Min Ook Kim

Offshore renewable energy (ORE) systems experience interacting chemical, biological, and mechanical stresses that make coating performance dependent on component duty and maintenance access. This critical narrative review evaluates conventional and advanced coatings across atmospheric, splash, tidal, and submerged zones, distinguishing laboratory demonstrations from field evidence. Coupled degradation is analyzed in relation to coating architecture: strain-assisted ingress and delamination in organic multilayers; cathodic disbondment in immersed barrier systems; sacrificial consumption in metallic and duplex systems; and wear-induced loss of support in ceramic-rich layers. Surface preparation, layer dimensions, filler dispersion, and wet interfacial properties govern these pathways. Component-specific multi-stressor protocols use matched controls to distinguish interaction from coincident damage. An explicitly illustrative worked example links assumed defect-area histories to a remaining protective life of 2.64 years, repair area, discounted intervention cost, and climate impact. Growth-rate and access-delay scenarios show why these outputs require site calibration and cannot be treated as validated offshore predictions. Qualified multilayer organic and metallic–organic systems remain practical reference options, while nanofillers, self-healing, and sensing functions require demonstrated benefits at matched thickness, cure, and damage state.

Polymeric composites have become indispensable engineering materials owing to their exceptional mechanical performance, lightweight characteristics, corrosion resistance, and design flexibility. Conventional computational approaches, including density functional theory (DFT), molecular dynamics (MD), finite element analysis (FEA), and machine learning (ML), have substantially improved the understanding of polymer structure–property relationships. However, the increasing complexity of multiscale polymeric systems, intricate fiber–matrix interactions, and the computational cost associated with accurately modelling quantum-mechanical phenomena continue to present significant challenges for classical computing. This review provides a comprehensive assessment of the emerging role of quantum computing in polymeric composite research. It first introduces the fundamental principles of quantum computing, including qubits, quantum gates, quantum circuits, representative quantum algorithms, and contemporary hardware platforms. Subsequently, the review examines conventional computational methods used for modelling polymeric composites and discusses how quantum computing may complement and accelerate these established approaches. Recent and prospective applications in polymer electronic structure calculations, fiber–matrix interface engineering, nanocomposite design, multiscale modelling, and intelligent materials discovery are critically evaluated. Particular emphasis is placed on hybrid quantum–classical frameworks that integrate quantum computing with artificial intelligence, materials informatics, and high-throughput computational workflows as candidate tools for polymer design. The evidence is separated into polymer-specific hardware demonstrations, simulator-based studies, transferable non-polymer benchmarks, and prospective applications. Existing studies establish feasibility for selected tasks; they do not establish general end-to-end quantum advantage for realistic polymeric composites.

Oxidative stress contributes to disease progression, food deterioration, agricultural losses, and material degradation, whereas many conventional antioxidants are limited by poor stability, low bioavailability, rapid consumption, or inadequate delivery. In this review, nanoantioxidants are defined as nanoscale systems intentionally designed to suppress or regulate damaging oxidative processes, and four functional categories are distinguished: intrinsically antioxidant nanomaterials, antioxidant nanozymes, antioxidant-functionalized nanoparticles, and nanocarriers used to deliver conventional antioxidants. The review systematically examines how particle size and surface area, morphology and exposed facets, oxidation state and defect density, surface chemistry and charge, aggregation, dissolution or ion release, dose, and the surrounding environment determine antioxidant activity. Particular emphasis is placed on the context-dependent transition from antioxidant to pro-oxidant behavior, the distinction between enzyme-like nanozyme activity and true enzymatic activity, and the need for nanoparticle-specific controls and complementary assays to avoid analytical interference. Evidence from chemical assays, cell culture, animal studies, and clinical research is considered separately because activity in a simplified chemical system does not by itself establish therapeutic efficacy or safety. Applications in nanomedicine, food preservation, agriculture, environmental protection, and engineering materials are critically compared. Long-term toxicity, biodistribution, clearance, degradation, environmental fate, and life-cycle impacts are highlighted as essential translational criteria; likewise, green synthesis is not treated as synonymous with sustainability without consideration of energy, solvents, purification, scalability, waste, and end-of-life behavior. Future progress will require standardized datasets and experimental protocols, mechanism-guided design, realistic validation, and carefully governed artificial-intelligence-assisted optimization.

