Noise is a significant environmental factor that must be eliminated by appropriate means. This study investigates the acoustic insulation properties of 3D-printed porous materials with an octet-truss lattice structure, fabricated using fused filament fabrication (FFF) technology. The sound insulation performance of the tested materials was evaluated based on the frequency dependent sound absorption coefficient, which was experimentally determined using an acoustic impedance tube. In this work, several parameters affecting the sound absorption properties of the investigated lattice material structures were systematically analysed, including volume ratio, sample thickness, excitation frequency, and the presence of air gaps. Based on the findings, specific recommendations are proposed to enhance the sound absorption characteristics of the octet-truss lattice structures and thereby reduce unwanted noise.

In recent years, advanced manufacturing has gained traction, particularly in the microscale industries, to enhance material properties. The creation of micro-textures and achieving precise accuracy at the microscale are crucial for the performance of modern devices. In this paper, a method of facilitating these measures will be presented with the integration of Artificial Neural Networks (ANNs) to find the nonlinear relationship between the machining input parameters and the output microstructures’ geometrical shapes. GoogleNet, a kind of Convolutional Neural Networks (CNNs), was also used to categorize the images between defective and ideal dimples to reach optimum precision in the manufacturing context.

Transcatheter aortic valve (TAV) implantation is an established treatment strategy for patients with severe aortic stenosis across the spectrum of surgical risk profiles. Numerous randomised controlled trials have consistently demonstrated the safety and efficacy of TAV implantation compared with surgical aortic valve replacement, prompting an expansion of indications towards lower surgical risk, often younger, patients. In parallel, the number and types of TAV prosthesis have also increased. Although all devices have generally demonstrated favourable procedural and longer-term clinical outcomes, variations in frame design, material properties and leaflet configurations render specific devices more favourable in certain settings. In this review, we describe key differences in TAV design and how this may affect the choice of TAV prosthesis in the challenging clinical scenarios of patients with small annuli, coronary disease, long life expectancy, risk of permanent pacing and aortic regurgitation, which are expected to be encountered more frequently as indications for TAV implantation expand.

In this study, a hot stamping process, which delivers ready-to-use parts for the production of aircraft components is applied as an alternative manufacturing method to, for instance, machined parts. The research has been focused on examining the formability of an aluminium alloy at high temperatures. An extensive experimental campaign has been conducted to establish the optimal hot stamping process parameters. As a final stage of the development, a demonstrator corresponding to a wing rib with AA2198 aluminium-lithium alloy has been successfully produced. After the corresponding heat treatment, material properties have been restored.

Two-dimensional (2D) materials and liquid crystals (LCs) originate from opposite ends of the materials spectrum, with both recognized for several sought-after properties. In line with current trends in materials science, LCs have progressively entered the realm of nanocomposites, creating new vistas for LC-based applications. Although there is considerable research on these nano-soft composites, the nano-component has predominantly been of zero- and one-dimensional nature, and integration of 2D materials into the field is an appealing upcoming research area. This review outlines such endeavours, describing the influence of 2D materials on both thermotropic and lyotropic LCs, primarily focussing on the nematic mesophase, the orientationally ordered liquid. Sections on both these LCs begin with the theoretical efforts and experimental findings on several physical properties of the 2D materials forming the LCs, or incorporated into nematics in the bulk and upon confinement in a polymer matrix, or as substrate layers for uniform orientation of the nematic director. Various applications, including the bio-related ones, are also described. Finally, we outline potential pathways along which the domain of 2D materials in LCs might advance by addressing the perceived challenges. The interspersed critical comments on the research reported aim to encourage researchers to enrich the field with comprehensive efforts.

Soft actuation via harnessing entropic elastic energy: from theory to morphing wings

Understanding the Role of Cladding Material and Substrate Properties on Heat Transfer in the Horizontal Single-Belt Casting (HSBC) Process for Bimetallic Clad Strips: A Finite Volume Approach

Vibrational free energy estimation is a cornerstone of atomic simulation, essential to predict finite-temperature material properties. Expressing the free energy as a function of interatomic potential parameters is actively sought in modern workflows for uncertainty quantification or inverse design. However, to achieve meV/atom accuracy, existing schemes conduct slow, sequential sampling with fixed potential parameters. We present a solution, an efficient model-agnostic free energy estimator which is meV/atom accurate over a broad, multi-element parameter range. For a broad class of machine learning potentials we show the free energy is the Legendre transform of an entropy function, accurately estimated via score-matching. Sampling requires 10× less effort than a single traditional estimate, tensor compression ensures lightweight storage, and inference is instantaneous. We demonstrate targeting of phase boundaries in back-propagation, fine-tuning the α-γ transition temperature in a Fe model from 2030 K to 1063 K. Extensions to a range of high-dimensional integration tasks are discussed.

