Search Results (4,282)

This work investigates Ferro-Rock concrete as a carbon-negative alternative to ordinary Portland cement (OPC), which accounts for 5–9% of global CO₂ emissions, and evaluates its viability as a sustainable construction material. Ferro-Rock is an iron-based binder comprising recycled iron powder, fly ash, metakaolin, limestone powder, and oxalic acid. This is enhanced by a carbonation reaction in which iron particles react with CO₂ and water to form iron (II) carbonate (FeCO₃), the main binding phase, thereby locking in atmospheric CO₂. The experimental program was divided into two groups. Group 1 studied 100% Ferro-Rock binders with different types of aggregate, specimen sizes, and CO₂ curing periods (0–6 days) with a new locally manufactured stainless steel curing chamber that provided a controlled CO₂ environment of 99.9% and 1.2–1.5 bar gauge pressure. Group 2 investigated Ferro-Rock as a partial cement replacement at 0%, 5%, 10%, 15% and 20% levels of substitution with 5% increments. The 7 and 28 days of compressive, flexural and indirect tensile strengths were determined. The results showed the Ferro-Rock with 100% iron ductile waste aggregates (Mix F4) achieved a 28-day compressive strength of 5.5 MPa, 37.5% higher than the standard Ferro-Rock reference mix. The optimum replacement range of Group 2 was 5–10% with an increase in compressive strength by 5–10%, flexural strength by 11%, and indirect tensile strength by 16% over the OPC control. When replacement exceeded 25%, the bonding was weakened, and all strength measures decreased significantly, reaching a 46% reduction in compressive strength at 50% substitution. Scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM–EDS) microstructural analysis verified the gradual formation of the iron carbonate crystalline phase and provided mechanistic insights into the observed strength trends. Fully cured Ferro-Rock specimens sequestered as much as 11% CO₂ by weight, with a verifiably carbon-negative profile that no OPC-based system can match. Full article

Using industrial by-product lithium slag (LS) as a raw material for ultra-high performance concrete (UHPC) is an important way to achieve low-carbon prefabricated structures. However, existing studies lack a constitutive model for LS-UHPC and its application in prefabricated beam connection nodes. To fill this gap, this paper first established a tensile-compressive constitutive model for LS-UHPC through mechanical tests; then it was embedded into the finite element model to simulate the bending performance of the connection nodes of the post-cast LS-UHPC prefabricated beams and verified by the test results. Finally, parameter analysis is carried out. The results show that moderately increasing the diameter of longitudinal reinforcement can significantly improve the flexural bearing capacity of the connection node, but when the diameter exceeds 18 mm and HRB500 high-strength steel bars are used, the node exhibits over-reinforced failure characteristics; increasing the strength grade of ordinary concrete has a limited effect on the improvement of flexural bearing capacity (<5%). This study clarified the mechanical constitutive relationship of LS-UHPC, revealed the failure mechanism and bearing capacity evolution law of its prefabricated connection nodes under parameter changes, and provided a theoretical basis and design suggestions for the application of low-carbon lithium slag UHPC in prefabricated assembly structures. Full article

Accurate cladding segmentation is essential for quantitative quality assessment of stainless-steel/carbon-steel clad wire rods used in bridge cables, yet remains challenging because of weak core–cladding contrast, narrow interfacial transition zones, local cladding-thickness fluctuations, and limited repeatability of manual inspection. This study proposes an improved U-Net framework that integrates residual feature extraction, multi-scale contextual perception, and attention-guided feature refinement for robust cladding identification. A cross-sectional image dataset comprising 18,566 samples was constructed through standardized specimen preparation, chemical color development, image acquisition, pixel-level annotation, and data augmentation. In the proposed model, the original U-Net encoder is replaced with ResNet50 to enhance deep semantic representation, while atrous spatial pyramid pooling and a convolutional block attention module are embedded into the feature-fusion stage to improve boundary discrimination and thin-cladding recognition. On the test set, the model achieved a mean pixel accuracy of 97.29%, cladding intersection over union of 88.82%, and mean intersection over union of 93.72%, outperforming the baseline U-Net by 1.38, 9.19, and 5.17 percentage points, respectively. Ablation and comparative experiments further demonstrate improved boundary continuity, local-detail preservation, and segmentation stability compared with representative CNN-based segmentation models. These findings suggest that the proposed framework provides a practical and reliable vision-based approach for cladding-thickness measurement, eccentricity evaluation, uniformity assessment, and batch quality inspection of clad wire rods. Full article