Two-component grouts backfill the annular tail void behind shield tunnel-boring-machine (TBM) linings, where they must gel within seconds and limit groundwater re-infiltration. In urban metro and railway tunnels this annulus is also a geotechnical element: it transfers ground load to the lining and forms a low-permeability shell whose conductivity relative to the host soil conditions the seepage regime. This study examines CO₂ wet pre-carbonation of coal combustion ash (CCA) as a pretreatment for such grouts. Six formulations — 100% ordinary Portland cement (OPC), 50% CCA replacement, and 50% CO₂-pretreated CCA — were cast under quick-set and slow-set regimes. The CCA behaved as a mixed hydraulic–pozzolanic material, and pre-carbonation fixed 2.34 wt.% carbonate after 30 min of CO₂ exposure at 400 mL/min. Among the quick-set mixtures the pretreated grout was the least permeable at 7 and 28 days (6.49 × 10⁻⁷ and 1.47 × 10⁻⁶ cm/s) yet developed almost no strength. Under slow setting, pre-carbonation shortened the gel time by 28.4% and the 28-day strengths of treated and untreated CCA converged near 1.1 MPa, although the permeability advantage did not persist. SEM-EDS, XRD, TGA, and FT-IR attributed the transport response to localized carbonate formation rather than uniform densification. The hardened grouts spanned 2.59 × 10⁻⁷ to 7.67 × 10⁻⁶ cm/s, the silt band of natural soils and one to four orders of magnitude below the sands typical of urban shield drives; the pretreatment is best positioned as a groundwater-control measure for a non-structural annulus rather than a strength-giving one.

Mineral carbonation offers a pathway for permanent CO₂ storage in construction materials while, in many systems, improving product performance and enabling the reuse of cementitious wastes. However, laboratory-scale carbonation efficiencies often decline during pilot- and industrial-scale implementation because conversion is governed not only by reaction chemistry but also by spatially heterogeneous transport conditions. Local velocity, CO₂ concentration, residence time, temperature, moisture, and phase distribution collectively determine the reaction environment experienced by each specimen, particle, or slurry region. This review develops a unified computational fluid dynamics (CFD) framework linking material carbonation with reactor hydrodynamics, reactive transport, model selection, validation, and scale-up. Four representative processes—accelerated carbonation curing, CO₂ injection during mixing, recycled-concrete-aggregate carbonation, and aqueous mineral carbonation—are interpreted as humid gas–porous solid, gas–liquid–solid non-Newtonian suspension, packed-bed, and gas–liquid–solid slurry systems, respectively. A Reynolds–Damköhler framework, extended through a local Damköhler number, Da(x), is proposed to characterize spatial variations in transport–reaction competition. Three coupled mechanisms are identified as essential for predictive modelling: reaction-induced CO₂ depletion, two-way moisture exchange between gas and cementitious phases, and thermal feedback on reaction kinetics and CO₂ solubility. Process-specific modelling strategies, coupling requirements, and validation criteria are then synthesized. The resulting framework enables CFD-informed reactor design, diagnosis of flow-induced non-uniformity, and more reliable scale-up by linking hydrodynamic performance to material conversion, product uniformity, energy demand, and process-level carbon efficiency.

