and 79.89 kJ/mol, respectively. The SEM micrographs of 3phr HNTs show uniform dispersion of HNTs onto epoxy/flyash composites, resulting in a tensile strength increase from 37.2 MPa to 43.6 MPa and modulus increase from 1238.4 MPa to 1463.6 MPa. The obtained results show that the addition of flyash and nanofiller (HNTs) to the epoxy matrix greatly increases the mechanical and thermal properties.

Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification

The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The framework employs a predetermined meta-model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.

Enhanced long-term stability of zinc-air batteries using a quaternized PVA-chitosan composite separator with thin-layered MoS2

Due to their high energy density and cost-effectiveness, rechargeable zinc-air batteries (ZABs) are increasingly recognized for their potential as long-duration energy storage solutions. A crucial component for maximizing their efficiency is the membrane separator, which must exhibit high hydroxide-ion conductivity and long-term stability. This study introduces an innovative hybrid composite separator, created by embedding thin-layered molybdenum disulfide (MoS2), known for its intrinsic negative charge, into a polycationic quaternized PVA-chitosan (CS) matrix. Polyvinyl alcohol (PVA) is functionalized with quaternary ammonium (QA) groups before being combined with CS and MoS2, using a solvent blending technique. The separator's three-dimensional structure and morphology is analyzed via synchrotron radiation X-ray tomographic microscopy (SR-XTM). Results demonstrate that the ZAB equipped with a quaternized PVA/CS/0.5 wt.% MoS2 composite separator achieved a high conductivity of 87.3 mS cm-1 and exceptional stability, enduring over 465 cycles. This performance is attributed to the synergistic interaction between the quaternized PVA-CS matrix and the MoS2 surface, forming robust polymer complexes through electrostatic interactions. These findings suggest that the developed separators hold significant promise for advanced ZAB applications.

Blended Learning in a Language Classroom: Implication for Pedagogy

This paper examined Blended Learning (BL) and its implication to Language learning. It reviews relevant literature over the last two decades on blended learning generally, and language instruction using blended learning techniques in particular. It discusses the concept of blended learning and its features. The paper further explores the discussion of the positive benefits from applying blended learning in language instruction. The paper also highlights some disadvantages of blended learning course for proper understanding and the practice and implementation of this method. An important part of this paper also explores the implications for pedagogy. The major conclusions of the study are that blending learning enhances the learner's experience of a new language, and offers greater efficiency in the communication and practice of a particular language. Efficient and user-friendly technology, with direction instruction and practice in a face-to-face setting are seen as the key to successful blended learning language instruction. The paper then recommends that teachers should be adequately trained in the art of BL because they are still greatly needed by students for their timely and adequate guidance, direction, feedback and comments while students are learning in blended learning environment.

Evaluating physico-mechanical properties of NaOH-treated natural fibres: Effects of polyolefin

The bottle gourd plant fibres (BGPF) and okra fibres were processed and refined (w/w 6 % NaOH) before being incorporated with polyolefin (polypropylene) for composite fabrication using a blending technique. The polyolefin matrix is used to develop composites with 5 % okra fibres and varying percentages (25, 30, 35 and 40 %) of BGPF. The results indicated that the "35 % BGPF +5 % okra +60 % polypropylene" composition achieved remarkable mechanical properties with tensile strength (26.95 MPa), tensile modulus (3.16 GPa), decreased elongation at break (1.71 %), bending strength (52.53 MPa), bending modulus (3.45 GPa), impact strength (14.25 kJ/m2), and hardness (69 D-shore). Moreover, these composites absorbed minimal water when a specific portion was submerged. The scanning electron microscopy (SEM) test on the composites revealed good fibre adhesion and adherence content between BGPF and okra fibres. Additionally, the thermo-gravimetric analysis and differential scanning calorimetry exhibited weight loss of composites during maximum cellulose degradation (80 %) at 483 °C. In the composites of the fibre-PP matrix, two distinct physical changes were observed indicating glass transition and degradation. However, the mechanical properties decreased after soil degradation. Thus, remarkable properties were achieved for the fibres-fibres and fibres-polyolefin (polypropylene) interfacial interactions.