This paper presents research on reinforced concrete beams strengthened with non-pretensioned and pretensioned near-surface-mounted (NSM) carbon fibre-reinforced polymer (CFRP) strips under self-weight and external preloading. The first part of this paper briefly describes and discusses the results of experimental tests performed on six beams with different reinforcing steel ratios, preloading levels, and strengthening-system configurations. Next, three-dimensional (3D) numerical models of the tested specimens were developed. The models consider the nonlinear behavior of concrete (both in tension and compression), steel bars, and the interface between concrete and CFRP laminates. For these models, the material parameters were established based on experiments and recommendations from the literature. Furthermore, a sensitivity analysis was conducted with respect to the material parameters of the model that were not directly obtained from experimental measurements. The analyses validated the applicability of the numerical model in predicting the flexural behavior of reinforced concrete (RC) members strengthened with near-surface-mounted (NSM) CFRP materials over the full loading range. Furthermore, the developed models were employed to assess the effectiveness of active strengthening relative to passive strengthening methods (i.e., without pretensioning of the laminate). A comparison study of actively and passively strengthened elements indicates that prestressing does not affect the ultimate limit state but enhances serviceability limit states. The presented computational model, together with the adopted computational strategy, demonstrates its effectiveness for analyzing realistic scenarios involving RC beams that are damaged and subjected to loading during the strengthening process. Full article

Ultra-high-performance concrete (UHPC) formulated with alternative binders represents a promising pathway for reducing carbon emissions while enabling multifunctional material performance. This study investigates the mechanical and electrical evolution of two systems: a traditional Portland cement-based UHPC (REF) and a geopolymer counterpart (GEO) where cement is fully replaced by ground granulated blast furnace slag (GGBS) and silica fume. By evaluating both mixes with and without steel fibers, the research assesses how binder chemistry interacts with conductive pathways to influence strength, resistivity, and impedance. Mechanical testing revealed comparable 28-day compressive strengths for the reference and geopolymer mixes (123 MPa and 120 MPa, respectively), which increased to 139 MPa and 130 MPa upon fiber incorporation. Electrical characterization showed that the geopolymer binder significantly enhances conductivity; resistivity values dropped from 9645 Ω·m in the reference mix to 925 Ω·m in the geopolymer and further to 76 Ω·m with fiber reinforcement. Impedance spectroscopy supported these results, as the GEO mixes displayed smaller Nyquist arcs compared to the REF system, indicating greater ionic mobility associated with pore solution chemistry and the GGBS-rich gel structure. Ultimately, this study demonstrates that geopolymer UHPC matches the mechanical integrity of Portland-based systems while offering superior electrical conductivity, making it a strong candidate for low-carbon, self-sensing infrastructure. Full article

The iron and steel industry is one of the most energy- and emission-intensive industrial sectors, accounting for approximately 95% of global metal production and 7–9% of global CO₂ emissions. Its decarbonization is therefore central to climate change mitigation and has potential co-benefits for environmental quality and human health through reductions in air pollutants associated with conventional coal-based steelmaking. This review addresses the following question: which technological and systemic pathways can reduce emissions from iron and steel production, and what constraints limit their deployment across regions? The article synthesizes current knowledge on the dominant blast furnace–basic oxygen furnace and electric arc furnace routes, their emission intensities, and their role in global steel production. It then evaluates two complementary groups of decarbonization pathways: optimization of existing carbon-intensive processes and the transition to low- and near-zero-carbon technologies, including hydrogen-based direct reduction, electrification, carbon capture, utilization and storage. Particular attention is given to the dependence of these pathways on low-carbon electricity, hydrogen availability, scrap supply, infrastructure, policy frameworks, and regional economic conditions. The review highlights that technological readiness alone is insufficient to ensure deep decarbonization; implementation depends on the alignment of energy systems, industrial investment cycles, and climate policy. From a public health perspective, steel decarbonization should be understood as a climate mitigation measure with potential health co-benefits, particularly where it reduces both greenhouse gas emissions and local air pollution. Full article

