The growing accumulation of non-biodegradable synthetic plastic waste has intensified the urgent need for sustainable, bio-based alternatives. This study focuses on the valorization of underutilized Enset ( Ensete ventricosum ) fiber, an abundant agricultural residue in Ethiopia, into high-performance cellulose nanocrystal (CNC) as a green reinforcing agent for biodegradable packaging applications. A multi-step extraction protocol was employed, involving Soxhlet dewaxing, two-stage alkaline treatment, chlorine-free hydrogen peroxide bleaching, and acid hydrolysis followed by ultrasonication. The hydrolysis parameters (acid concentration, temperature, and time) were thoroughly optimized using Response Surface Methodology (RSM) based on a Box-Behnken design (BBD) to maximize CNC yield and minimize particle size. The optimized conditions (59.90% H 2 SO 4 , 77.93 ℃ , 63.22 min) yielded CNC with a high yield of 73.50 ± 3.2 wt% and a mean hydrodynamic diameter of 63.22 ± 6.5 nm. The model confirmed excellent reliability (R² = 0.988). Advanced characterization via FTIR, XRD, SEM, DLS, TGA/DTG, and BET confirmed the successful removal of amorphous components. FTIR indicates the newly produced CNCs is very much rich with active sites like –OH, -C-O-C-,while SEM exposed the raw fiber surface was rough, whereas the surface of CNCs became even after the removal of lignin, fatty, and waxy compounds. Crystallinity index of 82.09 ± 5.1%. Mechanical testing of neat CNC films demonstrated a tensile strength of 150 ± 2.1 MPa, confirming their load-bearing capacity the resulting CNC exhibited low water vapor (30 ± 3.1 g/m²·day), oxygen (2.3 ± 5.2 g/m²·day·atm), and grease (<0.12 g/m²·day) transmission rates, confirming their barrier performance. These findings establish enset fiber as a sustainable, high-value feedstock for CNC production, with significant potential for subsequent development in biodegradable packaging and nanocomposite applications, contributing to agricultural waste valorization and the circular bio-economy in Ethiopia. Continued advancements in integrating CNCs into diverse applications hold significant potential to transform industries seeking sustainable, high-performance materials.
The growing demand for high-performance lithium-ion batteries (LIBs) necessitates anode materials with favorable Li-storage capacity and transport kinetics. In this study, first-principles density functional theory (DFT) calculations are employed to investigate the structural, electronic, and electrochemical properties of oxygen-terminated Ti2MoC2O2 MXene as a potential anode material for LIBs. The optimized structure exhibits favorable energetic stability, with a calculated formation energy of −3.42 eV atom−1, while 10 ps ab initio molecular dynamics (AIMD) simulations at 300 K indicate preservation of the structural framework under the investigated conditions. Electronic-structure calculations reveal intrinsic metallic character, with states near the Fermi level dominated by Ti (3d) and Mo (4d) orbitals, supporting favorable electronic charge transport. Lithium adsorption calculations identify hollow sites as the most favorable adsorption configurations, with an adsorption energy of −2.06 eV.The calculated Li-migration barrier along the preferred pathway is 0.158 eV, corresponding to an estimated Li hopping rate of 2.13 × 1010 s−1vand an intrinsic diffusion coefficient of approximately 1.9 × 10−9 m2 s-1. The calculated stepwise lithiation voltages decrease from 2.07 to 1.07 V over the investigated Li-loading range. A stable four-Li configuration in the 2 × 2 × 1 supercell corresponds to a configuration-specific theoretical capacity of 108 mAh g−1. Together, the favorable Li adsorption, thermodynamic lithiation behavior, low preferred-pathway migration barrier, and preservation of structural integrity during 10 ps AIMD simulations at 300 K indicate favorable intrinsic Li-storage and Li-transport characteristics of Ti2MoC2O2. These results support further investigation of Ti2MoC2O2 as an anode material for lithium-ion batteries.
We characterize cyclicity, convex-cyclicity, and supercyclicity for weighted composition operators and their adjoints on the harmonic Fock space. We formulate the criteria for cyclicity in terms of restrictions on the coefficient of the linear term in the composition symbol, with different phenomena in the analytic and conjugate-linear cases. We prove that convex-cyclicity can occur only in the rotational case, under suitable spectral separation conditions. We further show that a weighted composition operator is cyclic, respectively convex-cyclic, if and only if its adjoint has the corresponding property, and we also give descriptions of the cyclic and convex-cyclic vectors. Finally, we show that neither weighted composition operators nor their adjoints can be supercyclic on the harmonic Fock space. A key ingredient in the proofs is a complete determination of the eigenvalues and eigenvectors of the adjoint operator, a result that may be of independent interest.
Despite international consensus and actions, child labour remains prevalent and a global agenda. This paper uniquely examines the dynamic impact of parental death on child labour using rich longitudinal data from Ethiopia, while acknowledging limitations for making full causal interpretation. The empirical analysis, based on a conditional staggered difference-in-difference estimator, reveals a statistically significant impact of parental death on child labour. I specifically documented four key results. First, the cumulative effect of parental death outweighs the immediate effect. Second, the findings suggest a relatively higher impact of parental death around the age of 12 than earlier or later ages. Third, parental loss appears to push children toward unpaid household services rather than economic activities. Finally, maternal death has a stronger association with child labour than paternal death. These findings highlight the need for context-specific policy interventions and long-term support initiatives to mitigate the negative consequences of orphanhood on child development. Future research should explore multi-country analyses to provide a more comprehensive understanding and enable broader generalization of the impact of parental death on child labour.
Shear failure in reinforced concrete beams is brittle and sudden, posing significant risks to structural safety, and the incorporation of steel fibers has emerged as an effective way to improve shear resistance in beams lacking transverse reinforcement. Predicting the shear strength of such steel fiber-reinforced concrete (SFRC) beams remains challenging because of the nonlinear interactions among fiber, matrix, and loading parameters that traditional empirical design equations, developed from limited datasets, cannot fully capture. Machine learning models can analyze large datasets and uncover these complex relationships, but their lack of interpretability limits engineering trust and adoption. To address this, we develop an explainable machine learning framework using SHAP, partial dependence plots (PDP), LIME, and feature importance analysis on a database of 488 SFRC beam tests spanning eight design parameters. Among several models evaluated, XGBoost achieved the best predictive performance (5-fold cross-validated R² = 0.890 ± 0.086; testing R² = 0.977 on an 80/20 holdout split), outperforming a multivariate regression baseline and four widely used empirical formulas. SHAP analysis, verified through bootstrap stability testing, identified beam depth and width as the most influential parameters, followed by the shear-span-to-depth ratio and concrete compressive strength, with the longitudinal reinforcement ratio, steel fiber factor, and fiber volume fraction contributing more modestly. These findings can help structural engineers prioritize key design parameters when assessing SFRC beam shear capacity and support the future development of interpretable, data-driven design tools to complement existing empirical code equations.
