One of the primary greenhouse gases linked to global warming is carbon dioxide. This study investigates the determinants of carbon dioxide emissions in 29 African countries over the period 1990–2023 using a two-stage machine learning and econometric framework. In the first stage, Classification and Regression Trees, Random Forests, and Extreme Gradient Boosting algorithms are employed to identify the most important drivers of carbon dioxide emissions. The results indicate that Random Forests provide the highest predictive performance among the machine learning models. In the second stage, the variables identified by the machine learning analysis are incorporated into second-generation panel estimators that account for cross-sectional dependence and heterogeneity. Specifically, long-run and short-run relationships are examined using the Common Correlated Effects Autoregressive Distributed Lag estimator, while Augmented Mean Group, Common Correlated Effects Mean Group estimators, and Fixed Effects with Driscoll–Kraay standard errors are employed as robustness checks. The findings reveal that oil consumption is positively associated with carbon dioxide emissions, whereas renewable energy consumption is negatively associated with carbon dioxide emissions. The results provide limited but supportive evidence for the Environmental Kuznets Curve hypothesis, with income and income-squared terms exhibiting the expected signs and the estimated turning point falling within the observed income range. Robustness analyses based on oil-exporting and non-oil-exporting country groups, as well as an alternative carbon-intensity measure, confirm the central role of fossil fuel dependence and renewable energy adoption in shaping environmental outcomes across Africa. These findings highlight the importance of accelerating renewable energy deployment, reducing reliance on fossil fuels, improving energy efficiency, and promoting sustainable development strategies to achieve long-term environmental sustainability in African economies.
The valorization of lignocellulosic waste for biomedical applications has attracted considerable interest due to its sustainability and cost-effectiveness. In this study, lignin was extracted from spent coffee grounds using the soda pulping method and employed as a raw material for the preparation of methylene blue (MB)-loaded nanoparticles. The nanoparticles were fabricated via nanoprecipitation which a simple, scalable, and drug delivery-compatible method, using different lignin concentrations and volume ratios to identify the formulation that achieves the smallest particle size with maximum drug loading. Characterization results revealed that the nanoparticles were spherical, monodisperse, with an average size of 160 nm, and formed a stable aqueous suspension due to a zeta potential of-33.5 mV. Drug release studies indicated slower release under acidic conditions compared to neutral pH. Despite limited release, the nanoparticles retained the ability to generate reactive oxygen species (ROS) under light irradiation, as confirmed by the time-dependent decrease in DPBF absorbance. These findings suggest that lignin nanoparticles derived from coffee waste are promising candidates as photosensitizer carriers, and future studies are planned to evaluate their biocompatibility and in vitro therapeutic efficacy.
Background: Despite growing interest in artificial intelligence (AI)-mediated second or foreign language (L2) learning, little is known about the role of AI-driven informal digital learning of English (AI-IDLE) in shaping learners' communicative intention and motivation. Furthermore, how L2 learner traits such as grit may influence the relationship between AI-IDLE and communicative outcomes remains largely underexplored.Purpose: This study examined the association between AI-IDLE and two communicative variables - L2 willingness to communicate (WTC) and speaking motivation - with a particular focus on the mediating role of L2 grit.Methods: Participants were 244 English as a foreign language (EFL) learners (123 males, 121 females). Structural equation modeling was employed to examine the direct and indirect associations among AI-IDLE, L2 grit, WTC, and L2 speaking motivation.Results: Findings revealed that AI-IDLE significantly and directly predicted grit and WTC but not speaking motivation. L2 grit emerged as a strong predictor of both WTC and speaking motivation, and functioned as a full mediator in the relationships between AI-IDLE and the two communicative outcomes.Conclusion: These results highlight L2 grit as a key factor linking AI-driven informal learning with learners' communicative readiness and motivation, suggesting that fostering grit may maximize the benefits of AI tools for L2 learning.
LuFeO3 (LFO) is a perovskite oxide with promise for optical and electroceramic applications. In the present study, LFO and Co-substituted compositions (LFO, LuFe0.95Co0.05O3, and LuFe0.90Co0.10O3) were synthesized by a conventional solid-state route and characterized by SEM, Raman spectroscopy, diffuse reflectance, and broadband dielectric/impedance measurements. Co substitution alters the powder microstructure, yielding more irregular agglomerates composed of finer sub-units than undoped LFO. Dielectric spectra showed that the loss tangent (tanδ), the dissipation factor, of investigated samples was below 1 over the studied temperature–frequency window. It was seen that Co substitution decreased dielectric loss in the mid-to-high frequency region but raised low-frequency loss at advanced temperatures. The real part of impedance Z^' declined with both temperature and frequency, and Nyquist plots displayed depressed arcs, indicating non-Debye behavior with distributed grain and grain-boundary contributions. Arrhenius analysis of relaxation maxima yielded activation energies of ∼ 0.32–0.76 eV, consistent with oxygen-vacancy energies. Raman spectra revealed Co-induced lattice perturbations, including mode broadening/attenuation and red-shifts in the 200–600 cm− 1 range, together with a strengthened stretching feature near 600–650 cm− 1. Kubelka–Munk analysis was utilized to determine the band gaps of the studied samples, 2.19 eV (LFO), 2.29 eV (5
Co0.25Ni0.25Zn0.25Cu0.25AgxFe2-xO4 (0.00 ≤ x ≤ 0.10) nanospinel ferrites (Ag → CoNiZnCu (x ≤ 0.10) NSFs) have been synthesized via a one-pot sol–gel method. XRD analysis was applied to prove the phase formation for each product. The morphologies were confirmed via SEM/TEM. This study introduced a detailed analysis of the electrical and dielectric properties of ion-substituted spinel ferrites. AC/DC conductivity and complex impedance spectroscopy were both used to understand how substitution of Ag+ affects the charge-transport properties of these (NSFs). The unsubstituted NSF (x = 0.00) had very high resistivity (GΩ range) and was found to be mainly affected by one process of relaxation that occurred due to high resistive grain boundaries. After substitution with Ag, the DC conductivity increased dramatically by several orders of magnitude, which correlated with a massive reduction in resistance of the grain boundaries. The overall electrostatic and dielectric properties of these NSFs changed drastically with Ag+ ion substitution as well; this change was due to the presence of the effect known as colossal permittivity (ϵ′ > 104), which is explained by the Maxwell–Wagner theory of interfacial polarization. All types of analysis (using both the complementary impedance and electric modulus formalisms, including Nyquist plots) proved that by separating the different electrical responses from the grains and grain boundaries of the substituted NSFs, these materials are heterophase-natured. Therefore, it can be concluded that Ag + ion substitution is a very effective way to tune the properties of grain boundaries, resulting in a measurable difference in the total electrical/dielectric response of the NSFs.
