Search Results (121)

In this study, the influence of annealing on the phase evolution and mechanical properties of the Fe₆₂Ni₁₈P₁₃C₇ (at.%) alloy was investigated. Ribbons produced via melt-spinning were annealed at various temperatures, and their structural transformations and hardness were evaluated. The alloy exhibited a narrow supercooled liquid region (ΔT_x ≈ 22 °C), confirming its low glass-forming ability (GFA). Primary crystallization began at approximately 380 °C with the formation of α-(Fe,Ni) and Fe₂NiP, followed by the emergence of γ-(Fe,Ni) phase at higher temperatures. A significant increase in hardness was observed after annealing up to 415 °C, primarily due to nanocrystallization and phosphide precipitation. Further heating resulted in a hardness plateau, followed by a noticeable decline. Additionally, samples were produced via selective laser melting (SLM). The microstructure of the SLM-processed material revealed extensive cracking and the coexistence of phosphorus-rich regions corresponding to Fe₂NiP and iron-rich regions associated with γ-(Fe,Ni). Full article

The identification of suitable alloy compositions for the formation of bulk metallic glasses (BMGs) is a key challenge in materials science. In this study, we developed machine learning (ML) models to predict the critical casting diameter ($D_{max}$) of Fe-based BMGs, enabling rapid assessment of glass-forming ability (GFA) using composition-based and calculated thermophysical features. Three datasets were constructed: one based on alloy molar fractions, one using thermophysical quantities calculated via the CALPHAD method, and another utilizing Magpie-derived features. The performance of various ML models was evaluated, including support vector machines (SVM), XGBoost, and ensemble methods. Models trained on thermophysical features outperformed those using only molar fractions, with XGBoost and SVM models achieving test $R^2$ scores of up to 0.63 and 0.60, respectively. Magpie features yielded similar results but required a larger feature set. To enhance predictive accuracy, we explored data augmentation using the PADRE method and a modified version (PADRE-2). While PADRE-2 demonstrated slight improvements and reduced data redundancy, the overall performance gains were limited. The best-performing model was an ensemble combining SVM and XGBoost models trained on thermophysical and Magpie features, achieving an $R^2$ score of 0.69 and MAE of 0.69, comparable to published results obtained from larger datasets. However, predictions for high $D_{max}$ values remain challenging, highlighting the need for further refinement. This study underscores the potential of leveraging thermophysical features and advanced ML techniques for GFA prediction and the design of new Fe-based BMGs. Full article

This study investigates the use of Glass Fine Aggregate (GFA) and Fly Ash (FA) in mortar for Alkali–Silica Reaction (ASR) mitigation through a multidimensional evaluation. GFA was used to replace river sand in 20% increments up to 100%, while FA replaced cement at 10%, 20%, and 30%. Three GFA size ranges were considered: <1.18 mm, 1.18–4.75 mm, and a combined fraction of <4.75 mm. At 100% replacement, <1.18 mm GFA reduced ASR expansion to 0.07%, compared to 0.2% for <4.75 mm and 0.46% for 1.18–4.75 mm GFA. It also improved long-term strength by 25% from 28 days to 6 months due to pozzolanic activity. However, refining GFA to below 1.18 mm increased environmental impacts and resulted in a 4.2% increase in energy demand due to the additional drying process. Incorporating 10% FA reduced ASR expansion to 0.044%, had no significant effect on strength, and decreased key environmental burdens such as toxicity by up to 18.2%. These findings indicate that FA utilisation offers greater benefits for ASR mitigation and environmental sustainability than further refining GFA size. Therefore, combining <4.75 mm GFA with 10% FA is identified as the optimal strategy for producing durable and sustainable mortar with recycled waste glass. Full article