Role of Mo and Zr Additions in Enhancing the Behavior of New Ti–Mo Alloys for Implant Materials

AbstractThe utilization of Ti–Mo alloys in biomedical applications has gained attention for use in biomedical applications owing to their non-toxicity, reasonable cost, and favorable properties. In the present study, Ti–12Mo–6Zr and Ti–15Mo–6Zr alloys were prepared using elemental blend and mechanical alloying techniques. The effect of alloying elements Mo and Zr of Ti–Mo alloy, as well as the effect of fabrication techniques of Ti–Mo–Zr trinary alloys, were investigated. Thermodynamic calculations supported by CALPHAD analysis revealed that the addition of Zr increases lattice distortion, which contributes to enhancing the strength. Conversely, adding Mo decreases the enthalpy, facilitating improved mixing and solid solution formation. The as-sintered samples were characterized by X-ray diffraction, optical microscope, and scanning electron microscopy, and their microhardness, compressive, and corrosion behavior were investigated. Among all the investigated alloys, Ti–15Mo–6Zr alloy prepared by the mechanical alloying technique, milled for six hours at 300 rpm, compacted at 600 MPa, and sintered at 1250 ℃, shows good comprehensive mechanical properties with a preferable compressive strength (− 1710 MPa) and hardness (396 HV5), as well as the lowest wear rate (0.69%) and corrosion rate (0.557 × 10–3 mm/year). This can be related to the solid solution strengthening and relative density, together with dispersion and precipitation strengthening of the α phase. Remarkably, the combination of high mechanical and corrosion properties can be achieved by tailoring the content of the α phase, controlling the density, and providing new fabricating techniques for β Ti alloys. Graphical Abstract

Highly Filled Waste Polyester Fiber/Low-Density Polyethylene Composites with a Better Fiber Length Retention Fabricated by a Two-Rotor Continuous Mixer

A key challenge in the utilization of waste polyester fibers (PET fibers) is the development of fiber-reinforced composites with high filler content and the improvement of fiber length retention. Herein, the effects of a two-rotor continuous mixer and a twin-screw extruder on the structure and properties of waste polyester fiber composites were evaluated. The results revealed that the mechanical properties of the composites were improved significantly with increasing fiber content, especially when processed using the twin-rotor continuous mixer. This mixer facilitated the formation of a robust fiber network structure, leading to substantial enhancements in tensile strength, flexural strength, and heat resistance. Specifically, compared to those processed by the twin-screw extruder, with 60 wt% fibers content, the tensile and flexural strengths of specimens processed by the twin-rotor continuous mixer increase by 21% and 13%, respectively. The average fiber length in specimens processed by the twin-rotor continuous mixer was 32% longer than that in specimens processed by the twin-screw extruder, attributable to the lower shear frequency and the higher tensile ratio of the former. This blending technique emerges as an effective strategy, contributing significantly to promoting the development and practical application of waste textile fiber-reinforced polymer composites.

Enhancing thermo-physical properties of hybrid nanoparticle-infused medium temperature organic phase change materials using graphene nanoplatelets and multiwall carbon nanotubes

Phase change materials (PCMs) have emerged as an intriguing option for the storage of thermal energy because of their remarkable capacity to store latent heat. However, the practical application of these materials is hindered by their low thermal conductivity and limited photo-absorbance. For this investigation, graphene nanoplatelets (GNP) and multiwall carbon nanotubes (MWCNT) hybrid nanoparticles were disseminated in RT-54HC organic PCMs at different weight fractions. The nanoparticles were incorporated into the base PCMs using a melt blending technique. Based on the findings, one combination of GNP to MWCNT in a 0.25:0.75 ratio has shown the highest thermal conductivity, with an increase of 40% (0.28 Wm−1 K−1) compared to other hybrid combinations. This breakthrough could potentially open new avenues in the field of thermal energy storage. The chemical stability of the hybrid nanoparticle dispersed composites was assessed through FTIR analysis. In addition, the composites exhibited excellent thermal stability, maintaining their structural integrity even at temperatures as high as 300 ℃. The melting temperature of the composites also showed minimal variation. Based on the evaluation of latent heat enthalpy, the organic PCM known as base RT-54HC demonstrated a heat storage capacity of 230 J/g. However, the composites exhibited a slight decrease in latent heat with increasing nanoparticle weight fraction. In addition, the composite with added hybrid nanoparticles demonstrated an increase in optical absorbance, accompanied by a decrease in transmissibility. Therefore, the hybrid nano-enhanced composites have demonstrated enhanced thermo-physical properties, making them not only suitable but also highly promising for use in applications with mid-range melting temperatures.Graphical

BwMMV-pred: a novel ensemble learning approach using blood smear images for malaria prediction

