Search Results (701)

Waste-derived adsorbents represent a promising strategy for sustainable wastewater treatment; however, adsorption processes are frequently governed by complex nonlinear interactions that remain difficult to interpret using conventional approaches. The present study developed an integrated experimental and explainable machine learning framework to investigate Congo red (CR) removal using cetyltrimethylammonium bromide (CTABr)-modified bio-apatite-based adsorbent. The adsorbent was characterized using SEM–EDX, FTIR, XRD, and thermal analyses, confirming a heterogeneous Ca–P-rich structure associated with hydroxyapatite and CTABr-derived surface functionalities. A dataset comprising batch adsorption experiments was generated by varying solution pH, contact time, initial CR concentration, CTABr concentration, adsorbent dose, and temperature. The experimental results demonstrated that CR removal efficiency ranged from approximately 22.9% to 100%, with the experimental conditions giving the highest capacity (pH ≈ 2, contact time ≈ 120 min, initial CR concentration ≈ 10 mg L⁻¹, CTABr concentration ≈ 0.5 meq g⁻¹, adsorbent dose ≈ 0.2 g L⁻¹, and temperature ≈ 25 °C). Elastic Net, Random Forest (RF), and Gradient Boosting (GB) models were developed and evaluated using nested repeated five-fold cross-validation. The nonlinear ensemble models outperformed the regularized linear approach, indicating that is associated with complex nonlinear relationships among operational variables. RF provided the best predictive performance (R² = 0.675 ± 0.167, RMSE = 14.63 ± 5.81 percentage points) and was subsequently interpreted using SHapley Additive exPlanations (SHAP). Explainable analysis identified initial CR concentration as the dominant predictor within the fitted model and the investigated experimental domain, followed by contact time, adsorbent dose, pH, CTABr concentration, and temperature. SHAP dependence analysis revealed nonlinear behaviors, including concentration-dependent suppression, optimum-like dose effects, and CTABr saturation responses. Integration of characterization and modeling results suggested a multimodal adsorption mechanism involving electrostatic attraction between CTABr-derived quaternary-ammonium groups and CR sulfonate groups, complemented by interactions with the hydroxyapatite matrix, hydrogen bonding, and hydrophobic interactions. The proposed framework demonstrates how explainable machine learning can bridge predictive modeling and adsorption process interpretation while supporting the valorization of waste-derived materials for water treatment. Full article

Wide-bandgap indium-oxide thermoelectric materials face a strong electrical–thermal transport trade-off and low structural reliability, limiting their thermoelectric efficiency and practical deployment on welding robots. To overcome these bottlenecks, gradient Na-doped In₂O₃ ceramics are fabricated by mechanical alloying combined with spark plasma sintering. A dual-functional modulation mechanism via monovalent alkali-metal doping is proposed to decouple thermoelectric performance. Unlike conventional high-valence doping that degrades the Seebeck coefficient and raises thermal conductivity, moderate Na substitution introduces shallow acceptor states, oxygen vacancies, or other compensating defects inside the bandgap, precisely tuning the Fermi level and carrier concentration within the optimal transport window. This mild electronic-structure reconstruction balances conductivity and Seebeck coefficient, boosting the power factor without carrier overflow or saturation. The ionic-size mismatch between Na⁺ and In³⁺ generates controllable point defects and uniform lattice strain, scattering multi-frequency phonons to suppress lattice thermal conductivity while avoiding excessive electronic thermal conductivity. Na doping also improves lattice bonding and thermomechanical properties, enhancing Vickers hardness to offset doping-induced mechanical deterioration. Supported by first-principles calculations, this work reveals the mechanism of shallow-level electronic modulation coupled with lattice-strain engineering. The optimized sample delivers good medium-temperature conversion efficiency and structural stability. This study achieves simultaneous improvement in thermoelectric and mechanical properties, fills the research gap for alkali-metal-modified In₂O₃, and offers theoretical guidance for high-performance oxide thermoelectric material design. Full article