To investigate the influence of mechanical and chemical denture cleaners on the physical and adhesive properties of soft relining materials and suggest prospects for future studies. The present review followed the PICO (Population, Intervention, Control, Outcomes) format. The literature search for this review was conducted primarily using Web of Science and PubMed. However, because this search strategy did not capture some relevant articles, critical studies considered pertinent to the topic were additionally identified through manual screening of reference lists and related articles. The electronic search yielded a total of 22 studies (11 on PubMed and 11 on Web of Science). Duplicate references were removed, and those selected were in accordance with the inclusion criteria, as determined by reviewing the title and abstract for the influence of soft relining materials when using mechanical and chemical denture cleaners. Ultimately, 14 articles from PubMed and Web of Science as well as 9 articles from the manual search were included in the analysis. Mechanical cleaning has been indicated as potentially applicable to auto-polymerized acrylic and heat-polymerized silicone types. Chemical cleaning with sodium perborate has been indicated as potentially applicable to auto- and heat-polymerized acrylics and silicones.

Chemically vapor-deposited silicon carbide (CVD-SiC) is a high-performance material that possesses excellent mechanical, chemical, and electrical properties, making it highly promising for components in the semiconductor, aerospace, and automotive industries. However, its inherent hardness and brittleness present significant challenges to precision machining, thereby hindering the commercialization of reliable, high-precision parts. Therefore, the application of CVD-SiC in fields that require ultra-precision shaping and nanometric surface finishing necessitates the exploration of machining methods specifically tailored to the material’s unique characteristics. This paper presents a comprehensive review of CVD-SiC machining—from traditional mechanical approaches to advanced hybrid and high-energy techniques—aimed at overcoming machining limitations from its material properties and achieving high-efficiency and nanometric-quality machining. The study discusses various grinding tools designed for superior surface finishing and efficient material removal, as well as machining techniques that utilize micro-scale removal mechanisms for ductile regime machining. Looking ahead, the integration of AI-based process optimization with enhanced machining methods is expected to fully exploit the superior properties of CVD-SiC and broaden its industrial application as a high-performance material.

Concrete-filled steel tube (CFST) columns are composite structural elements preferred in various engineering structures due to their superior properties compared to those of traditional structural elements. However, fire resistance analyses are complex due to CFST columns consisting of two components with different thermal and mechanical properties. Significant challenges arise because current design codes and guidelines do not provide clear guidance for determining the time-dependent fire performance of these composite elements. This study aimed to address the existing design gap by investigating the fire behavior of circular long CFST columns under axial compressive load and developing robust, accurate, and reliable design models to predict their fire performance. To this end, an up-to-date database consisting of 62 data-points obtained from experimental studies involving variable material properties, dimensions, and load ratios was created. Analytical design models were meticulously developed using two advanced soft computing techniques: artificial neural networks (ANNs) and genetic expression programming (GEP). The model inputs were determined as six main independent parameters: steel tube diameter (D), wall thickness (ts), concrete compressive strength (fc), steel yield strength (fsy), the slenderness ratio (L/D), and the load ratio (μ). The performance of the developed models was comprehensively compared with experimental data and existing design models. While existing design formulas could not predict time-based fire performance, the developed models demonstrated superior prediction accuracy. The GEP-based model performed well with an R-squared value of 0.937, while the ANN-based model achieved the highest prediction performance with an R-squared value of 0.972. Furthermore, the ANN model demonstrated its excellent prediction capability with a minimal mean absolute percentage error (MAPE = 4.41). Based on the nRMSE classification, the GEP-based model proved to be in the good performance category with an nRMSE value of 0.15, whereas the ANN model was in the excellent performance category with a value of 0.10. Fitness function (f) and performance index (PI) values were used to assess the models’ accuracy; the ANN (f = 1.13; PI = 0.05) and GEP (f = 1.19; PI = 0.08) models demonstrated statistical reliability by offering values appropriate for the expected targets (f ≈ 1; PI ≈ 0). Consequently, it was concluded that these statistically convincing and reliable design models can be used to consistently and accurately predict the time-dependent fire resistance of axially loaded, circular, long CFST columns when adequate design formulas are not available in existing codes.

