Search Results (2,981)

Phytoremediation is an eco-friendly and in situ solution for remediating heavy metal-contaminated soils, yet practical application requires timely and accurate effectiveness evaluation. However, conventional chemical analysis of plant parts and soils is labor-intensive, time-consuming and limited for large-scale monitoring. This study proposed a rapid sensing framework integrating laser-induced breakdown spectroscopy (LIBS) with deep transfer learning and spectral indices to assess phytoremediation effectiveness of Sedum alfredii (a Cd/Zn co-hyperaccumulator). LIBS spectra were collected from plant tissues and diverse soil matrices. To overcome strong matrix effects, fine-tuned convolutional neural networks were developed for simultaneous multi-matrix quantification, achieving high-accuracy prediction for Cd and Zn (R²_test > 0.99). These predicted concentrations enabled calculating conventional phytoremediation indicators like bioconcentration factor (BCF), translocation factor (TF), plant effective number (PEN), and removal efficiency (RE), yielding recovery rates near 100% for TF and PEN. Additionally, novel spectral indices (SIs)—directly derived from characteristic wavelength combinations—were constructed to bypass intermediate quantification. SIs significantly improved the direct evaluation of Zn removal and translocation. Finally, a decision-level fusion strategy combining concentration predictions and SIs enhanced Cd removal assessment accuracy. This study validates LIBS combined with intelligent algorithms as a rapid sensor tool for monitoring phytoremediation performance, facilitating sustainable environmental management. Full article

Triply periodic minimal surface (TPMS) structures provide high surface area to volume ratios and tunable conduction pathways, but predicting their thermal behavior across different metallic materials remains challenging because multi-material experimentation is costly and full-scale simulations require extremely fine meshes to resolve the complex geometry. This study develops a physics-informed neural network (PINN) that reconstructs steady-state temperature fields in TPMS Gyroid structures using only two experimentally measured materials, Aluminum and Silver, which were tested under identical heat flux and flow conditions. The model incorporates conductivity ratio physics, Fourier-based thermal scaling, and complete spatial temperature profiles directly into the learning process to maintain physical consistency. Validation using the complete Aluminum and Silver datasets confirms excellent agreement for Aluminum and strong accuracy for Silver despite its larger temperature gradients. Once trained, the PINN can generalize the learned behavior to nine additional metals using only their conductivity ratios, without requiring new experiments or numerical simulations. A detailed heat transfer analysis is also performed for Magnesium, a lightweight material that is increasingly considered for thermal management applications. Since no published TPMS measurements for Magnesium currently exist, the predicted Nusselt numbers obtained from the PINN-generated temperature fields represent the first model-based evaluation of its convective performance. The results demonstrate that the proposed PINN provides an efficient, accurate, and scalable surrogate model for predicting thermal behavior across multiple metallic TPMS structures and supports the design and selection of materials for advanced porous heat technologies. Full article

Metal pollution from metallurgical emissions poses serious environmental and public health risks in Kazakhstan. A replicated pot-culture experiment (n = 4) in a completely randomized design under controlled phytotron conditions evaluated biomass production and metal accumulation in six crop and forage species, alfalfa (Medicago sativa), amaranth (Amaranthus spp.), corn (Zea mays), mustard (Brassica juncea), rapeseed (Brassica napus), and sunflower (Helianthus annuus); three ornamental species, purple coneflower (Echinacea purpurea), marigold (Tagetes spp., ‘Tiger Eyes’), and sweet alyssum (Lobularia maritima); and three native wild plants, greater burdock (Arctium lappa), horse sorrel (Rumex confertus), and mug wort (Artemisia vulgaris). Plants were grown in soils collected from the Qarmet industrial zone in Temirtau, central Kazakhstan. Initial soil analysis revealed substantial mixed-metal contamination, ranked as Mn > Ba > Zn > Sr > Cr > Pb > Cu > Ni > B > Co. Mn reached 1059 mg·kg⁻¹, ~50-fold higher than B (22.7 mg·kg⁻¹). Ba (620 mg·kg⁻¹) exceeded FAO/WHO limits sixfold, Zn (204 mg·kg⁻¹) surpassed the lower threshold, and Pb (41.6 mg·kg⁻¹) approached permissible levels, while Cr, Cu, Ni, Co, and Sr were lower. Biomass production varied markedly among species: corn and sunflower produced the highest shoot biomass (126.8 and 60.9 g·plant⁻¹), whereas horse sorrel had the greatest root biomass (54.4 g·plant⁻¹). Root-to-shoot ratios indicated shoot-oriented growth (>1–8) in most species, except horse sorrel and burdock (<1). Metal accumulation was strongly species-specific. Corn and marigold accumulated Co, Pb, Cr, Mn, Ni, Cu, B, and Ba but showed limited translocation (transfer function, TF < 0.5), whereas sunflower, amaranth, and mug wort exhibited moderate to high translocation (TF > 0.8 to <1) for selected metals. Corn is recommended for high-biomass metal removal, marigold for stabilization, sunflower, horse sorrel, and mug wort for multi-metal extraction, and amaranth and coneflower for targeted Co, Ni, and Cu translocation, supporting sustainable remediation of industrially contaminated soils. Full article