Silver-doped diamond-like carbon (Ag–DLC) coatings were investigated with respect to their tribological behavior under ambient and physiologically relevant conditions. Gradient Ag–DLC coatings deposited on AISI 316L stainless steel were tested in air, simulated body fluid (SBF), and an albumin-containing solution using a pin-on-disk configuration. Increasing silver content resulted in a systematic reduction in the H³/E² ratio, leading to increased coating wear irrespective of the environment. In contrast, friction behavior was strongly controlled by the surrounding medium. Under dry sliding in air, all coatings exhibited similar steady-state friction governed by the DLC matrix. The lowest steady-state friction coefficients were obtained in SBF, indicating that the aqueous ionic environment provided the most favorable friction conditions among the tested media. In the albumin-containing medium, friction also remained low, indicating that protein adsorption and interfacial layer formation modified the sliding conditions, although the CoF did not fall below that observed in SBF. Wear was highest in air and generally lowest in SBF, while tests in albumin promoted surface layer formation. Surface analyses indicated silver redistribution, transfer-layer formation, and the presence of protein-related surface agglomerates, with higher apparent surface coverage on coatings containing more Ag. Overall, the results show that Ag-doped DLC coatings exhibit environment-dependent tribological behavior under physiologically relevant conditions. The present work should be regarded as a tribological study rather than a direct validation of antibacterial performance. Future studies should combine tribological assessment with dedicated antibacterial and cytocompatibility experiments. Full article

Reported herein is tantalum (Ta)-based film, including TaN, TaO_x, composite TaO_xN_γ, multilayered TaN/TaO_x-(5:5) and TaN/TaO_x-(10:10), prepared by atomic layer deposition (ALD) technology via adjusting the sub-cycle of TaN and TaO_x films. The influence of different growth parameters on microstructure, crystal form, chemical bonding state and corrosion resistance of Ta-based films was systematically investigated. Representative results include the following: (1) The surface of the Ta-based films prepared by ALD is continuous, dense and smooth, and the root mean square roughness (Rq) of those are TaN: 0.74 nm, TaO_x: 0.69 nm, TaO_xN_γ: 0.55 nm, TaN/TaO_x-5:5: 0.56 nm and TaN/TaO_x-10:10: 0.77 nm. (2) The TaN film presents a polycrystalline structure with good crystallinity, while the incorporation of oxygen significantly inhibits the crystallinity of the film. (3) Electrochemical tests in 3.5 wt.% NaCl solution and neutral salt spray experiments show that ALD deposition of Ta-based films can significantly improve the corrosion resistance of carbon steel substrates. The order of corrosion resistance of different films is TaO_xN_γ film > TaN/TaO_x multilayer film > TaN film. Among them, the TaO_xN_γ film exhibited the most excellent corrosion resistance, with a charge transfer resistance (R_ct) as high as 24.75 Ω·cm² and a corrosion current density (I_corr) as low as 1.20 × 10⁻⁶ A/cm², and no obvious rusting phenomenon was observed on the surface of that film after the 2 h neutral salt spray test. Full article

Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon steel workpieces (JIS S50C/AISI SAE 1050-equivalent; as-received and non-heat-treated; nominal laboratory hardness approximately 4.3 HRC). Experiments were performed on a Tongtai MDV-508 vertical machining center at fixed cutting conditions (3000 rpm spindle speed, 2 mm axial depth of cut, 5 mm cutting width, and 300 mm/min feed rate) using eight TiAlN-coated fine-grain WC–Co solid carbide end mills (10 mm diameter, four flutes; nominal Co binder approximately 10 wt%). An oil-based HS Highstart/HS-SSHS-BH10 cutting fluid was applied through the machine external coolant nozzle in flood mode at an estimated nominal flow rate of approximately 3 L/min and near-room coolant temperature (25 ± 2 °C), and was used as supplied without dilution. A clamp-type AC current sensor was installed on one phase line supplying the spindle motor, and current was acquired using an NI-9221 module at 20 kHz. Cutting intervals were isolated by envelope-based segmentation, concatenated, and divided into 1 s windows (0.5 s overlap) for feature extraction. Three feature sets were evaluated: time-domain statistics, frequency-domain statistics, and an FFT→PCA hybrid representation. Tool states (New, Mid-life, Old) were labeled using post-process surface roughness Ra thresholds supported by microscope observation. The PCA transformation was fitted only on training data and then applied to the held-out test data. A logistic regression classifier achieved 97.44% test accuracy (152/156 windows; 95% Wilson CI: 93.59–99.00%) with the PCA-hybrid features, outperforming time-domain (89.74%) and frequency-domain (94.87%) models. The results support spindle current monitoring as a low-cost approach for quality-aligned tool condition monitoring, while the external validity remains limited to the tested machine, material, tool, coolant, and cutting-parameter combination. Full article

