As the planet faces the challenge of global warming, every individual and organization must adopt green practices to protect nature. The automobile industry is one of the primary industries which can contribute significantly towards sustainability. This study aims to examine the impact of green behavior and green perceived benefits on the green buying behaviors of automobiles. The research also explores the moderating influence of environmental awareness on the mechanism. The research is based on a quantitative method for which primary data was gathered from 406 respondents across Pakistan, China and Saudi Arabia via Quota-based purposive sampling. The gathered data was analyzed via SmartPLS. The results show that green behavior and perceived benefits positively and significantly influence green buying behavior. The findings also show the moderating role of environmental awareness on green behavior towards green buying and show no impact on the perceived benefits towards buying behavior. The study has practical and theoretical implications for managers, researchers, policymakers and institutions in the context of green automobile development and businesses. The study also contributes to the attainment of sustainable development goals.
Carbon-based perovskite solar cells (PSCs) have emerged as a hopeful alternative in the realm of photovoltaics. They are considered promising due to their affordability, remarkable durability in humid environments, and impressive electrical conductivity. One approach to address the cost issue is to use affordable counter electrodes in PSCs that do not require organic hole transport materials (HTMs). This study utilized an innovative and economical method to create NiOx/PANI nanocomposites. Later, these nanoparticles were integrated into a carbon paste to act as an HTM. This incorporation is intended to optimize charge extraction, improve interfacial contact, align energy levels, reduce energy loss, minimize charge recombination, and protect the perovskite (FAPbI3) surface from degradation. The optoelectronic properties of these devices were investigated, and all cells showed improved efficiency compared to control cells. The NiOx/PANI doped carbon (NiOx/PANI+CE) exhibited excellent performance due to strong hole conductivity, well-aligned energy levels, and the formation of stepwise band alignment at the perovskite interface.
Climate change due to increased greenhouse gas emissions (GHG) in the atmosphere has been consistently observed since the mid-20th century. The profound influence of global climate change on greenhouse gas (GHG) emissions, encompassing carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), has established a vital feedback loop that contributes to further climate change. This intricate relationship necessitates a comprehensive understanding of the underlying feedback mechanisms. By examining the interactions between global climate change, soil, and GHG emissions, we can elucidate the complexities of CO2, CH4, and N2O dynamics and their implications. In this study, we evaluate the global climate change relationship with GHG globally in 246 countries. We find a robust positive association between climate and GHG emissions. By 2100, GHG emissions will increase in all G7 countries and China while decreasing in the United Kingdom based on current economic growth policies, resulting in a net global increase, suggesting that climate-driven increase in GHG and climate variations impact crop production loss due to soil impacts and not provide climate adaptation. The study highlights the diverse strategies employed by G7 countries in reducing GHG emissions, with France leveraging nuclear power, Germany focusing on renewables, and Italy targeting its industrial and transportation sectors. The UK and Japan are making significant progress in emission reduction through renewable energy, while the US and Canada face challenges due to their industrial activities and reliance on fossil fuels.
Greenhouse Gases , Greenhouse Gases/analysis , Carbon Dioxide/analysis , Agriculture , Soil , Crop Production , Methane/analysis , Nitrous Oxide , Greenhouse Effect
This research explores the 3-D flow characteristics, entropy generation and heat transmission behavior of nanofluids consisting of copper and titanium in water as they flow across a bidirectional apparent, while considering the influence of magneto-hydrodynamics. The thermophysical properties of nanofluids are taken advantage of utilizing the Tiwari and Das demonstrate. The concept of the boundary layer has facilitated the comprehension of the physical ideas derived from it. By applying requisite transformations, the connected intricate sets of partial differential equation have been converted into ordinary differential equation. The modified model is calculated employing the widely recognized technique known as OHAM by using Mathematica program BVPh2.0 Software. For different dimensionless parameters computational and graphical investigations have been performed. It is notice that as fluid parameters change, they exhibit distinct responses in comparison to the temperature, velocity profiles and entropy generation. The results show that velocity profile rise with greater estimates of the magnetic parameter and the rate of entropy formation. Furthermore, thermal profiles become less significant as Eckert and Prandtl numbers increase.
This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm's efficiency. The architecture, called the Multi-Agent Exploration-Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm's average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.
Artificial Intelligence (AI) has become essential to Electronic-Commerce technology over the past decades. Its fast growth has changed the way consumers do online shopping. Using the Technology Acceptance Model (TAM) as a theoretical framework, this research examines how AI can be made more effective and profitable in e-commerce and how entrepreneurs can make AI technology to assist in achieving their business goals. In this regard, an online survey was conducted from the online purchasers of e-commerce firms. The Partial Least Square (PLS) Smart was used to examine the data. The broadly used TAM was identified as an appropriate hypothetical model for studying the acceptance of AI technology in e-commerce. The findings of this study show that Subjective Norms positively impact Perceived Usefulness (PU) and Pursued Ease of Use (PEU), trust has a positive effect on PEU, and PEU positively impacts PU and attitudes toward use. Similarly, PU also has a positive effect on attitudes toward use and intention to use. Furthermore, the findings do not support the impact of Trust on PU and attitudes towards behavioural intention to use. Lastly, behavioural intention to use positively impacted the actual use of AI technology. This study adds theoretical and practical knowledge for adopting the TAM model in the E-commerce sector. It helps entrepreneurs to implement the TAM model in their business to use AI in a better and more appropriate way.