ABSTRACT The increasing demand for sustainable construction materials has driven research into the utilisationof coir fibers with sedu soil for brick manufacturing. This study explores the feasibility of incorporating coconut coir fiber (Husk) waste products --- coconut coir fiber (Husk) into brick production.By replacing traditional coir fiber (Husk) The use of soil s techniques in brick manufacturing has become increasing important for enhancing the properties of raw materials and promoting sustainable construction practices. Sedu soil, known for high plasticity and poor strength, poses challenges when used in traditional brick-making processes.This study explores the potential of incorporating coil fibers into sedu soil to improve its suitability for brick manufacturing. Coli fibers, with their helical structure, are recognized for their ability to enhance the mechanical properties of composite materials. In this research, the effects of different coil fiber content on the properties of sedu soil bricks were investigated. Various experimental tests, including compressive strength, shrinkage, and thermal conductivity, were performed on the bricks produced from sedu soil stabilized with varying percentages of coil fibers.The findings indicate that the inclusion of coil fibers significantly improved the mechanical strength, durability, and thermal performance of the bricks, making them more resilient and cost-effective. This study demonstrates that coil fibers can serve soil, offering a sustainable solution for improving the quality and performance of building materials in constructionIndia is one of the leading coconut directors in the world, producing 13 billion nuts per annum. Fired bricks are made by using soil beach mixes with different probabilities of rice cocoon ash.The blasting durations at 9000C were independently 2, 4 and 6 hours. The goods of rice cocoon content on workable mixing water content, Atterberg limits, direct loss , compressive strength and water absorption of the bricks were deived.The rapid growth of urbanization and industrialization has led to an increased demand for sustainable and eco-friendly building materials. The use of natural fibers, such as coir fibers, has gained significant attention in recent years due to their abundance, renewability, and potential to improve the mechanical properties of construction materials. This study investigates the utilization of coir fibers with Sedu soil for the manufacturing of bricks, aiming to develop a sustainable and cost-effective alternative to traditional building materials.Sedu soil, a type of expansive soil, is widely available in many regions and is often considered unsuitable for construction due to its high plasticity and low bearing capacity. However, its high clay content makes it an ideal candidate for brick production. Coir fibers, obtained from coconut husks, are a natural, biodegradable, and renewable resource that can improve the strength and durability of bricks.This research focuses on the development of coir fiber-reinforced Sedu soil bricks, evaluating their physical, mechanical, and durability properties. The coir fibers were added to the Sedu soil in varying proportions (0%, 0.5%, 1%, 1.5%, and 2%) and the resulting mixtures were molded into bricks. The bricks were then tested for their compressive strength, water absorption, and durability.The results showed that the addition of coir fibers significantly improved the compressive strength and durability of the bricks, with the optimal fiber content being 1.5%. The coir fibers helped to reduce the cracks and improve the bonding between the soil particles, resulting in a more compact and durable brick. The water absorption of the bricks decreased with the addition of coir fibers, indicating improved resistance to weathering and erosion.The study demonstrates the potential of utilizing coir fibers with Sedu soil for the manufacturing of sustainable and eco-friendly bricks. The developed bricks exhibited improved mechanical properties, reduced water absorption, and enhanced durability, making them suitable for use in low-cost housing and infrastructure projects. The use of coir fibers and Sedu soil can help reduce the environmental impact of traditional building materials, promote sustainable development, and support the local economy.

