Material Selection Optimization Research Articles

In Vitro Analysis of Shear Bond Strength in Repaired Cohesive and Adhesive Fractures of Conventional and DMLS Porcelain-fused-to-metal Crowns.

Porcelain-fused-to-metal (PFM) restorations are essential in fixed prosthodontics for their strength and esthetics, but are prone to fractures due to material disparities and stress factors. This study evaluates the shear bond strength (SBS) of two porcelain repair systems for cohesive and adhesive fractures in conventional PFM and direct metal laser-sintered (DMLS) restorations, addressing clinical repair needs. Thirty metal-ceramic discs were fabricated and divided into two main groups based on the fabrication method: Conventional casting and DMLS. Each group had three subgroups: Conventional casting (A: Control, B: Cohesive defect, C: Adhesive defect) and DMLS (D: Control, E: Cohesive defect, F: Adhesive defect), each with 5 specimens. Shear bond strength was measured using a Universal Testing Machine (UTM) at 0.5 mm/min. Data were analyzed with a one-way ANOVA (α = 0.05), and Tukey's post hoc test was used for significant differences. Student's t-tests compared SBS between control groups. A one-way ANOVA revealed significant differences in SBS among Conventional Casting subgroups (p < 0.001). Subgroup A (36.282 ± 1.692 MPa) had higher SBS than B (13.202 ± 1.336 MPa) and C (17.033 ± 1.634 MPa), with Tukey's test confirming significant differences (p < 0.001). For DMLS subgroups, subgroup D (37.768 ± 0.560 MPa) had higher SBS than E (22.381 ± 1.137 MPa) and F (13.245 ± 0.693 MPa), with Tukey's test showing significant differences (p < 0.001). No significant difference was found between subgroups A and D (p = 0.10). Subgroup E had a higher SBS than B (p < 0.001), and subgroup C had a higher SBS than F (p = 0.001). This study offers insights into the performance of two porcelain repair systems, aiding clinicians in selecting effective materials and techniques for PFM restoration repairs. Understanding the bond strength of these repair systems can enhance clinical outcomes by guiding the selection of optimal repair materials and techniques, improving the longevity and durability of fractured PFM restorations. How to cite this article: Irissan R, Mohan A, Augustine C, et al. In Vitro Analysis of Shear Bond Strength in Repaired Cohesive and Adhesive Fractures of Conventional and DMLS Porcelain-fused-to-metal Crowns. J Contemp Dent Pract 2024;25(8):726-731.

Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry

This study proposes a qualitative analysis for identifying the best predictors for ensuring passive noise control, aiming to achieve superior acoustic comfort in transportation systems. The study is based on real experimental data, collected through acoustic measurements performed by the authors on materials from six different classes and employs a multidisciplinary approach, including Mann–Whitney U tests, Kruskal–Wallis analysis with Dunn’s post hoc multiple comparisons and multilinear regression. This research presents an analysis and evaluation of how the physical properties of various materials influence acoustic comfort, acoustic absorption class and absorption class performance and proposes quantitative models for material selection to address sustainable strategies in the automotive industry. The results highlight significant differences between material categories in terms of acoustic absorption properties and demonstrate the importance of rigorous material selection in vehicle design to enhance acoustic comfort. Additionally, the research contributes to the development of predictive models that estimate acoustic performance based on the physical properties of materials, providing a basis for optimizing material selection in the design phase.

Machine Learning in Wear Prediction

Abstract As modern devices and systems continue to advance, device wear remains a key factor in limiting their performance and lifetime, as well as environmental and health effects. Traditional approaches often rely on wear prediction based on physical models, but due to device complexity and uncertainty, these methods often fail to provide accurate predictions and accurate wear identification. Machine learning, as a data-driven approach based on its ability to discover patterns and correlations in complex systems, has enormous potential for monitoring and predicting device wear. Here, we review recent advances in applying machine learning for predicting the wear of mechanical components. Machine learning for wear prediction shows significant potential in optimizing material selection, manufacturing processes, and equipment maintenance, ultimately enhancing productivity and resource efficiency. Successful implementation relies on careful data collection, standardized evaluation methods, and the selection of effective algorithms, with artificial neural networks (ANNs) frequently demonstrating notable success in predictive accuracy.