In this study, we evaluated the morphological behavior of polyurethane elastomers (PUEs) by modifying the soft segment chain length. This was achieved by increasing the soft segment molecular weight (Mn = 400-4000 gmol-1). In this regard, polycaprolactone diol (PCL) was selected as the soft segment, and 4,4'-cyclohexamethylene diisocyanate (H12MDI) and 1,6-hexanediol (HDO) were chosen as the hard segments. The films were prepared by curing polymer on Teflon surfaces. Fourier transform infrared spectroscopy (FTIR) was utilized for functional group identification in the prepared elastomers. FTIR peaks indicated the disappearance of -NCO and -OH groups and the formation of urethane (NHCOO) groups. The morphological behavior of the synthesized polymer samples was also elucidated using scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques. The AFM and SEM results indicated that the extent of microphase separation was enhanced by an increase in the molecular weight of PCL. The phase separation and degree of crystallinity of the soft and hard segments were described using X-ray diffraction (XRD). It was observed that the degree of crystallinity of the synthesized polymers increased with an increase in the soft segment's chain length. To evaluate hydrophilicity/hydrophobicity, the contact angle was measured. A gradual increase in the contact angle with distilled water and diiodomethane (38.6°-54.9°) test liquids was observed. Moreover, the decrease in surface energy (46.95-24.45 mN/m) was also found to be inconsistent by increasing the molecular weight of polyols.
Due to their inexpensive and eco-friendly nature, and existence of manganese in various oxidation states and their natural abundance have attained significant attention for the formation of Mn3O4 nanoparticles (Mn3O4 NPs). Herein, we report the preparation of Mn3O4 nanoparticles using manganese nitrate as a precursor material by utilization of a precipitation technique. The as-prepared Mn3O4 nanoparticles (Mn3O4 NPs) were characterized by using X-ray powder diffraction (XRD), UV-Visible spectroscopy (UV-Vis), High-Resolution Transmission electron microscopy (HRTEM), Field emission scanning electron microscopy (FESEM), Thermal gravimetric analysis (TGA) and Fourier-transform infrared spectroscopy (FT-IR). The antimicrobial properties of the as-synthesized Mn3O4 nanoparticles were investigated against numerous bacterial and fungal strains including S. aureus, E. coli, B. subtilis, P. aeruginosa, A. flavus and C. albicans. The Mn3O4 NPs inhibited the growth of S. aureus with a minimum inhibitory concentration (MIC) of 40 µg/ml and C. albicans with a MIC of 15 µg/ml. Furthermore, the Mn3O4 NPs anti-cancer activity was examined using MTT essay against A549 lung and MCF-7 breast cancer cell lines. The Mn3O4 NPs revealed significant activity against the examined cancer cell lines A549 and MCF-7. The IC50 values of Mn3O4 NPs with A549 cell line was found at concentration of 98 µg/mL and MCF-7 cell line was found at concentration of 25 µg/mL.
Exfoliated kaolinite sheets/cellulose fibres nanocomposite (EXK/CF) was synthesised as a novel hybrid of materials of enhanced surface area and adsorption capacities for inorganic-selenate [Se(VI)] and selenite [Se(IV)]-and organic selenium pollutants-selenomethionine (SeMt). The adsorption reactions of the addressed selenium forms followed pseudo-first-order as a kinetic model and Langmuir as an isotherm model. The fitting results and the calculated Gaussian energies-Se (VI) at 2.0 KJ/mol, Se (IV) at 2.2 KJ/mol, and SeMt at 1.7 KJ/mol-suggested physisorption uptake in a monolayer and homogeneous form. The theoretical maximum selenium uptake capacity (qmax) for Se (VI), Se (IV), and SeMt was 137.5 mg/g, 161.4 mg/g, and 95.4 mg/g, respectively. The thermodynamic investigation verified spontaneous and exothermic properties of the selenium uptake reactions by the EXK/CF composite.
A bentonite/Zeolite-P (BE/ZP) composite was synthesized by controlled alkaline hydrothermal treatment of bentonite at 150 °C for 4 h for effective sequestration of phosphate and ammonium pollutants. The composite is of 512 m2/g surface area, 387 meq/100 g ion-exchange capacity, and 5.8 nm average pore diameter. The experimental investigation reflected the strong effect of the pH value in directing the uptake behavior and the best results were attained at pH 6. The kinetic properties showed an excellent agreement for phosphate and ammonium adsorption results with the pseudo-second-order model showing equilibrium intervals of 600 and 360 min, respectively, and maximum experimental capacities of 170 and 155 mg/g, respectively. Additionally, their equilibrium modeling confirmed excellent fitness with the Langmuir hypothesis, signifying homogeneous and monolayer uptake processes with a theoretical q max of 179.4 and 166 mg/g for phosphate and ammonium, respectively. Moreover, the calculated Gaussian adsorption energies of phosphate (0.8 kJ/mol) and ammonium (0.72 kJ/mol) suggested physisorption for them with mechanisms close to the zeolitic ion-exchange process or the coulumbic attractive forces. This was supported by the assessed thermodynamic parameters which also suggested spontaneous uptake by endothermic reaction for phosphate and exothermic reaction for ammonium. The BE/ZP composite is of excellent reusability and used for eight recyclability runs achieving removal percentages of 61.5 and 74.5% for phosphate and ammonium, respectively, in run 8. Finally, the composite was applied in the purification of sewage water and groundwater, achieving complete removal for phosphate from sewage water and ammonium from groundwater and reduction of the ammonium ions in the sewage water to 2.3 mg/L.