Purpose This research investigates the effects of the thickness parameter (m) on the mechanical behavior of rotating cylinders composed of isotropic materials under non-local media, subjected to varying strain-hardening indices and thermal conditions. The study systematically explores how variations in thickness, angular speed, and material properties influence the distributions of stress, displacement, and strain rate in a cylinder under diverse thermal and strain boundary conditions. Design/methodology/approach The analysis is carried out using B.R. Seth’s transition theory. Governing equations for radial, circumferential, and axial stresses and strain rates are derived in the context of non-local elasticity. Numerical solutions are obtained under varying strain-hardening indices, thermal gradients, and angular velocities. Results are graphically visualized using MATLAB. Findings The results reveal a significant sensitivity of stress and strain distributions to changes in thickness and strain-hardening indices, especially under high thermal gradients and angular speeds. Non-local effects are accurately prominent in predicting mechanical behavior, highlighting deviations from classical local elasticity models. Research limitations/implications The model assumes steady-state conditions and ideal material isotropy, which may limit its direct application to real-world engineering systems with more complex behaviors. Future research could include anisotropic materials, transient loading, or viscoelastic effects to improve realism. Despite these limitations, the framework offers a useful foundation for extending non-local theories to other geometries and applications, encouraging further investigation into materials and structures exposed to harsh thermal and rotational environments. Practical implications The findings provide practical guidance for engineers in selecting appropriate material properties and geometrical configurations for rotating components in aerospace, automotive, and energy applications under complex thermal and mechanical environments. Social implications The findings from this study can help engineers design safer and longer-lasting components for important industries like aerospace, power generation, and transportation. By using non-local elasticity to better understand how materials behave under high-speed and high-temperature conditions, the research helps reduce the chances of mechanical failures’ ultimately improving safety for people and systems that rely on rotating machinery. In addition, using models like B.R. Seth’s transition theory can lead to smarter use of materials, cutting down on waste and energy use during production. These improvements contribute to a more sustainable, efficient, and reliable engineering future. Originality/value This work integrates non-local elasticity theory with B.R. Seth’s transition framework to analyze rotating cylinders under thermo-mechanical loading. The study demonstrates the inadequacy of local theories in certain conditions and supports the use of non-local models for more accurate and reliable engineering design.

Recent studies have revealed a series of connections between the topological features of structural glasses and their material properties. These findings show a striking resemblance to results observed in quantum physics that underscore the significance of the nature of the wavefunction. However, so far the structural arrangement of the topological defects in glasses has remained elusive. Here, we investigate numerically the geometry and statistical properties of the topological defects related to the vibrational eigenmodes of a prototypical three-dimensional glass. We find that at low frequencies these defects form scale-invariant, quasi-linear structures and dictate the morphology of plastic events when the system is subjected to a quasi-static shear, i.e., the eigenmode geometry shapes the plastic behavior in 3D glasses. Our results indicate the presence of a profound connection between the topology of eigenmodes and plastic energy dissipation in disordered materials, thus generalizing the known link identified in crystalline materials.

3D bioprinting enables rapid fabrication of complex biological structures for tissue engineering applications. However, optimizing bioink formulation remains challenging due to complex relationships among material properties, printability, and cell viability. While the perimeter ratio (Pr) is commonly used to evaluate printability, it cannot adequately capture the full geometric fidelity required for comprehensive printability assessments, limiting robust bioink design. To address this limitation, a novel Hausdorff distance (HD) metric is employed to quantify printability, directly measuring the maximum deviation between the designed and printed structures. Furthermore, multiple machine-learning approaches were applied to alginate-hyaluronic acid (ALG-HA) composite inks and rat pheochromocytoma-derived PC12 cells to assess printability and cell viability. Rheological parameters were characterized using support vector regression (SVR) with R² ≥ 0.974. Multi-layer perceptron (MLP) regressors achieved R² values of 0.932 and 0.945 when predicting HD values of printed grid structures and cell viability, respectively. A regression-based convolutional neural network (CNN) was developed to predict HD values directly from grid structure images, achieving an R² of 0.986. Through optimization, optimal as-extruded cell viability (≥ 95%) can be achieved while maintaining high printability (HD ≤ 0.20). The optimal ink composition was further verified with good long-term cell viability and proliferation potential. This proposed AI-integrated approach can dramatically reduce ink optimization time by rapidly predicting rheological properties, printability, and cell viability from minimal experimental data.

To investigate the effects of different graphene block distribution modes on the vibration characteristics of functionally graded graphene-reinforced composite materials, this study first establishes the motion control differential equations based on the Halpin–Tsai micromechanical model and Hamilton’s principle. The nonlinear and free vibration behaviors of the materials are also analyzed. The results indicate that different graphene mass fractions and distribution patterns have varying degrees of influence on the resonance characteristics and mechanical properties of functionally graded graphene layer–reinforced composites. Among them, the cross distribution pattern exhibits the most effective vibration suppression under specific conditions. These findings provide a theoretical foundation for the development of lightweight, high-strength materials and contribute to understanding the inherent relationship between the distribution of graphene blocks and material properties.