Hyperbolic interface problems are widely applied to model wave propagation and shock transmission across discontinuous media, such as acoustic waves in layered materials, seismic waves in the Earth’s crust, and stress or electromagnetic waves in composite structures. This study introduces a novel computational framework for hyperbolic interface problems, specifically designed to unify and extend the treatment of regular interfaces within partial differential equations. The proposed hybrid approach combined Haar wavelet-based spatial discretization with finite difference schemes for temporal integration. By employing truncated Haar series to approximate spatial derivatives and leveraging finite difference techniques for time evolution, the method delivers accurate solutions for both linear and nonlinear systems regardless of whether the governing coefficients are constant or spatially variable. In addressing linear problems, the resulting algebraic equations are solved efficiently using Gaussian elimination. For nonlinear formulations, the method incorporates a quasi-Newton linearization strategy, effectively transforming the system into a linear one. Extensive validation is performed through a suite of benchmark problems, with performance assessed via metrics including maximum absolute errors (MAEs), root mean square errors (RMSEs), and convergence behavior as a function of collocation point (CP) density. Numerical experiments highlight the method’s superior stability and accuracy, particularly in scenarios marked by discontinuities or sharp gradients in the solution. The approach proves especially effective in bridging inconsistencies between boundary and initial conditions, offering a robust alternative to existing techniques. Theoretical soundness, strong convergence properties, and comprehensive numerical validation collectively underscore the method’s reliability and adaptability across a broad spectrum of applications. Full article

The development of sustainable soil amendment materials derived from natural and recycled resources represents an important strategy for improving soil functionality and mitigating the impacts of land degradation and climate change. This pot study evaluated the effects of biochar (BC), poultry litter (PL), smectite-silica clay (SSC), and two composted combinations, C(PL+BC) and C(PL+BC+SSC), on soil biochemical activity, pore structure, and water retention. Seven treatments included an unfertilized control, mineral fertilization (MF), and MF combined with each amendment. The highest cumulative oxygen demand was recorded in MF+SSC (1.805 mg O₂ g⁻¹ DM), whereas the lowest occurred in MF+C(PL+BC) (1.356 mg O₂ g⁻¹ DM). Mineral fertilization produced the highest dehydrogenase activity. Compared with CTR, MF+BC had a larger volume of storage pores (0.5–50 μm) and produced the highest field capacity (0.385 cm³ cm⁻³) and available water content (0.265 cm³ cm⁻³). MF+SSC had the highest bulk density (1.309 g cm⁻³) and permanent wilting point (0.120 cm³ cm⁻³), but its available water content did not differ significantly from CTR. The first two principal components explained 58.0% of the variance and represented gradients associated with water retention and with bulk density and fine porosity. Among the tested materials, BC and C(PL+BC+SSC) provided the most balanced improvements in soil functionality by simultaneously enhancing water retention and maintaining moderate biological activity. The results indicate that biochar-, clay-, and compost-based amendment materials have potential for improving soil quality and supporting sustainable land management. Full article

Background: The clinical success of modern dental implants requires a balance between mechanical endurance and aesthetic integration. While titanium alloy (Ti-6Al-4V) provides a reliable load-bearing core, yttria-stabilized tetragonal zirconia (Y-TZP) is frequently preferred for the cervical collar to secure optimal peri-implant soft tissue responses. Yet, fusing these materials directly creates a structural challenge, an abrupt stiffness gradient. This discontinuity promotes localized tensile stresses within the brittle ceramic component, elevating the risk of subcritical crack initiation under oblique masticatory loads. Methods: To address this challenge, we conducted a narrative review to establish the rationale for a novel Ti-PEEK-Zr tri-layered concept. This approach integrates materials science and dental biomechanics to provide a theoretical framework prior to experimental testing. Results: The synthesized data supports the integration of polyetheretherketone (PEEK) as an intermediate compliant layer. Rather than serving as an intermediate stiffness layer, PEEK operates as a viscoelastic buffer. This functional transition zone dampens oblique forces, redistributing localized stress away from the fragile rigid–rigid junction and shielding the Y-TZP collar. The modular tri-layered configuration offers a theoretically sound mechanical hypothesis, though its clinical feasibility depends on rigorous validation that must encompass not only biomechanical performance but also biological compatibility, resistance to bacterial colonization, and long-term stability under the challenging conditions of the oral environment. The practical advantages, including manufacturability, surgical handling, and cost-effectiveness, remain to be demonstrated through future experimental and numerical studies. Conclusions: The Ti-PEEK-Zr multi-material concept is a biomechanical hypothesis. By functionally isolating the roles of each material, this paradigm addresses several limitations of traditional hybrid implants, providing the basis for future finite element analyses. Full article

