Extensive research has investigated the use of fly ash (FA) in self‐compacting concrete (SCC); however, the available literature remains fragmented, with most studies focusing on individual performance aspects rather than providing an integrated assessment of fresh‐state behavior, mechanical performance, durability, microstructural evolution, and environmental sustainability. This review provides a comprehensive critical synthesis of the current knowledge on the role of FA in SCC, highlighting the governing mechanisms, identifying existing research gaps, and outlining future research priorities. The reviewed literature demonstrates that FA significantly influences the fresh properties, mechanical performance, durability, and sustainability of SCC. Moderate FA replacement levels (approximately 20%–30%) generally provide the optimum balance between workability, strength development, durability, and environmental performance. In contrast, higher replacement levels may delay early‐age strength development and increase carbonation susceptibility unless appropriate mixture optimization and curing strategies are adopted. The improvements in fresh and hardened properties are primarily attributed to the spherical morphology of FA particles, enhanced particle packing, and secondary pozzolanic reactions that promote calcium silicate hydrate (C–S–H) formation and pore refinement. Durability assessments, including rapid chloride penetration and chloride migration tests, further demonstrate improved resistance to chloride ingress, sulfate attack, and shrinkage. In addition, life cycle assessment (LCA) indicates that partial replacement of Portland cement with FA substantially reduces global warming potential (GWP) and CO 2 emissions, supporting the development of low‐carbon SCC. Despite these advantages, important challenges remain regarding standardized mix design, variability in FA characteristics, early‐age strength development, carbonation resistance, and the integration of artificial intelligence (AI) with performance‐based mixture optimization. Overall, this review identifies key knowledge gaps and future research directions to facilitate the broader application of durable, low‐carbon, and sustainable FA‐based SCC in modern construction.
The applications of aluminium metal matrix composites (AMMCs) using Al7075 as the base alloy are increasing in the automobile and aerospace industries due to their superior strength‐to‐weight ratio. Al7075, being a heat‐treatable alloy, offers scope for enhancing mechanical properties through suitable reinforcement and heat treatment. In this study, hybrid AMMCs were fabricated using Al7075 alloy reinforced with silicon carbide (SiC) and aluminium oxide (Al 2 O 3 ) particles through the stir casting process. The stir casting process involves melting preheated Al7075 alloy at 650°C and further heating it to 780°C for complete liquefaction. Preheated reinforcements (650°C) are then added and stirred thoroughly for uniform blending. Subsequently, 2 g of magnesium powder is introduced into the slurry to improve wettability. After solidification, the material is removed from the mould to produce samples using a wire‐cut electrodischarge machine. SiC was added in varying proportions of 3, 5, 7 and 9 wt %, while Al 2 O 3 was maintained at a constant 2 wt % for all samples. The T6 heat treatment of the samples involved solutionizing at 530°C, holding time for 8 h, ageing at 165°C for 8 h and cooling by water quenching. Mechanical and metallurgical properties were examined for both as‐cast and heat‐treated (T6) composites. The results revealed that the inclusion of SiC and Al 2 O 3 significantly improved the tensile strength and hardness of the composites compared to the base alloy. The heat‐treated specimens exhibited superior performance compared to as‐cast samples, achieving a maximum tensile strength of 553.1 MPa (70% increment) and hardness of 204.5 VHN (71.8% increment), compared to 325 MPa and 119 VHN for the as‐cast samples. SEM micrographs confirmed uniform dispersion of reinforcements and refined grain structures in heat‐treated composites. The strengthening mechanisms in metal matrix composites mainly include load transfer, Hall–Petch strengthening and Orowan strengthening. Strengthening occurs by load transfer from the ductile matrix to the hard reinforcement particles, Hall–Petch strengthening by an increase in the number of grain boundaries to obstruct dislocations and Orowan strengthening, in which dislocations form loops around reinforcements, obstructing dislocation movement. The developed hybrid AMMCs demonstrate excellent potential for structural applications in the aerospace and automotive sectors.