The 2D nanofilm bulge test, which uses an Atomic Force Microscope (AFM) to measure the deflection of a suspended film under various conditions, has emerged as an important measurement platform for understanding mechanical, barrier, and permeability properties of 2D materials as thickness approaches the angstrom scale. The problem considered in this work is the limitation of such bulge analyses imposed by the AFM whereby dynamic measurements under high pressure, high temperature, and chemically corrosive conditions are limited. In this work, a technique is developed for measuring nanofilm deflection using only visible light interferometry. Both theoretical and semi-empirical models are applied to translate multicolor interference patterns from broadband excitation into estimates of nano-film deflection, allowing nanoscale precision in most cases. The technique and algorithm advanced in this work allows the use of widespread optical microscopy to widen the study of these important 2D nanofilm systems to more relevant conditions.

Abstract Students are introduced to the term ‘refractive index’ at the School level. It is an integral and very significant property of any particular material. In this paper, we present a simple and innovative method to determine the relation between refractive index and wavelength. We verify that for any liquid, the refractive index is inversely related to the wavelength of light. The novel approach here is to introduce different colours of LEDs and observe the degree of refraction of light as the wavelength is varied. This method can be incorporated as a school laboratory activity, as yet another way to help the students clearly understand the concept of refractive index. This experimental setup was developed at the Science Laboratory, Nehru Planetarium (Mumbai). This experiment can be used to explain the phenomenon of dispersion of light as a substitute for a prism, using different colour LEDs, and the different degrees of bending of respective light rays can be demonstrated.

It is known that reversible-deactivation radical polymerization (RDRP) offers distinct advantages in preparing homogeneous gel network microstructures. However, flexibly regulating hydrogel network microstructures via RDRP remains a significant challenge. Herein, we fully leveraged the advantages of atom transfer radical polymerization (ATRP) in preparing well-defined polymers and uniform hydrogel networks and proposed a strategy to construct hydrogel structures with a controlled hierarchical network. This approach employs a presynthesized, well-defined telechelic bromide macroinitiator (via ATRP) to initiate the photoATRP of vinyl monomers and divinyl cross-linkers. A primary polymer network was first formed by the telechelic macroinitiator. Subsequently, the active chain-end sites initiated ATRP of small-molecule cross-linkers, thereby grafting a covalently linked secondary cross-linked network. In other words, we have embedded larger, uniformly sized pores within a smaller, homogeneous network structure. The size of these "macropores" can be tuned by adjusting the molecular weight of the macroinitiator. This hierarchical architecture endows the hydrogel with significantly altered swelling behavior and mechanical properties. Furthermore, by using carbon-dot-catalyzed aqueous photoATRP, this type of hydrogel with a controllable hierarchical structure can be fabricated via digital light processing (DLP) 3D printing technology. This work provides new insights into the regulation of the microstructure and macroscopic properties of hydrogel materials.

The expanding range of materials available for 3D printing is driving its widespread adoption in advanced fields. As 3D printing becomes increasingly prevalent in the manufacturing of industrial components, its advantages in accommodating complex geometries and reducing material waste are attracting significant attention. Acquiring and applying precise elastic properties of materials during structural design is crucial for ensuring part safety and consistency. However, non-destructive mechanical property assessment methods remain limited. In this paper, we propose an efficient surrogate model, built using a Bayesian model updating approach combined with a random forest algorithm, to achieve high-precision calibration of material elastic constants. In the experiment, samples were 3D printed using fused deposition modeling, and modal information was obtained using operational modal analysis with one end fixed to simulate cantilever beam boundary conditions. Parameter updating was then performed within a Bayesian Markov Chain Monte Carlo framework. The deviation between the updated calculated frequencies and the measured frequencies was significantly reduced, and the Modal Assurance Criterion value between the updated calculated mode shapes and the measured mode shapes was higher than 0.99, demonstrating the accuracy of the updated parameters. Compared to traditional destructive testing methods, the proposed method directly calibrates the structural elastic modulus at the component level without affecting the normal use of the component, providing a more practical approach for the analysis and research of material properties in 3D printing additive manufacturing. The related technology can be extended to other structural forms of 3D-printed products.