Spray Drying Microencapsulation of Antioxidant Bioflavonoids: A Bibliometric Analysis and Review on Recent Research Landscape (2013–2023) and Process Optimization

ABSTRACTSpray drying has become a prevalent method for microencapsulating antioxidant bioflavonoids, offering cost‐effectiveness and suitability for commercial nutraceutical production. Yet, challenges persist due to powder instability stemming from material compatibility issues and suboptimal operating conditions. A bibliometric analysis spanning 2013–2023 explored this research domain, revealing a burgeoning interest globally, notably from China, India, and Brazil. Keyword analysis highlighted prevalent themes like response surface methodology and process optimization, emphasizing maltodextrin's frequent use in encapsulation. Research delved into critical parameters such as wall material selection, compound‐to‐wall ratio, and operational optimization encompassing flow rate and inlet temperature. These insights drive advancements in spray drying technology, enabling the production of premium‐quality nutraceutical ingredients while addressing stability concerns. This research underscores the significance of optimizing spray drying processes to enhance the efficacy and shelf life of bioflavonoid‐based nutraceuticals for commercial viability.

Near-field underwater explosion and its interaction with a sandwich composite plate

Polymeric composite sandwich materials are critical for marine structures, but their behavior under near-field underwater explosions is not well understood. This study investigates the dynamic response of carbon-fiber-reinforced sandwich composites with varying core densities subjected to near-field underwater explosions. Lab-scale experiments were conducted at two explosive stand-off distances using high-speed imaging and Digital Image Correlation (DIC) to capture the evolution of gas bubble dynamics, surface cavitation, and structural deformation. Results showed that reducing the stand-off distance led to a 0.7 ms increase in gas bubble period, along with an 80 mm increase in horizontal migration of the bubble, while vertical migration remained unaffected. The interaction between the gas bubble and surface cavitation, driven by fluid-structure interaction (FSI), significantly influenced the structural response. In particular, the simultaneous collapse of the gas bubble and surface cavitation resulted in higher localized impulsive loading, causing catastrophic failure in low-density core panels. Meanwhile, panels with higher core densities exhibited a 40 % reduction in out-of-plane deflection, demonstrating enhanced resistance to blast loading. This study provides new insights into the fluid-structure interaction mechanisms that occur during near-field underwater explosions and offers a basis for improving the design of marine structures by optimizing material selection and geometric configurations. These findings contribute to a deeper understanding of shock mitigation strategies in composite materials and inform future research in marine structural design under extreme loading conditions.

Research on the Cavitation Resistance Testing of Friction Stir Welded Joint of En Aw 1200 Aluminium Alloy

Aluminium alloys are being used more often as a result of industry demands for strong, lightweight materials. Because of its advantages over conventional fusion welding techniques, friction stir welding, or FSW, has become a promising technique for joining aluminium alloys. Nonetheless, there is still cause for concern regarding FSW joints' vulnerability to cavitation erosion, which is a major issue in applications exposed to harsh environments. The cavitation resistance of EN AW 1200 aluminium alloy joints made by the FSW process is the main objective of this study. By utilizing specialized equipment to conduct cavitation tests, the study uses a comprehensive methodology to assess the cavitation erosion behaviour of these joints. A number of variables are investigated in order to determine how they affect the cavitation resistance of the welded joints, including mechanical attributes, welding parameters, and microstructural features. The goal of the study is to shed light on how well FSW joints function and hold up in cavitation scenarios. This information will be useful in improving the dependability and suitability of EN AW 1200 aluminium alloy in sectors where the ability to withstand cavitation erosion is critical. The findings from this study are expected to contribute to the optimization of welding parameters and material selection, ultimately advancing the use of aluminium alloys in critical applications requiring resistance to cavitation-induced damage.