The work investigated the formation mechanisms, structure, and properties of the modified layer obtained during electrolytic-plasma nitroboriding of Steel 20 in the temperature range of 650–850 °C. A comprehensive approach, including numerical modeling and experimental methods, was applied to analyze diffusion processes, phase formation, and performance characteristics. A one-dimensional diffusion model was developed, taking into account the coupled transport of boron, nitrogen, and carbon, as well as the movement of phase boundaries. It was shown that a gradient layer is formed, characterized by boron enrichment in the near-surface zone, deeper nitrogen penetration, and carbon redistribution. The calculated layer thickness at 850 °C (~70–75 μm) is in good agreement with the SEM data (~73.4 μm, taking into account the transition zone). SEM and EDS analysis confirmed the formation of a multilayer structure with a pronounced transition region. A significant increase in microhardness up to ~950–1000 HV at 850 °C was established, with a gradual decrease to the matrix level (~200–250 HV) at a depth of 70–90 μm. Tribological tests showed a decrease in the coefficient of friction and an increase in wear resistance, with the best characteristics achieved at 850 °C. Full article

Chloride and sulfate ion attacks are among the leading causes of deterioration in reinforced concrete structures, leading to the corrosion of steel reinforcement, expansion, cracking, and premature structural failure. Early detection of these ion-induced deteriorations is essential not only for maintaining safety but also for supporting sustainability objectives by extending service life, reducing material consumption, and minimizing carbon-intensive repairs. This review synthesizes current advances in non-destructive testing (NDT) techniques used to identify and quantify the impacts of chloride and sulfate ions in reinforced concrete. The mechanisms of ion ingress and their associated degradation processes are examined together with the operating principles, strengths, and limitations of key NDT methods, including electrical resistivity, acoustic emission, infrared thermography, ground penetrating radar, and ultrasonic pulse velocity. By enabling timely maintenance decisions and reducing unnecessary demolition or intrusive testing, these NDT methods contribute directly to sustainable infrastructure management. Through comparative analysis and real-world case studies, the paper highlights the most effective NDT applications for deterioration scenarios and outlines emerging innovations that enhance accuracy, data interpretation, and long-term monitoring capabilities. The findings demonstrate how advancements in NDT support the development and preservation of durable and sustainable concrete structures. Full article

This study investigates spherical methyl silicone resin, a potentially environmentally friendly additive free of sulfur, phosphorus, and chlorine, as a lubricant additive in polyethylene glycol 200 (PEG 200) base oils. We evaluated concentration-response characteristics and tribological performance across PEG base oils containing 0.01–0.05 wt% resin. Tribological testing was conducted with a four-ball wear tester at 98 N and 1450 rpm for 30 min. All tested concentrations demonstrated excellent friction-reduction and anti-wear performance, with an optimal efficacy observed at 0.02 wt%. Surface characterization was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. This friction-reducing and anti-wear performance is attributed to the formation of silicon-oxygen species and graphene-like carbon structures, thereby effectively suppressing direct surface contact and mitigating wear. Consequently, spherical methyl silicone resin demonstrates considerable potential as a green lubricant additive for bearing steel applications. Full article