This study investigates the effects of molybdenum (Mo) additions on the crystallization behavior and soft magnetic properties and of Fe_82-xSi₄B₁₂Nb₁Mo_xCu₁ (x = 0–2) nanocrystalline alloys. Molybdenum enhances glass-forming ability (GFA) and magnetic properties by increasing negative mixing enthalpy ($∆{\mathrm{H}}_{\mathrm{m}\mathrm{i}\mathrm{x}}$), mixing entropy ($∆{\mathrm{S}}_{\mathrm{m}\mathrm{i}\mathrm{x}}$), and atomic size mismatch ($\delta$), which stabilize the amorphous phase. X-ray diffraction (XRD) analysis shows that Mo addition improves amorphous phase stability, further enhancing GFA. The simultaneous addition of Mo and Nb increases mixing entropy, promotes nucleation rates, and creates favorable conditions for optimizing nanocrystallization. Upon annealing, this optimized microstructure demonstrated low coercivity and high permeability. Notably, the Fe₈₀Si₄B₁₂Nb₁Mo₂Cu₁ ribbon, annealed at 470 °C for 10 min, exhibited exceptional soft magnetic properties, with a coercivity of 4.54 A/m, a maximum relative permeability of 48,410, and a saturation magnetization of 175.24 emu/g. High-resolution transmission electron microscopy (TEM) revealed an average crystal size of 18.16 nm. These findings suggest that Fe_82-xSi₄B₁₂Nb₁Mo_xCu₁ (x = 0–2) nanocrystalline alloys are suitable for advanced electromagnetic applications pursuing miniaturization and high efficiency. Full article

To utilize garbage fly ash (GFA) as a resource, this research proposes a method for preparing GFA with higher reactivity through carbonation and applies it to the production of foamed concrete. The effects of CO₂-cured GFA substitution rate and foam volume on slump flow, rheological properties, mechanical strength, thermal conductivity, water absorption rate, and water resistance coefficient of foam concrete are clarified. The results show that an increase in the CO₂-cured GFA substitution rate from 0 to 100% improves the slump flow by 10.8%~34.5% and decreases the plastic viscosity by 4.8%~36.4% and yield stress by 5.6%~28.1%. The higher carbonized GFA substitution rate can prolong the initial setting time with the largest amplitude of 30.4%. In addition, increasing the CO₂-cured GFA substitution rate improves the mechanical strengths, water resistance, thermal conductivity, and solidification of heavy metals. When the CO₂-cured GFA substitution rate is 100%, the 28-day compressive strength, 28-day flexural strength, water absorption rate, water resistance coefficient, thermal conductivity, leached Zn, and leached Cr of foam concrete are 18 MPa, 3.6 MPa, 20.7%, 0.46, 0.69 W·m⁻¹·K⁻¹, 9.4 × 10⁻⁵ mg/mL, and 8.6 × 10⁻⁵ mg/mL, respectively. Moreover, more foam volume improves the fresh-mixed performance of foam concrete while reducing the mechanical strength, water resistance property, thermal conductivity, and solidification of heavy metals. It is found that the technical approach for preparing foamed concrete containing CO₂-cured GFA with 40% foam volume can achieve its large-scale use. Full article

Background: The Petroleum Products Distribution Company in Anbar Governorate is responsible for securing and distributing petroleum products to various sectors, including transportation, agriculture, industry, and households, through over 100 gas stations. The company has faced significant challenges due to the destruction of its infrastructure caused by past conflicts. These challenges have necessitated strategic decisions to design an efficient distribution network. Methods: This study aimed to assist the company in selecting the optimal location for a distribution center by evaluating four potential locations. Three of the proposed locations were suggested by the company: Ramadi, Habbaniyah, and Haqlaniyah. The fourth location, referred to as the GFA DC location, was determined through a greenfield analysis (GFA) experiment using AnyLogistix software (version 3.2.1. PLE) ALX. The simulation experiment in ALX was conducted using product data, fuel station locations, order quantities, distribution center data, and transportation and emissions data. Results: The simulation results, taking into account both practical and regulatory constraints, indicated that the Ramadi location was the most suitable for establishing the new distribution center. Conclusions: Based on the analysis, the study concluded that the Ramadi location was the optimal site for building the petroleum products distribution center in Anbar Governorate, offering a solution that aligns with the company’s goals of improving distribution efficiency and overcoming existing logistical challenges. Full article