The use of machine learning in healthcare has become widespread, enhancing the capabilities of doctors and clinicians. This study introduces a novel ensemble learning approach named Blending with Meta Majority Voting (BwMMV) for malaria prediction using blood smear images. The BwMMV technique combines the strengths of eight base classifiers to form an intermediate dataset, which is subsequently used to train five distinct meta-models using different machine learning algorithms. A Local Binary Pattern Histogram (LBPH) method is employed to extract texture features from blood smear images, effectively capturing the underlying patterns necessary for classification. The final classification decision is determined through a majority voting mechanism, selecting the outcome with the most votes as the final prediction. Our results indicate that the BwMMV approach significantly outperforms traditional hard voting and blending techniques, achieving superior accuracy, robustness, and resilience in performance. This innovative method demonstrates promising potential as a powerful tool for automated diagnosis systems, with the ability to be expanded to analyze various datasets efficiently.

Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model

Accurate prediction of PM2.5 concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several challenges owing to the impact of unique meteorological conditions, potential correlation between PM2.5 levels in neighboring ports, and coupling influence of background pollutants in city zones. Therefore, considering the spatiotemporal correlation among the factors influencing PM2.5 concentration variations within the harbor cluster, we developed a novel blending ensemble deep learning model. The proposed model combined the strengths of four deep learning architectures: graph convolutional networks (GCN), long short-term memory networks (LSTM), residual neural networks (ResNet), and convolutional neural networks (CNN). GCN, LSTM, and ResNet served as the base models aimed at capturing the spatial correlation of PM2.5 concentrations in neighboring ports, the potential long-term dependence of meteorological factors and PM2.5 concentrations, and the effects of urban ambient air pollutants, respectively. Following the blending ensemble technique, the prediction outcomes of three base models were used as the input data for the meta-model CNN, which employs the blending ensemble technique to produce the final prediction results. Based on actual data obtained from 18 ports in Nanjing, the proposed model was compared and analyzed for its prediction performance against six state-of-the-art models. The findings revealed that the proposed model provided more accurate predictions. It reduced mean absolute error (MAE) by 10.59 %–20.00 %, reduced root mean square error (RMSE) by 13.22 %–17.11 %, improved coefficient of determination (R2) by 10 %–35.38 %, and improved accuracy (ACC) by 3.48 %–7.08 %. Additionally, the contribution of each component to the prediction performance of the proposed model was measured using a systematic ablation study. The results demonstrated that the GCN model exerted the most substantial influence on the prediction performance of the GCN-LSTM-ResNet model, followed by the LSTM model. The influence of urban background pollutants can significantly enhance the generalizability of the complete model. Moreover, a comparison with three blended ensemble models incorporating any two base models demonstrated that the GCN-LSTM-ResNet model exhibited superior prediction performance and was particularly excellent in predicting the occurrence of high-concentration events. Specifically, the GCN-LSTM-ResNet model improved MAE and RMSE by at least 12.3% and 9.2%, respectively, but reduced R2 and ACC by 26.1% and 6.8%, respectively. The proposed model provided reliable PM2.5 concentration prediction outcomes and decision support for air quality management strategies in dry bulk port clusters.

An Analytical Study of Training Needs of University Teachers in Online Environment in Perspectives of Physical Resources and Pedagogical Skills in the Subject of Social Sciences

The objectives of this research study were about finding out the availability of physical resources, the pedagogical requirements of teachers training for online teaching and examining the ICT skills that the teacher needs to teach in online teaching. The mode of teaching has widely got more importance from regular mode towards online teaching and learning from post COVID time. These days it is very familiar concept. The study aimed to investigate the teachers training need assessment for online teaching at university level. This is the descriptive research study. The population as well as sampling units for this study was 150 male along with female university educators. It was concerned in this study about the needs of training of them for online mode of teaching. Questionnaire was used to collect data from the teachers of social science currently serving in various universities in Islamabad. The data were analysed through SPSS by using mean, frequencies, and standard deviation. The data revealed that Information and Communication Technology (ICT) structure needs to be strengthened for online education environments with the concept of blended education and learning techniques self-paced learning and online interactions as an effective and alternate mode of learning.