Extracellular vesicles (EVs) have dominated research on intercellular communication for decades. However, the discovery of exomeres in 2018 revealed that conventional exosome preparations contain large quantities of long-overlooked non-membranous nanoparticles. Additional non-vesicular extracellular nanoparticles (NVEPs), including supermeres and SECmeres, have since been reported. Most of these particles are smaller than 50 nm in diameter, yet they carry protein and RNA cargo that was previously thought to be delivered exclusively by exosomes. Consequently, certain functions once attributed to exosomes now require reassignment. This review summarizes the classification, cargo composition, and structural features of NVEPs. It then compares the respective limitations of differential ultracentrifugation, density gradient centrifugation, size-exclusion chromatography, and flow field-flow fractionation for NVEP isolation, together with strategies for their combined use. Furthermore, the functional mechanisms that distinguish NVEPs from vesicular EVs are discussed at three levels: the extracellular stability of non-vesicular extracellular RNA (nv-RNA), cellular uptake pathways, and downstream effects. Current evidence indicates that NVEPs are enriched for disease-associated cargo in cancer and neurodegenerative diseases and show potential for liquid biopsy and drug delivery. Nevertheless, ambiguous definitional boundaries, the absence of reference materials, and insufficient in vivo evidence jointly hinder quantification and clinical translation in this field. Because these limitations arise largely from the absence of unified technical criteria, establishing internationally recognized standards for isolation and characterization is a prerequisite for the maturation of NVEP research. Full article

Aluminum alloys used in aircraft structures are susceptible to corrosion fatigue cracking under combined aggressive environments and cyclic loading, threatening structural integrity. Pure data-driven models often fail under distribution shifts, while single-physics-informed neural networks (PINNs) lack flexibility in complex conditions. This paper proposes a dual-physics-informed neural network (DPINN) that integrates Walker and Forman crack growth models into a deep residual network. The model adaptively fuses both physical formulas via a trainable weight α and predicts material constants. A hybrid loss function with α regularization ensures physically consistent predictions. Using comprehensive corrosion fatigue data covering eight aluminum alloys, we evaluate the model on an internal test set and, more importantly, on an independent external test set simulating real-world distribution shifts. We further investigate an incremental learning scenario where the model is sequentially fine-tuned with increasing fractions of the external set. Results demonstrate that DPINN rapidly rectifies initial distribution mismatch, crossing the engineering reliability threshold (R² > 0.90) at an early incremental stage, and achieves superior performance after fine-tuning, significantly outperforming both a single Walker-PINN and gradient boosting regressors. SHAP feature importance analysis identifies ΔK and stress ratio as dominant drivers, confirming mechanistic consistency. The proposed architecture offers a data-efficient and interpretable tool for corrosion fatigue crack growth prediction in aluminum alloy structures. Full article