In recent years, frequent open biomass burning (OBB) activities such as agricultural residue burning and forest fires have led to severe air pollution and carbon emissions across South and Southeast Asia (SSEA). We selected this area as our study area and divided it into two sub-regions based on climate characteristics and geographical location: the South Asian Subcontinent (SEAS), which includes India, Laos, Thailand, Cambodia, etc., and Equatorial Asia (EQAS), which includes Indonesia, Malaysia, etc. However, existing methods—primarily emission inventories relying on burned area, fuel load, and emission factors—often lack accuracy and temporal resolution for capturing fire dynamics. Therefore, in this study, we employed high-resolution fire point data from China’s Feng Yun-4A (FY-4A) geostationary satellite and the Fire Radiative Power (FRP) method to construct a daily OBB emission inventory at a 5 km resolution in this region for 2020–2022. The results show that the average annual emissions of carbon (C), carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), non-methane organic gases (NMOGs), hydrogen (H₂), nitrogen oxide (NO_X), sulfur dioxide (SO₂), fine particulate matter (PM_2.5), total particulate matter (TPM), total particulate carbon (TPC), organic carbon (OC), black carbon (BC), ammonia (NH₃), nitric oxide (NO), nitrogen dioxide (NO₂), non-methane hydrocarbons (NMHCs), and particulate matter ≤ 10 μm (PM₁₀) are 178.39, 598.10, 33.11, 1.44, 4.77, 0.81, 1.02, 0.28, 3.47, 5.58, 2.29, 2.34, 0.24, 0.58, 0.43, 0.99, 1.87, and 3.84 Tg/a, respectively. Taking C emission as an example, 90% of SSEA’s emissions come from SEAS, especially concentrated in Laos and western Thailand. Due to the La Niña climate anomaly in 2021, emissions surged, while EQAS showed continuous annual growth at 16.7%. Forest and woodland fires were the dominant sources, accounting for over 85% of total emissions. Compared with datasets such as the Global Fire Emissions Database (GFED) and the Global Fire Assimilation System (GFAS), FY-4A showed stronger sensitivity and regional adaptability, especially in SEAS. This work provides a robust dataset for carbon source identification, air quality modeling, and regional pollution control strategies. Full article

In the past few decades, traditional methods for developing amorphous alloys, such as empirical trial-and-error approaches and density functional theory (DFT)-based calculations, have enabled researchers to explore numerous amorphous alloy systems and investigate their properties. However, these methods are increasingly unable to meet the demands of modern research due to their long development cycles and low efficiency. In contrast, machine learning (ML) has gained widespread adoption in the design, analysis, and property prediction of amorphous alloys due to its advantages of low experimental cost, powerful performance, and short development cycles. This review focuses on four key applications of ML in amorphous alloys: (1) prediction of amorphous alloy phases, (2) prediction of amorphous composite phases, (3) prediction of glass-forming ability (GFA), and (4) prediction of material properties. Finally, we outline future directions for ML in materials science, including the development of more sophisticated models, integration with high-throughput experimentation, and the creation of standardized data-sharing platforms. These insights provide potential research directions and frameworks for subsequent studies in this field. Full article

This study explores the enhancement of antimicrobial and biomedical properties in Fe-based bulk metallic glasses (BMGs) through the addition of Ag. Fe_55-xCr₂₀Mo₅P₁₃C₇Ag_x (x = 0, 1, 2, 3 at.%) master alloy ingots were synthesized by the induction melting technique and industrial-grade raw materials, the master alloy ingots were prepared as bulk metallic glasses (referred to as Ag0, Ag1, Ag2, and Ag3) by the water-cooled copper-mold suction casting technique, and their glass-forming ability, corrosion resistance, biocompatibility, and antimicrobial properties were systematically investigated. The results indicate that the glass forming ability (GFA) decreased with increasing Ag content, reducing the critical diameter for fully amorphous formation from 2.0 mm for Ag0 to 1.0 mm for Ag3. Electrochemical tests in Hank’s solution revealed the superior corrosion resistance of the Fe-based BMGs as compared with conventional 316 L stainless steel (316L SS) and Ti6Al4V alloy (TC4), with Ag3 demonstrating the lowest corrosion current density and the most stable passivation. Biocompatibility assessments, including fibroblast cell viability and adhesion tests, showed enhanced cellular activity and morphology on Fe-based BMG surfaces as compared with 316L SS and TC4, with minimal harmful ion release. Antimicrobial tests against E. coli and S. aureus revealed significantly improved performance with the Ag addition, achieving bacterial inhibition rates of up to 87.5% and 86.7%, respectively, attributed to Ag⁺-induced reactive oxygen species (ROS) production. With their excellent corrosion resistance, biocompatibility, and antimicrobial activity, the present Ag-containing Fe-based BMGs, particularly Ag3, are promising candidates for next-generation biomedical implants. Full article

Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonlinear relationship between the urban built environment and CEs from residential land. By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. The research results show that, first, the goodness of fit of the XGBoost model reached 91.9%, and its prediction accuracy was better than that of gradient boosting decision tree (GBDT), random forest (RF), the Adaboost model, and the traditional logistic model. Second, compared with other ML models, the XGBoost-SHAP model explained the influencing factors of CEs from residential land more clearly. The SHAP attribution analysis results indicate that BD, FAR, and Phh were the most important factors affecting CEs. Third, there was a significant nonlinear relationship and threshold effect between built environment characteristic variables and CEs from residential land. Fourth, there was an interaction between different dimensions of environmental factors, and BD, FAR, and Phh played a dominant role in the interaction. Reducing FAR is considered to be an effective CE reduction strategy. This research provides practical suggestions for urban planners on reducing CEs from residential land, which has important policy implications and practical significance. Full article

Colored proteins play an important role in synthetic biology research, providing a systematic labeling tool for visualizing microscopic biological activities in vivo. They can exhibit visible colors to the naked eye under natural light, and some of them are well-known fluorescent proteins. Here, several colored proteins were taken into consideration for acting as biocolorants in Escherichia coli, including green fluorescent proteins (eGFP and sfGFP), a red fluorescent protein (mKate2), and three chromoproteins (GfasPurple, AmilCP, and AeBlue). All of them can significantly change the colors of their bacterial colonies. The color of GfasPurple was much more stable after the heat treatments at 65 °C with 75% or 95% ethanol. In addition, several factors commonly occurring under natural conditions that lead to color dissolution, such as heat, ethanol, H₂O₂, vitamin C, acid, and alkali treatments, were further tested on GfasPurple. Visual observation and absorption spectroscopy analysis results showed an excellent tolerance of GfasPurple against these unfriendly conditions. GfasPurple could withstand temperatures of 65 °C for 2 h or 70 °C for 1 h in aqueous solutions, but it fades rapidly in 50% ethanol. The color of GfasPurple is more stable in 80% ethanol than in 50% ethanol, which could be attributed to its poor solubility in high-concentration ethanol. The visible light absorption curves of GfasPurple were basically not affected by physiological concentrations of vitamin C or H₂O₂, but reversible effects of high-concentration H₂O₂ were found. GfasPurple maintains its color within the pH range of 7–11; the chromophore of GfasPurple will suffer irreversible damage when pH is up to thirteen or as low as three. These results suggest that GfasPurple is an excellent biocolorant far beyond its application in prokaryotes. Furthermore, GfasPurple variants created via mutagenesis expanded the color library of chromoproteins, which have a potential value in the color manipulation of living organisms. Full article

Six degrees of freedom (6-DoF) object pose estimation is essential for robotic grasping and autonomous driving. While estimating pose from a single RGB image is highly desirable for real-world applications, it presents significant challenges. Many approaches incorporate supplementary information, such as depth data, to derive valuable geometric characteristics. However, the challenge of deep neural networks inadequately extracting features from object regions in RGB images remains. To overcome these limitations, we introduce the Geometry-Focused Attention Network (GFA-Net), a novel framework designed for more comprehensive feature extraction by analyzing critical geometric and textural object characteristics. GFA-Net leverages Point-wise Feature Attention (PFA) to capture subtle pose differences, guiding the network to localize object regions and identify point-wise discrepancies as pose shifts. In addition, a Geometry Feature Aggregation Module (GFAM) integrates multi-scale geometric feature maps to distill crucial geometric features. Then, the resulting dense 2D–3D correspondences are passed to a Perspective-n-Point (PnP) module for 6-DoF pose computation. Experimental results on the LINEMOD and Occlusion LINEMOD datasets indicate that our proposed method is highly competitive with state-of-the-art approaches, achieving 96.54% and 49.35% accuracy, respectively, utilizing the ADD-S metric with a 0.10d threshold. Full article