Improving energy efficiency of electric arc furnace (EAF) steelmaking is a key pathway for the iron and steel industry to achieve carbon neutrality. Based on statistical data from 56 industrial EAFs, this study established and validated a comprehensive mass and energy balance model with a verification error of less than 5% and systematically quantified the effects of furnace type, furnace capacity, hot metal charging ratio, and scrap preheating on EAF energy efficiency through statistical analysis and scenario simulation. The results show that furnace type is the decisive factor for energy efficiency; Consteel and shaft furnace EAFs with scrap preheating are significantly more efficient than conventional EAFs, with the shaft furnace exhibiting the highest preheating efficiency and best stability. The scale effect of furnace capacity on energy efficiency is weak and fully overshadowed by furnace type. Each 10% increase in hot metal ratio reduces specific power consumption by about 50 kWh/t in conventional furnaces, and the optimal hot metal ratio is 40–50% to balance power consumption and total energy consumption. Scrap preheating saves electricity by recovering physical heat, with each 100 °C temperature increase reducing power consumption by 25 kWh/t; compared with the Consteel process, the shaft furnace process reduces total energy consumption by approximately 14% and increases energy efficiency by 9%. This study provides theoretical support and practical guidance for process optimization in the low-carbon transformation of EAF short-flow steelmaking. Full article

The reliable evaluation of the interfacial bonding quality of steel structures strengthened with carbon fiber-reinforced polymer (CFRP) is crucial to ensuring the long-term service safety of the structures. Focusing on the active and passive detection methods based on piezoelectric sensing, this paper takes numerical simulation as the core research method to provide theoretical verification and mechanism explanation for subsequent key experiments, thus supporting the accurate detection of interfacial damage in CFRP–steel plate joints. A 3D piezoelectric–structural coupling finite element model and a 2D ultrasonic guided wave propagation finite element model were established via COMSOL Multiphysics 6.2 to systematically simulate the electromechanical response characteristics of three piezoelectric sensors (PMN-PT, PZT and PVDF). The research focused on analyzing the potential output and voltage–load response of the three sensors, and simultaneously explored the propagation laws and energy evolution mechanisms of ultrasonic waves in the presence of different debonding damages and groove defects in CFRP plates. The simulation results show that the PMN-PT sensor exhibits the optimal detection performance, with its peak potential output reaching 2.66 times that of the PZT sensor and 4.69 times that of the PVDF sensor, with a load sensitivity of 484.3 mV/kN. In the ultrasonic active detection of interfacial debonding damage, the first-wave amplitude has a significant positive correlation with the debonding length, and this characteristic is attributed to the strong reflection effect and energy accumulation caused by the acoustic impedance mismatch at the CFRP–air interface. For the internal groove defects in CFRP plates, the simulation clarifies that the increase in groove length leads to energy trapping in the plate, while the increase in groove depth intensifies ultrasonic wave energy reflection. The numerical simulation results were compared and verified with data from companion experiments conducted by the authors’ team, showing a high degree of consistency, which confirms the accuracy and reliability of the established finite element models. Meanwhile, the physical essence of damage detection is elucidated from the perspective of wave theory, providing a solid numerical analysis foundation and theoretical support for the intelligent monitoring of interfacial damage in CFRP–steel structures. Full article

Emission factor matching is the most critical step in product carbon footprint (PCF) accounting based on life cycle assessment (LCA). However, this step has long been hindered by several major challenges: a lack of standardization, overreliance on expert judgment, inconsistencies in raw data, and complex processing workflows. To address these issues, this study proposes an automated emission factor matching algorithm that combines large language models (LLMs) with semantic retrieval. The algorithm proceeds in two stages: first, an LLM identifies the reference product within the LCA database; then, an embedding model retrieves the most relevant emission factors through high-precision matching. Depending on practical requirements, the algorithm can either automatically select a single best-match factor or rank multiple best-match candidates in descending order of match precision to assist LCA experts in decision-making. We evaluate the algorithm on eight industrial products—tires, cement, ammonium phosphate, wood products, textiles, electronics and electrical appliances, steel, and lithium batteries—using the Ecoinvent 3.10 LCA database. Results demonstrate that the algorithm achieves high precision and low processing latency, significantly outperforming manual expert screening. These findings confirm that the proposed algorithm enables efficient and accurate emission factor matching, thereby providing a reliable technical solution and decision-making pathway for large-scale, automated PCF accounting. Full article