Additive manufacturing (AM) can support more sustainable polymer production, but the benefit is conditional on process energy, material chemistry, build yield, post-processing, service life, repair, and end-of-life recovery. This critical integrative review compares three polymer AM routes: stereolithography (SLA), digital light processing (DLP), and fused deposition modelling (FDM) through two deliberately contrasting application scales: pallet-scale load-bearing structures and membrane feed spacers. The review distinguishes direct application evidence from design opportunities inferred from adjacent AM literature. For pallet-scale structures, the literature currently supports large-format thermoplastic extrusion, zoned cellular architectures, modular repair, and controlled recycled feedstock as plausible translation routes, but direct peer-reviewed evidence for fully additively manufactured transportation pallets remains very limited. For membrane feed spacers, direct studies provide stronger quantitative evidence: published 3D-printed designs have reported approximately threefold pressure-drop reduction with doubled specific water flux, pressure-drop gradients as low as 0.091 bar m⁻¹ under reported test conditions, and a 16% increase in permeate flux with a thinner fouling layer for a honeycomb geometry. Process-energy evidence also shows that results depend strongly on the functional unit and machine state; reported desktop values span 24.8–85.7 kJ cm⁻³ for FFF and 10.8–21.5 kJ cm⁻³ for SLA, while post-processing and machine utilization can materially change the lifecycle result. Recycled polymers likewise involve a performance–circularity trade-off: some post-consumer PLA studies report strength losses of about one-third or more, whereas controlled blends and recycling strategies can retain a much larger fraction of virgin-material performance. The synthesis therefore treats geometry, process parameters, material state, operational performance, lifecycle impact, and cost as one coupled design problem rather than assuming that AM, recycled content, or bio-based chemistry is inherently sustainable. Full article

Topology optimization is an effective method for obtaining high-performance material distributions with novel configurations. However, its application to complex electromagnetic devices remains challenging because of the difficulty of deriving sensitivities and the low computational efficiency associated with repeated finite element method (FEM) evaluations for nonlinear materials. This paper proposes an efficient gradient-free topology optimization method that integrates superellipse curves with a multilayer perceptron (MLP) surrogate model while accounting for nonlinearity. First, based on the general superellipse curve, an improved expression is introduced, in which the size, shape, and position can be flexibly controlled by only seven parameters. Then, a parameterized superellipse-curve-based gradient-free topology optimization framework is established, which can be applied to complex electromagnetic devices with nonlinear materials and complex objective functions. Moreover, a lightweight MLP-based surrogate model is constructed using limited training samples generated by Latin hypercube sampling and FEM, and it can replace FEM for evaluating nonlinear material behavior with negligible computational cost. Finally, the proposed topology optimization framework is applied to the design of a magnetic actuator, both with and without considering nonlinear B–H characteristics, demonstrating its effectiveness. Full article

The deep sea harbors a substantial proportion of the ocean’s unexplored biological and chemical diversity, while hydrothermal vent ecosystems expose resident organisms to unusual combinations of hydrostatic pressure, steep chemical gradients, reduced compounds, and elevated metal concentrations. This Review examines the deep-sea vent mussel Bathymodiolus azoricus, a dominant species at Mid-Atlantic Ridge hydrothermal fields, as a source of biological mechanisms and molecular systems with potential biomedical relevance. We integrate three areas that have largely developed separately in the literature: innate immunity and host–symbiont interactions, mussel-derived wet adhesion and byssal structural proteins, and shell biomineralization and repair. These published observations are complemented by targeted reanalyses of legacy and more recent B. azoricus transcriptomic resources, used here as supporting transcriptomic evidence for molecular families relevant to these themes rather than as standalone genome-scale transcriptomic studies. Particular attention is given to mussel foot proteins and byssal collagens as candidate templates for wet-tissue adhesives and structural biomaterials, and to shell-derived calcium carbonate as a potential precursor for calcium-phosphate-based materials. We further advance a specific, testable hypothesis—long-term exposure to the metal-rich hydrothermal vent environment may have influenced the metal-binding chemistry of B. azoricus adhesive and structural proteins, potentially generating functional properties distinct from those of shallow-water mytilids. This possibility is biologically plausible in light of established DOPA–metal coordination mechanisms in mussel adhesion, but no direct comparative measurements of Fe³⁺-binding affinity, metal-mediated cross-linking, or adhesive performance currently demonstrate such an advantage in B. azoricus. The species should therefore be regarded not as a proven source of superior vent-adapted biomaterials, but as a well-suited experimental system in which immunity, bioadhesion, biomineralization, and environmental adaptation converge to generate specific hypotheses for biomedical discovery. Comparative functional studies, protein-level validation of transcript-derived candidates, and improved molecular characterization of foot and mantle tissues will be required to test these possibilities. Full article