In jointless bridges, a grade flat approach slab (GFAS) with the same elevation as the pavement can transfer the girder’s longitudinal deformation to the backfill. However, any cracks and settlement of the pavement usually occur at the end of the GFAS. To address this shortcoming, the buried flat approach slab (BFAS) horizontally embedded at a depth in the backfill was proposed. The complicated flat approach slab–soil interaction (FASSI) of the BFAS has not been systemically investigated. To address this gap, the influence of the FASSI on the mechanical performance of the approach slab and the backfill deformation was investigated in this research to understand the mechanism of the FASSI in absorbing one part of the girder’s longitudinal deformation and transferring the rest to the soil. Experimental tests on the FASSI with different embedded depths under longitudinal displacements were conducted. Numerical parametric analyses were carried out by considering the embedded depths and slab lengths as the parameters based on a finite element model verified using the test results. The results show that load–displacement curves of the FASSI comprise three stages: the elastic stage (approach slab’s displacement was absorbed by sand), the elastoplastic stage (sand deformation was observed), and the failure stage (overall shear failure of the sand was found). The longitudinal displacement transfer mode and vertical deformation distribution mode of the sand were affected by the embedded depth and slab length. With an increase in the embedded depth or a decrease in the slab length, the sand deformation decreases, which is beneficial for avoiding pavement crack risks and improving the pavement evenness. Finally, a simplified calculation formula that can be used to predict the load–displacement curves of the FASSI was proposed. This research provides the theoretical basis for the design and construction of the flat approach slab in jointless bridges. Full article

The article mostly addresses the application of sustainable technologies in residential construction through life cycle cost analysis (LCC) using the net present value (NPV) calculation method. The authors rely on data obtained through their own research and information received from the market environment. The article outputs are in the form of conclusions based on a case study on a specific building (apartment building), elaborated in several versions with respect to the technologies used. In total, there are seven alternative versions divided into two groups, where a so-called reference technology representing a traditional (standard) technical solution is present in each group so that a relevant comparison can be made. The first group includes technologies related to heating and hot water, while the second group focuses on the application of recycled water (so-called grey water). The outputs obtained provide an interesting and fact-based view of sustainable technologies within the life cycle of a building drawing from currently available information sources. At the same time, the presented analysis has incorporated price predictions for key commodities, i.e., electricity, water, gas. The article’s specific conclusions indicate that the technologies utilizing renewable energy sources (RES) are typically less economically advantageous (in the absence of subsidy sources) compared to conventional (traditional) solutions, despite the significant savings in operating costs. The LCC indicator revealed a cost value per square meter of gross floor area (GFA) for a residential building ranging from EUR 43 to 68, contingent on the specific option under consideration. This cost value was determined over a 20-year follow-up period and a real discount rate of 4%. Full article

This study offers a thorough comparative analysis of space efficiency in skyscrapers across three distinct forms: prismatic, tapered, and free. By examining case studies from each form category, this research investigates how architectural and structural design features impact space utilization in supertall towers. The findings reveal form-based differences in space efficiency and design element usage. In prismatic skyscrapers, which are primarily residential and utilize concrete outrigger frames, the average space efficiency was around 72%, with the core occupying 24% of the gross floor area (GFA). Tapered skyscrapers, commonly mixed-use with composite outrigger frames, showed an average space efficiency of over 70%, with a core-to-GFA ratio of 26%. Freeform towers, often mixed-use and using composite outrigger frames, demonstrated a space efficiency of 71%, with an average core-to-GFA ratio of 26%. Despite these variations, a consistent trend emerged: as the height of a building increases, there is a general decline in space efficiency, highlighting the challenges in optimizing space in taller structures. This analysis adds to the understanding of skyscraper design and space utilization, providing important insights for architects and urban planners aiming to improve the efficiency of future high-rise developments. Full article