We report the first use of CO₂ wet pre-carbonation of coal combustion ash (CCA) as raw material activation technique for two-part rapid-set waterproof injection grouts. Fresh-state behavior, compressive strength, hydraulic conductivity, and microstructural evolution of six formulations composed of three binder systems (100% OPC, 50% CCA replacement, and 50% CO₂-pretreated CCA) were assessed under both the quick-set and the slow-set regimes (SEM-EDS, XRD, TGA, and FT-IR). The CCA offered a dual hydraulic–pozzolanic reactivity, and mass balance analysis established that ~48% of CO₂ delivered as CaCO₃ (2.34 wt% of CCA mass) was fixed at 30 min carbonation at 400 mL/min. Despite this small increment, the quick-set CO₂-pretreated mix exhibited a hydraulic conductivity of 6.49 × 10⁻⁷ cm/s at a 7-day time point a 2.97-fold improvement over the OPC control relative to 28-day benchmarks for conventional fly ash–cement grouts. In contrast, preparation with CO₂ improved slow-set gel time by 28.4%, which allows out-of-specification gelation to be reinstated without the need for admixture reformulation. Strength data revealed transient surface passivation after 7 days and the convergence of CO₂-treated and untreated CCA mixes was finally obtained after 28 days (1.1 MPa), showing that pre-treatment can slow down but not decrease the pozzolanic activation. SEM and TGA data study confirmed that the increase in impermeability with CaCO₃ increased in larger amounts due to spatially acting pore-blocking, rather than bulk densification, is a clear possibility that may be a consequence favoring pre-carbonating toward distribution efficacy rather than maximum CO₂ uptake.

Microplastics (MPs) are an increasingly pervasive pollutant, prompting interest in using them as a waste valorization feedstock in cementitious composites—most commonly as partial replacements for mineral aggregates. This review critically assesses the technical feasibility and implications of this approach based on current experimental and analytical evidence. Across the literature, MPs differ fundamentally from natural aggregates in stiffness, density, and surface chemistry, which weakens particle packing and interfacial bonding. Consequently, MP–aggregate substitution typically reduces workability and compressive strength and degrades durability-related performance, including resistance to chloride ingress, carbonation, and freeze–thaw action, with adverse effects generally increasing at higher replacement levels. While isolated benefits such as reduced unit weight and occasional post-cracking responses have been reported under specific mix designs, untreated MPs usually behave as mechanically inactive inclusions and stress concentrators rather than effective reinforcement. Major uncertainties remain regarding long-term durability and the risk of secondary MP release. Overall, MP-based aggregate replacement should be considered a conditional, application-specific strategy, currently most defensible for non-structural or function-driven applications under carefully defined performance and environmental criteria.

Polymeric coatings play a pivotal role in enhancing the durability, functionality, and sustainability of structural materials exposed to harsh environmental conditions. Recent advances in artificial intelligence (AI) have transformed the development, optimization, and evaluation of these coatings by enabling data-driven material discovery, predictive performance modeling, and autonomous inspection. This review aims to provide a comprehensive overview on AI-driven polymeric coating strategies for structural applications, emphasizing the integration of machine learning, computer vision, and multi-physics simulations into traditional materials engineering frameworks. The discussion encompasses AI-assisted material selection methods for polymers, fillers, and surface modifiers; predictive models for corrosion, fatigue, and degradation; and intelligent evaluation systems using digital imaging, sensor fusion, and data analytics. Case studies highlight emerging trends such as self-healing, smart, and sustainable coatings that leverage AI to balance mechanical performance, environmental resistance, and carbon footprint. The review concludes with identifying current challenges—including data scarcity, model interpretability, and cross-domain integration—and proposes future research directions toward explainable, autonomous, and circular coating design pipelines.

The mechanical and durability performance of cementitious materials is fundamentally governed by the symmetry, anisotropy, and hierarchical organization of their microstructures. Conventional experimental characterization—based on imaging, spectroscopy, and physical testing—often struggles to capture these multiscale spatial patterns and their nonlinear correlations with macroscopic performance. Recent advances in machine learning (ML) provide unprecedented opportunities to interpret structural symmetry and anisotropy through data-driven analytics, computer vision, and physics-informed models. Furthermore, we summarize cases where symmetry-informed descriptors improve performance prediction accuracy in fiber- and nano-modified composites, demonstrating that ML-based symmetry analysis can substantially complement the limitations of conventional experimental-based characterization. We confirm that image-based models such as CNN and U-Net quantify the directionality and connectivity of pores and cracks, and that physically informative neural networks (PINNs) and heterogeneous data-based models enhance physical consistency and computational efficiency compared to conventional FEM and CFD. Finally, we present the conceptual and methodological foundation for developing AI-based microstructural symmetry analysis, aiming to go beyond simple prediction and establish a conceptual foundation for AI-driven cement design based on microstructure–performance causality.