Accurate prediction of the effective thermal conductivity (ETC) of nuclear fuels is essential for optimizing fuel performance and ensuring reactor safety. However, the experimental determination of ETC is often limited by cost and complexity, while high-fidelity simulations are computationally intensive. This study presents a novel hybrid framework that integrates experimental data, validated mesoscale finite element simulations, and machine-learning (ML) models to efficiently and accurately estimate ETC for advanced uranium-based nuclear fuels. The framework was demonstrated on three fuel systems: UO₂-BeO composites, UO₂-Mo composites, and U-10Zr metallic alloys. Mesoscale simulations incorporating microstructural features and interfacial thermal resistance were validated against experimental data, producing synthetic datasets for training and testing ML algorithms. Among the three regression methods evaluated, namely Bayesian Ridge, Random Forest, and Multi-Polynomial Regression, the latter showed the highest accuracy, with prediction errors below 10% across all fuel types. The selected multi-polynomial model was subsequently used to predict ETC over extended temperature and composition ranges, offering high computational efficiency and analytical convenience. The results closely matched those from the validated simulations, confirming the robustness of the model. This integrated approach not only reduces reliance on costly experiments and long simulation times but also provides an analytical form suitable for embedding in engineering-scale fuel performance codes. The framework represents a scalable and generalizable tool for thermal property prediction in nuclear materials. Full article

Regenerative medicine is increasingly leveraging the synergies between bioinspired conductive biomaterials and 3D/4D bioprinting to replicate the native electroactive and hierarchical microenvironments essential for functional tissue restoration. However, a critical gap remains in the intelligent integration of these technologies to achieve dynamic, responsive tissue regeneration. This review introduces a “bioinspired material–printing–function” triad framework to systematically synthesize recent advances in: (1) tunable conductive materials (polymers, carbon-based systems, metals, MXenes) designed to mimic the electrophysiological properties of native tissues; (2) advanced 3D/4D printing technologies (vat photopolymerization, extrusion, inkjet, and emerging modalities) enabling the fabrication of biomimetic architectures; and (3) functional applications in neural, cardiac, and musculoskeletal tissue engineering. We highlight how bioinspired conductive scaffolds enhance electrophysiological behaviors—emulating natural processes such as promoting axon regeneration cardiomyocyte synchronization, and osteogenic mineralization. Crucially, we identify multi-material 4D bioprinting as a transformative bioinspired approach to overcome conductivity–degradation trade-offs and enable shape-adaptive, smart scaffolds that dynamically respond to physiological cues, mirroring the adaptive nature of living tissues. This work provides the first roadmap toward intelligent electroactive regeneration, shifting the paradigm from static implants to dynamic, biomimetic bioelectronic microenvironments. Future translation will require leveraging AI-driven bioinspired design and organ-on-a-chip validation to address challenges in vascularization, biosafety, and clinical scalability. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

The migration and transformation of Cr(VI) are primarily regulated by soil minerals, soil flora and fauna, hydrological conditions, and microbial communities, with these mechanisms being influenced by pH, temperature, and oxygen levels. In terms of single environmental media, relatively extensive research has been conducted on the behaviors of Cr(VI). However, studies on the migration and transformation of Cr(VI) from the perspective of the soil–groundwater multimedia system are rarely published. Therefore, this study comprehensively analyzes the migration and transformation behaviors of Cr(VI) from the perspective of the entire soil–groundwater system. By synthesizing the effects of individual factors, such as pH and organic matter, on Cr(VI) in both soil and groundwater, as well as interactions among these factors, we systematically clarify the patterns governing Cr(VI) migration and transformation under multi-factor coupling. Through the analysis of multiple factors in the complex system, the redox fluctuation zone at the soil–groundwater interface is a hot spot for Cr(VI) transformation, and the synergistic effect among climatic conditions, microbial community structure, and the aquifer interface significantly affects the transport efficiency of Cr(VI). The results of the present study could provide a theoretical framework for future research on the environmental behavioral effects of Cr(VI) at the soil–groundwater interface. Moreover, this study could provide important theoretical bases for the prevention and control of heavy metal pollution. Full article

Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article

Ceramic matrix composites (CMCs) exhibit excellent high-temperature strength, oxidation resistance, and fracture toughness, making them superior to traditional metals and single-phase ceramics in extreme environments such as aerospace, nuclear energy equipment, and high-temperature protection systems. The mechanical properties of CMCs directly influence the reliability and service life of structures; thus, accurately predicting their mechanical response and service behavior has become a core issue in current research. However, the multi-phase heterogeneity of CMCs leads to highly complex stress distribution and deformation behavior in traditional mechanical property testing, resulting in significant uncertainty in the measurement of key mechanical parameters such as strength and modulus. Additionally, the high manufacturing cost and limited experimental data further constrain material design and performance evaluation based on experimental data. Therefore, the development of effective numerical simulation and mechanical modeling methods is crucial. This paper provides an overview of the research hotspots and future directions in the field of CMCs numerical simulation and mechanical modeling through bibliometric analysis using the CiteSpace software. The analysis reveals that China, the United States, and France are the leading research contributors in this field, with 422, 157, and 71 publications and 6170, 3796, and 2268 citations, respectively. At the institutional level, Nanjing University of Aeronautics and Astronautics (166 publications; 1700 citations), Northwestern Polytechnical University (72; 1282), and the Centre National de la Recherche Scientifique (CNRS) (49; 1657) lead in publication volume and/or citation influence. Current research hotspots focus on finite element modeling, continuum damage mechanics, multiscale modeling, and simulations of high-temperature service behavior. In recent years, emerging research frontiers such as interface debonding mechanism modeling, acoustic emission monitoring and damage correlation, multiphysics coupling simulations, and machine learning-driven predictive modeling reflect the shift in CMCs research, from traditional experimental mechanics and analytical methods to intelligent and predictive modeling. Full article

Tunnel Oxide-Passivated Back Contact solar cells represent a next-generation photovoltaic technology with significant potential for achieving both high efficiency and low cost. This study addresses the challenge of low bifaciality inherent to the rear-side structure of TBC cells. Using the Quokka3 simulation and assuming high-quality surface passivation and fine-line printing accuracy, a systematic optimization was conducted. The optimization encompassed surface morphology, optical coatings, bulk material parameters (carrier lifetime and resistivity), and rear-side geometry (emitter fraction, metallization pattern and gap width). Through a multi-parameter co-optimization process aimed at enhancing conversion efficiency, a simulated conversion efficiency of 27.26% and a bifaciality ratio of 92.96% were achieved. The simulation analysis quantified the trade-off relationships between FF, bifaciality, and efficiency under different parameter combinations. This enables accurate prediction of final performance outcomes when prioritizing different metrics, thereby providing scientific decision-making support for addressing the core design challenges in the industrialization of TBC cells. Full article

To address the challenges posed by rock bolt corrosion to engineering safety and service life, this study focuses on corrosion detection through integrated image processing, deep learning, and feature extraction methods. An automatic corrosion identification model was constructed based on computer-vision object-detection algorithms. By incorporating a Feature Pyramid Network, the model’s multi-scale object-detection capability was significantly enhanced. The corrosion features were extracted via image binarization and grayscale matrix analysis. The binary image method accurately quantified pitting density, revealing an initial increase followed by a decrease over time. The corrosion morphology was simulated using a Fractional Brownian Motion model, validating the accuracy of fractal feature calculations. The fractal dimension increased significantly with prolonged corrosion time, which not only characterize surface roughness evolution and corrosion rate, but also provide a reliable quantitative indicator for metal corrosion assessment. This research offers a technical framework integrating image processing, deep learning, and fractal theory for rock bolt corrosion monitoring and maintenance. Full article

Numerical simulations of the forming limit diagram (FLD) for SUS430/Al1050/TA1 laminated metal composites (LMCs) are conducted through the crystal plasticity finite element (CPFE) model integrated with the Marciniak–Kuczyński (M–K) theory. Representative volume elements (RVEs) that reconstruct the measured crystallographic texture, as characterized by electron backscatter diffraction (EBSD), are developed. The optimal grain number and mesh density for the RVE are calibrated through convergence analysis by curve-fitting simulated stress–strain responses to the uniaxial tensile data. The established multi-scale model successfully predicts the FLDs of the SUS430/Al1050/TA1 laminated sheet under two stacking sequences, namely, the SUS layer or the TA1 layer in contact with the die. The Nakazima test results validate the effectiveness of the proposed model as an efficient and accurate predictive tool. This study extends the CPFE–MK framework to multi-layer LMCs, overcoming the limitations of conventional single-layer models, which incorporate FCC, BCC, and HCP crystalline structures. Furthermore, the deformation-induced texture evolution under different loading paths is analyzed, establishing the relationship between micro-scale deformation mechanisms and the macro-scale forming behavior. Full article