A niobium and tantalum co-doped perovskite electrolyte with high ionic conduction for low-temperature Ceramics Fuel cell

In recent studies, fast ionic conduction through surface doping and coating has been a favorite subject and has indicated a promising and stable strategy to optimize ions in the developed electrolytes for low-temperature ceramic fuel cells (LT-CFCs). We have designed co-doped perovskite (Nb/Ta-SrCoO3) to enhance further ionic properties using the Solid-state blending technique. The prepared SCNT (SrCoNb0.3Ta0.3O3) was used as an electrolyte sandwiched between symmetrical electrodes and delivered attractive fuel cell performance (650 mW/cm2) with better stability at the low operating temperature of 520 °C compared to other compositions of SCNT. The low grain boundary resistance manifests SCNT's high ionic conduction + microstructural properties, assisting with higher fuel cell performance. The co-doping enables the fermi-level to move towards the -ive side, establishing a space charge region constituting BIEF (built in electric field) and helping to enhance the ions' transportation through the surface and interface. This work thus points out a new type of electrolyte with a different working mechanism from previous studies. It indicates a feasible approach to developing high-performing and stable electrolytes for LT-CFCs.

Performance of Asphalt Mixtures Modified with Desulfurized Rubber and Rock Asphalt Composites

This study explores the performance of asphalt mixtures modified with North American rock asphalt and desulfurized rubber particles at varying rubber-to-asphalt ratios ranging from 18% to 36% by weight. A comprehensive set of laboratory tests, including high-temperature rutting tests, low-temperature bending tests, indirect tensile tests, and freeze–thaw splitting tests, were conducted to evaluate the modified mixtures. The results indicate that both wet and dry blending methods produce mixtures that meet technical requirements, with the optimal asphalt-to-aggregate ratio determined to be 7.1%. At a rubber-to-asphalt ratio of 18%, the wet blending method slightly improves high-temperature rutting resistance compared to the dry method. However, an increase in rubber content generally enhances rutting resistance regardless of the blending technique. The wet blending method excels in low-temperature crack resistance, possibly due to better rubber dispersion, while an increase in rubber content diminishes crack resistance due to a thinning asphalt film. In terms of fatigue performance, the dry blending method results in significantly longer fatigue life, with a 27% rubber-to-asphalt ratio exhibiting optimal balance. The dry method consistently outperforms the wet method in water stability, and the resistance to water damage increases with rubber content. In conclusion, this study provides valuable insights into optimizing rubber-to-asphalt ratios and blending methods for various application needs, showcasing the benefits of rock asphalt and desulfurized rubber particles in asphalt modification.

Mechanism underlying anion-sieving in poly(ionic liquid)-based membrane: Effective acid recovery from engineering waste

Anion exchange membranes (AEMs) are promising for recovering acid from different engineering effluent due to their lower energy consumption, positive environmental impact, and potential for providing clean water resources. Utilizing the diffusion dialysis (DD) process, AEMs effectively retain metal ions while selectively allowing fast proton permeation. Achieving high hydrophilicity, proton conductivity, and ion exchange capacity through exact control of polymeric structure and chemical composition is crucial for enhancing the efficiency of the acid recovery process. This study presents the findings on the impact of novel Poly(ionic liquid) on cellulose acetate-based AEMs for acid recovery through DD application. In this study, interconnected nanochannel AEMs with high acid permeability are engineered using a straightforward blending technique. Poly(3-butyl-1-vinylimidazolium bromide-co-methyl methacrylate-co-styrene) (poly([BVIM]-[Br]–co–MMA-co-Styrene, PIL) is blended with cellulose acetate to achieve ionic crosslinking, resulting in a mechanically stable membrane. The dosage of PIL within the membrane matrix plays a vital role in determining the prepared membranes' physicochemical properties and ion exchange capabilities, which exhibit excellent thermal stability. Remarkably, the optimal AEM (7.5 % PILM) exhibits a high acid dialysis coefficient (UH+) of 1.05 m/h and a separation factor (S) of 802, outperforming previously reported state-of-the-art AEMs and commercial membranes. These findings indicate that the prepared AEMs are highly effective for acid recovery through DD. Notably, our work stands out by introducing new AEMs with superior acid dialysis performance and selectivity.

Physico-Chemical Analysis of Ayurvedic Formulation -Chaturbhadra Kwatha Churna

In recent times, there has been an increasing need for products derived from plants in the fields of medicine, nutrition, and beauty. This rise demands the creation of dependable methods for ensuring product quality, which involves blending age-old techniques with contemporary analytical tools. It's essential to standardize the production of herbal medicines to guarantee their quality, which requires a thorough assessment of different aspects such as sensory evaluation, chemical composition analysis, and examination of microbial content. In the realm of Ayurveda, where the use of plant extracts is fundamental, establishing standards is key to confirming the product's genuineness and effectiveness. This is demonstrated through the example of Chaturbhadra Kwatha Churna, a complex herbal mixture designed to treat various health issues. By conducting a detailed analysis, this research seeks to lay a solid groundwork for consistent quality and performance, building trust in Ayurvedic therapies globally.