Sodium metal anodes (SMAs) are regarded as the most promising anode materials for next-generation high-energy-density sodium metal batteries, owing to their ultrahigh theoretical specific capacity (1166 mAh g⁻¹) and low electrochemical potential (−2.71 V vs. SHEs). However, their practical application is severely hindered by a series of interrelated challenges, including unstable solid electrolyte interphase (SEI) films, severe volume fluctuations arising from their hostless nature, uncontrollable dendrite growth, and the consequent low Coulombic efficiency and short cycle life. This review systematically summarizes recent progress in stabilizing SMAs through three major categories of strategies: current collector engineering, which involves the design of planar, three-dimensional, and gradient architectures to regulate the local current density and Na⁺ flux, thereby guiding uniform nucleation and enabling “bottom-up” dendrite-free deposition; electrolyte engineering, which focuses on optimizing solvents, salts, and functional additives to tailor the solvation structure, construct robust inorganic-rich SEI layers, and utilize electrostatic shielding effects to suppress dendrite formation; and artificial SEI engineering, which aims to pre-construct inorganic or inorganic–organic hybrid protective layers that establish a physicochemical barrier between the electrode and electrolyte, combining high ionic conductivity, superior mechanical strength, and sufficient flexibility. Finally, we provide a critical perspective on the remaining challenges and outline future research directions, emphasizing the importance of in situ/operando characterization, synergistic multi-strategy integration, breakthroughs in high areal capacity and high-rate performance, and artificial intelligence-driven material discovery for the practical implementation of SMAs. Full article

Transformer lifecycle data across organizations are typically vertically partitioned among material suppliers, manufacturers, logistics service providers, testing agencies, and operation and maintenance units. This study presents a reproducible multi-layer vertical federated learning (VFL) prototype that integrates salted-hash identifier matching, additive sharing of local score vectors over finite fields, and a local public key infrastructure with a signature-based audit verification mechanism. A deterministic synthetic dataset is first constructed, comprising 5200 aligned records and 31 predictor variables, which are partitioned among five participants with varying numbers of features per participant. Second, across five validation runs, the VFL models under both the standard block-wise and score-sharing paths achieved an AUC of 0.8825 ± 0.0119, an F1 score of 0.7367 ± 0.0249, and an accuracy of 0.8102 ± 0.0183 on the test set. The classification results of both paths were fully consistent with the centralized gradient-descent logistic regression baseline. Notably, the score-sharing path exhibited a maximum log-odds deviation of only 2.22 × 10⁻⁸ on the test set, with no prediction discrepancies observed. Third, across 10 independently generated synthetic populations, the nonlinear output mechanism highlights the limitations of linear models: the AUC of vertical federated learning (VFL) drops to 0.6457 ± 0.0171, while Extra Trees and HistGradientBoosting achieve 0.7731 ± 0.0149 and 0.7743 ± 0.0139, respectively. Finally, in a separate residual-sharing diagnostic test, when 1 to 4 participants jointly shared the residuals, the label inference AUC remained around 0.499–0.500; however, when all five participants shared or plaintext residuals were used, the labels could be fully recovered. Both simple membership inference diagnostic tests yielded results close to random. The local signature log verifier rejected all 700 injected faults and accepted the 400 clean control log events. These results validate the feasibility of numerical reproducibility and local audit functionality under synthetic data and single-process conditions, yet they are insufficient to demonstrate end-to-end label privacy protection, malicious security, effectiveness on real data, or real-time ledger performance. Full article