This study investigates a 26th Dynasty Ptah–Sokar–Osiris wooden statuette excavated from the Tari cemetery, Giza Pyramids area, to decode ancient Egyptian manufacturing techniques and establish evidence-based conservation strategies of such wooden objects. Using minimal sampling (1.0–2.0 mm²), integrated XRF, synchrotron-based X-ray diffraction, FTIR, and confocal microscopy distinguished original technological choices from burial-induced alterations. The 85 cm Vachellia nilotica sculpture exhibits moderate structural preservation (cellulose crystallinity index 62.9%) with partial chemical deterioration (carbonyl index 2.22). Complete pigment characterization identified carbon black, Egyptian Blue (cuprorivaite, 55 ± 5 wt %), atacamite-dominated green (65 ± 5 wt %) with residual malachite (10 ± 2 wt %), orpiment (60 ± 5 wt %), red ochre (hematite, 60% ± 5 wt %), white pigments (93 ± 5 wt % calcite), and metallic gold (40 ± 5 wt %). Confocal microscopy revealed sophisticated multi-pigment mixing strategies, with black carbon systematically blended with chromophores for nuanced color effects. Atacamite predominance over malachite provides evidence for chloride-mediated diagenetic transformation over 2600 years of burial. Consistent calcite detection (~ 20–45%) across colored layers confirms systematic ground layer application, establishing technological baseline data for 26th Dynasty Lower Egyptian workshops. Near-complete organic binder loss, severe lignin oxidation, and ongoing salt-mediated mineral transformations indicate urgent conservation needs requiring specialized consolidants, paint layer stabilization, and controlled environmental storage. This investigation demonstrates synchrotron methods’ advantages while establishing a minimally invasive framework for studying polychrome wooden artifacts. Full article

As a potential strategic resource of critical metals, deep-sea cobalt-rich crusts represent one of the most promising metal reservoirs within oceanic seamount systems, and their metallogenic mechanism constitutes a frontier topic in deep-sea geoscience research. This review focuses on the cobalt-rich crusts from the Magellan Seamount region in the northwestern Pacific and synthesizes existing geological, mineralogical, and geochemical studies to systematically elucidate their mineralization processes and metal enrichment mechanisms from a microstructural perspective, with particular emphasis on cobalt enrichment and its controlling factors. Based on published observations and experimental evidence, the formation of cobalt-rich crusts is divided into three stages: (1) Mn/Fe colloid formation—At the chemical interface between oxygen-rich bottom water and the oxygen minimum zone (OMZ), Mn²⁺ and Fe²⁺ are oxidized to form hydrated oxide colloids such as δ-MnO₂ and Fe(OH)₃. (2) Key metal adsorption—Colloidal particles adsorb metal ions such as Co²⁺, Ni²⁺, and Cu²⁺ through surface complexation and oxidation–substitution reactions, among which Co²⁺ is further oxidized to Co³⁺ and stably incorporated into MnO₆ octahedral vacancies. (3) Colloid deposition and mineralization—Mn–Fe colloids aggregate, dehydrate, and cement on the exposed seamount bedrock surface to form layered cobalt-rich crusts. This process is dominated by the Fe/Mn redox cycle, representing a continuous evolution from colloidal reactions to solid-phase mineral formation. Biological processes play a crucial catalytic role in the microstructural evolution of the crusts. Mn-oxidizing bacteria and extracellular polymeric substances (EPS) accelerate Mn oxidation, regulate mineral-oriented growth, and enhance particle cementation, thereby significantly improving the oxidation and adsorption efficiency of metal ions. Tectonic and paleoceanographic evolution, seamount topography, and the circulation of Antarctic Bottom Water jointly control the metallogenic environment and metal sources, while crystal defects, redox gradients, and biological activity collectively drive metal enrichment. This review establishes a conceptual framework of a multi-level metallogenic model linking macroscopic oceanic circulation and geological evolution with microscopic chemical and biological processes, providing a theoretical basis for the exploration, prediction, and sustainable development of potential cobalt-rich crust deposits. Full article

Plasma nitriding is an advanced method to increase the hardness and wear resistance of different metal parts with complex shapes and geometries. The modelling is an appropriate approach for better understanding and improving such technologies based on multi-physical processes. Mathematical models of the coupled electromagnetic, fluid flow, and thermal processes in vacuum chambers for the low-temperature plasma treatment of metal parts have been developed. They were solved numerically via ANSYS/CFX software for a discretized solid and gas space of a plasma nitriding chamber. The specific electrical conductivity of the gas mixture, containing plasma, has been calibrated on the basis of an electrical model of the chamber and in situ measurements. The three-dimensional fields of pressure, temperature, velocity, turbulent characteristics, electric current density, and voltage in the chamber have been simulated and analysed. Methods for further development and application of the models and for technological and constructive enhancement of the plasma treatment technologies are discussed. Full article