Enhanced self-biased photocatalytic fuel cell performance by dual-photoelectrode with novel graphene-based substrate for tetracycline photodegradation and electricity production

In this study, a graphene-based dual-photoelectrode photocatalytic fuel cell (PFC) system, incorporating n-type ZnO photoanode and p-type Cu2O photocathode, has been meticulously designed and fabricated to achieve concurrent organic pollutant decomposition and electricity production. The rGO-ZnO｜rGO-Cu2O PFC system displayed superior photoelectrocatalytic performance, surpassing the comparative FTO-based PFC system. Notably, it achieved power density of 22.23 μW cm−2 maximumly, tetracycline hydrochloride (TCH) degradation efficiency of 74.0 % within 120 min, and exhibited long-term stability. This enhanced performance can give the credit to the larger active areas generated by the in-situ growth of photocatalytic materials in layered microstructure of graphene films, as well as the rapid electron transfer within graphene films. This study underscores the potential application of graphene films as electrode substrate materials, and introduces a strategy for achieving the diversification and optimization of electrode substrate material selection, ultimately facilitating the development of more efficient PFC systems.

Application of artificial intelligence in construction

This paper mainly introduces the application and assistance of artificial intelligence in architecture and construction cost, as well as the future prospects and development trends. The study adopts a quantitative research design of survey research and data collection method of literature analysis. In addition, it can further explore the innovative applications of artificial intelligence in architectural design, such as spatial planning optimization based on big data analysis and simulation experience through virtual reality technology. At the same time, it can also deeply explore the role of artificial intelligence in the automated management of construction processes, material selection optimization, etc., and give detailed explanations with specific cases. In addition, when discussing the future prospects and development trends, it can consider the prediction of the impact of the continuous evolution of artificial intelligence technology on the construction industry, such as the wider application of machine learning algorithms in project management, the gradual maturity of intelligent sensor systems in building equipment monitoring. At the same time, it can also analyze the profound impact of artificial intelligence on the future development of the construction industry from economic, social and environmental angles, and put forward corresponding solutions.

Machine Learning‐Driven Selection of Two‐Dimensional Carbon‐Based Supports for Dual‐Atom Catalysts in CO2 Electroreduction

AbstractThe electrocatalytic reduction of carbon dioxide by metal catalysts featuring dual‐atomic active sites, supported on two‐dimensional carbon‐nitrogen materials, holds promise for enhanced efficiency. The potential synergy between various support materials and transition metal compositions in influencing reaction performance has been recognized. However, systematic studies on the selection of optimal support materials remain limited, primarily due to the intricate structure of dual‐atom catalysts generating a variety of potential adsorption sites. Incorporating the influence of support materials further amplifies computational challenges, doubling the already substantial calculation requirements. This study addresses this challenge by introducing a machine learning approach to expedite the identification of the most stable intermediate adsorption sites and simultaneous prediction of adsorption energy. This innovative method significantly reduces computational costs, enabling the simultaneous consideration of active sites and support materials. We explore the use of both graphene‐like (g−)C2N and g‐C9N4 materials, revealing their main distinction in the adsorption capacity for the intermediate *CHO. This variation is attributed to the different C : N ratios influencing support for the active site through distinct charge transfer conditions. Our findings offer valuable insights for the design and optimization of dual‐atom catalysts.

Tribological behavior of carbon steel 45 and brass H90 in dry sliding on bearing steel GCr15 in the sand-dust environment

PurposeMachinery operating in a sand-dust environment is more susceptible to sand particles. The purpose of this paper is to investigate the impact of sand particle deposition rate, surface hardness and normal load on the tribological performance.Design/methodology/approachA predictive model to approximate the number of sand particles within the pin-on-disc contact surface is proposed. The efficacy of the model is validated through experimental method, which replicates a sand environment with two distinct particle deposition rates. Dry sliding friction experiments are also conducted using 45 carbon steel and H90 brass pins against GCr15 bearing steel discs.FindingsWhen at high particle deposition rate [6.89 × 10–5 g/(s·mm2)], the contact surfaces are separated by particles, resulting in an indirect metal contact. While at low deposition rate [6.08 × 10–8 g/(s·mm2)], there is an alternating occurrence of direct and indirect metal contacts. In sand environment, the specific wear rate of 45 and H90 decreases by 50% and 33%, respectively, compared to non-sand environment when the applied load is 2.45 N. However, it is only 0.18% for 45 but remains significant at 25% for H90 at load of 9.8 N.Originality/valueThe predictive model and experimental method used in this paper are helpful for understanding the interaction between particles and sliding surfaces, thereby providing a solid foundation for material selection and load optimization of friction pairs influenced by sand-dust environments.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0155/