Digital prosthodontic workflows are often advocated as environmentally friendly alternatives to traditional prostheses, although laboratory-process-impact analyses are not universally supported by empirical evidence. This systematic qualitative review critically synthesized existing life cycle–related evidence concerning prosthodontic manufacturing workflows in accordance with PRISMA 2020 guidelines. Review is not registered prospectively on PROSPERO since the review is synthesis of laboratory-based data related to environment and technology but not clinical patients’ outcome, and there was no external funding for the review. PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar were searched using specific search terms for LCA, environmental impact, additive manufacturing, and prosthodontic materials. The inclusion criteria were fulfilled by 20 original research studies published from 2000 to 2025. Methodological quality and risk of bias were assessed independently by two authors using a modified appraisal instrument derived from ISO 14040 and JBI criteria. Given the heterogeneity of functional units, a qualitative synthesis with comparison in tabular format was undertaken. In addition to material waste of more than 60% of the blank volume, subtractive manufacturing, especially zirconia milling process, showed consistently the highest energy demand because of long mill time and high-temperature sintering processes over 1400°C. Conversely, additive manufacturing showed material utilization ratios of over 90%, but added to that, it added on environmental burdens of using chemicals for postprocessing, the use of solvents, and possible ecotoxicological risks from uncured resin waste. The results indicate that sustainability of a prosthodontic manufacturing requires balancing between the embodied energy, material efficiency, clinical success, and end-of-life management, not just digitalization. The review additionally emphasizes the necessity for frameworks and universal reporting procedures for the assessment of environments across the prosthodontics life cycle. Meta-analysis was, however, limited by the lack of uniformity across studies in terms of functional units and system boundaries.
Despite recent advances, the development of multifunctional scaffolds remains a major challenge. Therefore, this study aimed to fabricate biomimetic scaffolds composed of hydroxyapatite (HA), carbon nanotubes (CNTs), collagen (COL), chitosan (CS), and silver nanoparticles (AgNPs) and to evaluate their applicability in bone tissue engineering. The incorporation of low concentrations of functional multiwalled carbon nanotubes (MWCNTs) resulted in decreased porosity, swelling capacity, and biodegradation rate; however, all scaffolds maintained porosity levels exceeding 75%, which is suitable for bone tissue regeneration. Increasing the MWCNTs content significantly enhanced the mechanical performance of the scaffolds. The ATR spectrum confirmed the successful incorporation of all scaffold components through characteristic phosphate, carbonate, amide, hydroxyl, carboxyl, and Ag–O vibrational bands. The optimized scaffold with 20-mL MWCNTs from a 0.2% (w/v) suspension exhibited an average pore size of 223.33 μm, within the range favorable for osteoblast proliferation and bone growth. Scaffolds exhibited RBC biocompatibility and were noncytotoxic to Vero cells. In addition, the synergistic combination of MWCNTs and AgNPs imparted effective antibacterial activity against bacteria commonly associated with bone infections. Moreover, the drug loading percentage was found to be 65.87 ± 3.54%, and a sustained release of Ceftriaxone was observed over a period of 16 days with a cumulative percentage of 61.46 ± 2.28%. These findings suggest that the MWCNT/AgNP-reinforced HA–COL–CS scaffold possesses an array of desirable properties, offering a versatile, clinically relevant platform for the treatment of complex bone defects and associated infections through bone tissue engineering.
The technical changes brought about by the 4th Industrial Revolution have led to a substantial rise in the use of artificial intelligence (AI) and machine learning (ML) in the field of advanced materials engineering. Fiber-reinforced polymers (FRPs) have become a desirable class of materials for use in structural applications in the aerospace, automotive, marine, civil, and energy industries because of their light weight, high strength-to-weight ratio, corrosion resistance, and sustainability. Yet it is still a major challenge to establish accurate process–structure–property (PSP) relationships for FRPs, since there are complex interactions between the processing conditions, the development of the microstructure, the properties of the reinforcement, and the environmental factors, and the traditional methods of experimental and numerical characterization or optimization make this problem worse by being costly, time-consuming, and computationally demanding. This study looks at the use of ML models both on their own and in combination with finite-element analysis, materials informatics, and digital manufacturing systems in order to create intelligent solutions for composite design, including those based on natural fibers, glass fibers, carbon fibers, and aramid fibers. For each type of fiber, the paper provides examples of ML applications together with quantitative performance standards, with a focus on predicting the mechanical, thermal, tribological, and acoustic properties. The ML models examined as a rule attain a high level of predictive accuracy, the reported R2 values being above 0.90 and in some cases getting as high as 0.99; ensemble methods and neurofuzzy approaches especially adaptive neurofuzzy inference systems (ANFISs) work particularly well with small datasets that have experimental variability, with reported R2 values reaching up to 0.9997. The study also points out the ongoing difficulties that are hindering wider acceptance, such as limited datasets, heterogeneous data, inadequate model interpretability, and the lack of a standard database for FRP characterization. All of these findings show the potential of ML-supported methods for speeding up materials design, for optimizing manufacturing processes, and for predicting the performance of advanced FRP composites, while they also highlight emerging areas such as explainable AI, physics-informed ML, and digital twin technology as the forefront of future composite engineering.