We formulate a gauge-invariant phenomenological theory for a charged three-component condensate in an anisotropic layered superconductor. The construction is conditional on a material-specific pairing calculation selecting an isolated, predominantly triplet channel; it does not infer triplet pairing from layering or spin–orbit coupling alone. The order parameter is represented equivalently by a spin-1 spinor, a complex d-vector, and a pure-vector complex quaternion. Only the mapping and the density-spin bilinear are retained in the main text. A static Ginzburg–Landau functional then yields axial-polar, planar-polar, easy-axis polarized, and broken-axisymmetry mean-field states. Conservative Gross–Pitaevskii dynamics are introduced only as an additional composite-boson limit, not as a generic consequence of the Ginzburg–Landau theory. In that limit, analytic spectra are given for the axial-polar and easy-axis phases: the transverse spin branch softens at the axial-polar to broken-axisymmetry boundary, whereas crystal locking gaps the transverse magnon of the polarized phase. We do not claim a closed analytic spectrum for the mixed broken-axisymmetry phase. The static transverse current-response kernel provides a quantitative link between penetration-depth anisotropy and the gradient tensor. Ideal Bose condensation and BKT formulas are stated only in their controlled three- and two-dimensional limits. The framework therefore supplies a compact, falsifiable set of phase, mode, and response relations without introducing additional quaternionic degrees of freedom. Full article

Styrene–butadiene–styrene (SBS)-modified asphalt is a physical polymer gel system in which SBS forms a three-dimensional elastic network within the asphalt matrix. This network structure governs the rheological and mechanical properties of the material, yet the quantitative relationships among processing parameters, material composition, microstructure, and macroscopic performance remain insufficiently understood. This study proposes a multi-scale machine learning framework to establish processing–composition–structure–performance mappings for SBS-modified asphalt gels. A dataset of 1072 experimental samples was compiled from a gene database and supplementary laboratory tests. Ten input features were used to predict four performance indicators: penetration, softening point, ductility, and viscosity at 135 degrees Celsius. Four machine learning models were developed and compared. The support vector machine with radial basis function kernel achieved the highest accuracy for penetration with an R² value of 0.9997 and for ductility with an R² value of 0.9996. The artificial neural network performed best for softening point with an R² of 0.9996, and extreme gradient boosting for viscosity with an R² of 0.9993. Optuna-based optimization improved the average R² by 2.1% over default configurations. SHAP analysis identified shear temperature, SBS dosage, and SBS particle size as the most influential factors. The framework enables accurate and interpretable prediction of gel properties and provides a data-driven foundation for material design. Full article

AlSb film has attracted attention for its excellent properties, and many preparation methods have been explored. Herein, AlSb thin films were prepared by the DC magnetron co-sputtering method, and the interfacial behavior between the films and air molecules was investigated by X-ray diffraction (XRD), Auger electron spectroscopy (AES) testing, and density functional theory (DFT) calculations to elucidate the deliquescence process of AlSb thin films and its underlying mechanism. The results revealed that AlSb thin films exhibited Sb₂O₄ and Sb₂O₅ phases, while the thin films doped Cu no longer showed any Sb oxide phases after the film was exposed to air for one day. The chemical state of aluminum in the film remained stable along the depth direction, whereas antimony exhibited a pronounced gradient in chemical state from the surface to the interior. The oxidation state of Sb ions varied from −3 in the interior to +5 at the surface. The interaction between the (111) crystal plane of the AlSb film and air molecules is an exothermic process, with water molecules exhibiting the highest adsorption energy on the film surface, followed by oxygen molecules. The adsorption energies for nitrogen and carbon dioxide molecules were the lowest. Consequently, AlSb molecules readily combine with H₂O molecules. Furthermore, doping the AlSb film with copper or zinc atoms effectively reduced the adsorption energy for water and oxygen molecules, offering a new approach to suppress the deliquescence and oxidation of AlSb thin films. This study provides an important theoretical foundation for subsequent research on this material system. Full article