Optimizing CuInSe2 solar cells with kesterite-based upper absorber and back surface field layers for enhanced efficiency: a numerical study

Abstract This study explores the performance enhancement of an innovative multi-layer solar cell structure using the SCAPS-1D (Solar Cell Capacitance Simulator in One Dimension) software. We aim to improve the efficiency of a solar cell structure comprising ZnO/ZnSe/CZTSe/CuInSe2/CZTSSe/Mo by incorporating CZTSe as the upper absorber layer, CuInSe2 as the main absorber layer, and CZTSSe as a back surface field (BSF) layer. Initially, we compare the performance of three different configurations by analyzing their J-V characteristics. For the best performing structure, we further examine the external quantum efficiency (EQE) spectrum. We then evaluate various window (ZnO, ZnMgO, SnO2, Zn2SnO4) and buffer (ZnSe, ZrS2, SnS2, In2S3) materials, identifying ZnO and ZrS2 as the most effective for achieving high current density and efficiency. Through detailed simulations, we determine the optimal thicknesses for CZTSSe (0.2 µm), CZTSe (0.4 µm), and CuInSe2 (3.2 µm). Additionally, by optimizing the acceptor density to 1020 cm−3, we significantly enhance the performance of both CZTSe and CZTSSe layers. Temperature management is shown to be crucial, with the highest efficiency observed at 300 K. As a result of these optimizations, the solar cell structure achieves a remarkable efficiency of 35.38%. Furthermore, we compare our results with existing literature to highlight the advancements made in this study. These findings underscore the importance of material selection and structural optimization in developing high-efficiency solar cells and provide a framework for future advancements in photovoltaic technology.

Material Performance Evaluation for Customized Orthoses: Compression, Flexural, and Tensile Tests Combined with Finite Element Analysis.

Orthoses are commonly used for treating injuries to improve the quality of life of patients, with customized orthoses offering significant benefits. Additive manufacturing, especially fused deposition modelling, enhances these benefits by providing faster, more precise, and more comfortable orthoses. The present study evaluates nine polymeric materials printed in horizontal and vertical directions by assessing their performance through compressive, flexural, and tensile tests. Among all materials, polycarbonate, polylactic acid, and ULTEMTM 1010 showed the most promising results, not only because they had the highest mechanical values, but also due to their minimal or no difference in performance between printing directions, making them advantageous in orthoses fabrication. Based on this, a finite element model of an ankle-foot orthosis was developed to simulate the deformation, strain, and stress fields under static conditions. The findings aim to optimize material selection for orthotic fabrication, where ULTEMTM 1010 is presented as the material with improved performance and durability.

Theoretical Study of the Steady Time of Thermal Transmission in Materials That Have Engineering Uses

General Background: Understanding the time required to achieve steady-state heat transfer in construction materials is crucial for various engineering applications, including construction, architecture, and electronics. Specific Background: This study investigates the steady time of thermal transmission in engineering materials using both theoretical and experimental approaches. Knowledge Gap: Despite existing research on heat transfer, there is a lack of comprehensive studies that integrate both simulation and experimental validation for a wide range of building materials.Aims: The primary aim is to evaluate the steady-state time of heat transfer in fifteen construction materials through theoretical models and simulations using MATLAB and ANSYS, and to experimentally validate these findings with four selected materials. Results: The study finds that the steady time for heat transfer varies significantly among materials, with wood packaging requiring the longest time (32.49 hours) and aluminum the shortest (0.49 hours). Theoretical results, validated by simulations, show close agreement between MATLAB and ANSYS (0.087% deviation) and between numerical and experimental results (9.5% deviation). Brick demonstrated a 61.18% longer heat transfer time compared to concrete, while thermostone showed a 17.45% and 89.31% increase over brick and concrete, respectively. Novelty: This research provides new insights into the comparative stability of heat transfer times across a broad spectrum of materials, highlighting significant performance variations and validating simulation methods against experimental data. Implications: These findings are vital for optimizing material selection in construction to enhance thermal efficiency. The study’s methodologies can aid in developing more accurate predictive models for heat transfer in construction materials, thereby contributing to improved design and energy performance in building applications.

