Coronary stent implantation is an invasive procedure performed to correct atherosclerosis. The aftermath of this procedure is identified with neointimal hyperplasia, which contributes to in‐stent restenosis (ISR), and thus remains a significant clinical challenge. This study introduces a novel, noninvasive conceptual approach for addressing ISR through the thermal activation of shape memory alloy (SMA) stent using focused ultrasound (FU) in a controlled manner to restore luminal patency. COMSOL Multiphysics and ABAQUS finite element software were employed to perform the numerical modeling to simulate the thermal and mechanical responses of the SMA stent integrated into the arterial wall. After 15 s of insonation, the temperature within the tissue layers rises from 37°C to 42.63°C. Surface temperature rise plot confirms that heat is directed at the target region (focal zone) with minimal heating effect to the surrounding tissues. Minor increases in stress development in both the stent and artery observed were within acceptable limits, which indicates mechanical integrity and a low chance of increasing mechanical damage via stretching to the tissue. This development offers a viable alternative to reintervention, providing precise, patient‐specific, and ultrasonically guided therapy for ISR.

Shape memory alloys (SMAs), such as Ni–Ti, are valuable in structural and aerospace applications for their shape memory and superelastic properties. However, their performance can be degraded by corrosive and high-temperature environments. Although surface coatings such as thermal barrier coatings (TBCs) and anti-corrosion coatings (ACCs) are used for protection, their effects on the phase transformations of SMAs are not well understood. This study evaluates the impact of commercial polymer-based TBC and ACC coatings on the behavior of Ni–Ti SMAs through thermal, mechanical, and microstructural analysis. Results indicate that both coatings maintain the SMA’s phase transformation temperatures. However, the transformation enthalpy decreased for ACC-coated samples and increased for TBC-coated samples compared to uncoated ones. In thermal recovery tests, uncoated samples showed the highest shape recovery, followed by ACC-coated and TBC-coated samples, suggesting a compromise between protection and transformation efficiency. After 100 cycles, ACC coatings deteriorated significantly, while TBC coatings showed only minor cracking. Electrochemical tests confirmed the anti-corrosion effectiveness of the ACC, and thermal analysis verified the insulating property of the TBC. These findings demonstrate the trade-offs between coating functionality and SMA performance, offering practical insights for selecting coatings in SMA systems under thermal cycling conditions.

Recent advancements in material fabrication technologies have led to the creation of dual-property materials, characterized by distinct core and surface properties tailored to meet specific functional requirements. Surface modification is crucial for enhancing the functionality of Ni–Ti-based thermosensitive shape memory alloys (SMAs). This enhancement is important for various applications, including corrosion resistance, wear resistance, biocompatibility, and thermal stability. This review presents a novel process-based framework that categorizes surface modification strategies into five distinct classes: bulk alloying, thermochemical, thermophysical, hybrid, and direct surface coatings. It systematically evaluates their performance trade-offs, thermal compatibility issues, and failure mechanisms. By incorporating a meta-analysis of failure susceptibility and offering application-driven selection guidelines, this work provides a structured decision-support tool for researchers and engineers involved in the design of surface-modified Ni–Ti SMAs for biomedical, aerospace, and civil applications.

Metakaolin-based geopolymer coatings (GCs) represent an environmentally friendly alternative to conventional protective coatings, but their effectiveness is highly influenced by formulation parameters. In this study, the effect of activator/binder (a/b) ratio on the microstructural development, interfacial adhesion, and mechanical performance of metakaolin-based geopolymers was systematically investigated. Three ratios (0.3, 0.4, 0.5) were evaluated for pull-off adhesion, pencil hardness, 3D profilometry, FTIR, XRD, and digital microscopy. The 0.3 ratio provided weak geopolymerization, high roughness (Sa = 42.8 µm), and low hardness, which were beyond the valid range. Both 0.4 and 0.5 yielded coherent coatings, but with distinct performance: 0.4 maximized adhesion due to moderate roughness (Sa = 27.1 µm) and optimal thickness; 0.5 maximized scratch hardness due to a smoother surface (Sa = 9.1 µm). Increasing the a/b ratio increased porosity via excess water evaporation and reduced cross‑linking density. Digital microscopy confirmed no early‑age drying damage. The processing window for metakaolin‑based GCs is a/b = 0.4–0.5, within which adhesion (0.4) or hardness (0.5) can be prioritized, providing a rational formulation framework for tunable geopolymer coatings.

The conventional trial-and-error method used in concrete mix design results in considerable inefficiencies, material waste, and cost overruns in construction. Construction and demolition debris, which includes concrete, accounts for 35%–65% of landfill volume, with concrete making up 60%–70% of that total. This inefficiency highlights the urgent need for improved methodologies to produce high-performance concrete (HPC) that is economically viable and environmentally sustainable. To automate this process, this study introduces a novel approach that reverses the conventional prediction task by predicting optimal mix compositions directly from target strength and age, rather than estimating strength from mix proportions. This innovative framework employs a multi-tower multilayer perceptron architecture that integrates various feature groups to align computational design with performance objectives. By eliminating the need for costly empirical iterations, this method places sustainability at the forefront of material optimization. The proposed deep learning model was rigorously tested against four established regression methods: (1) support vector machines; (2) random forest; (3) eXtreme Gradient Boosting; and (4) multioutput regression. The deep learning architecture outperformed these methods, achieving a mean coefficient of determination of 0.739 and an average mean absolute error of 16.712 kg/m ³ . Additional error and statistical analyses confirmed the model’s robustness and reliability across diverse performance scenarios. In contrast, SHapley Additive exPlanations analysis revealed that the model’s predictions align with concrete material science: strength dominates cement and admixture predictions, with age governing supplementary cementitious materials. These findings illustrate that machine learning can effectively generate mix proportions based on performance objectives, paving the pathway toward sustainable, cost-effective, automated, and performance-based HPC design.

Metakaolin-based geopolymer coatings (GCs) represent an environmentally friendly alternative to conventional protective coatings, but their effectiveness is highly influenced by formulation parameters. In this study, the effect of activator/binder (a/b) ratio on the microstructural development, interfacial adhesion, and mechanical performance of metakaolin-based geopolymers was systematically investigated. Three ratios (0.3, 0.4, and 0.5) were evaluated for pull-off adhesion, pencil hardness, 3D profilometry, FTIR, XRD, and digital microscopy. The 0.3 ratio provided weak geopolymerization, high roughness (Sa = 42.8 µm), and low hardness, which were beyond the valid range. Both 0.4 and 0.5 yielded coherent coatings, but with distinct performance: 0.4 maximized adhesion due to moderate roughness (Sa = 27.1 µm) and optimal thickness; 0.5 maximized scratch hardness due to a smoother surface (Sa = 9.1 µm). Increasing the a/b ratio increased porosity via excess water evaporation and reduced cross-linking density. Digital microscopy confirmed no early-age drying damage. Based on the tested range (a/b = 0.3, 0.4, 0.5), the preferred formulation range for metakaolin-based GCs is identified as a/b = 0.4–0.5, within which adhesion (0.4) or hardness (0.5) can be prioritized to provide a rational formulation framework for tunable geopolymer coatings.