Materials characterization for Refuse Derived Fuel (RDF) production as renewable energy resources

This study offers a comprehensive analysis of key parameters—volatile matter, carbon content, ash content, and gross energy—across various material samples intended for Refused Derived Fuel (RDF) briquette production. Through meticulous examination, promising trends emerge, highlighting optimal material combinations for efficient combustion and heat generation. Samples rich in volatile matter and carbon content, notably those incorporating wood powder, demonstrate elevated calorific values, indicating their potential for effective energy production. Conversely, material combinations with low ash content suggest cleaner combustion and reduced environmental impact. The gross energy analysis further validates the substantial heat generation potential of specific sample combinations, rendering them suitable for diverse heating applications. These findings emphasize the critical role of precise raw material selection and meticulous manufacturing process optimization in producing RDF briquettes with desirable properties. Such briquettes not only offer economic viability but also contribute to environmental sustainability by providing an alternative fuel source with reduced emissions. This research underscores the importance of continued exploration and refinement in the development of RDF briquettes, aiming to meet growing energy demands while mitigating environmental concerns.

Correlation analysis of feedstock flowability and temperature for laser-based powder bed fusion of polymers

PurposeFor additive manufacturing (AM) through laser-based powder bed fusion of polymers (PBF-LB/P), accurate characterization of powder flowability is vital for achieving high-quality parts. However, accurately characterizing feedstock flowability presents challenges because of a lack of consensus on which tests to perform and the diverse forces and mechanisms involved. This study aims to undertake a thorough investigation into the flowability of eight feedstock materials for PBF-LB/P at different temperatures using various techniques.Design/methodology/approachFor ambient temperature assessments, established metrics such as avalanche angle and Hausner ratio, along with the approximated flow function coefficient (FFCapp), are used. The study then focuses on the influence of elevated temperatures representative of in-process conditions. FFCapp and differential scanning calorimetry (DSC) are performed and analyzed, followed by a correlation analysis as a holistic approach to identify key aspects for flowability. Furthermore, two feedstock materials are compared with a previous study to connect the present findings to PBF-LB/P processing.FindingsThe study revealed intrinsic material properties such as mechanical softening near the melting point to become significant. This partially explains why certain powders with poor ambient temperature flowability are consistently demonstrated to produce high-quality parts. FFCapp and thermal characterization through DSC are identified as critical metrics for optimizing feedstock material characteristics across temperature ranges.Originality/valuePrevious studies emphasized specific characterizations of feedstock material at ambient temperature, presented a limited materials selection or focused on metrics such as shape factors. In contrast, this study addresses a partially understood aspect by examining the critical role of temperature in governing feedstock material flowability. It advocates for the inclusion of temperature variables in flowability analyses to closely resemble the PBF-LB/P process, which can be applied to material design, selection and process optimization.

Advances in Filament Structure of 3D Bioprinted Biodegradable Bone Repair Scaffolds.

Conventional bone repair scaffolds can no longer meet the high standards and requirements of clinical applications in terms of preparation process and service performance. Studies have shown that the diversity of filament structures of implantable scaffolds is closely related to their overall properties (mechanical properties, degradation properties, and biological properties). To better elucidate the characteristics and advantages of different filament structures, this paper retrieves and summarizes the state of the art in the filament structure of the three-dimensional (3D) bioprinted biodegradable bone repair scaffolds, mainly including single-layer structure, double-layer structure, hollow structure, core-shell structure and bionic structures. The eximious performance of the novel scaffolds was discussed from different aspects (material composition, ink configuration, printing parameters, etc.). Besides, the additional functions of the current bone repair scaffold, such as chondrogenesis, angiogenesis, anti-bacteria, and anti-tumor, were also concluded. Finally, the paper prospects the future material selection, structural design, functional development, and performance optimization of bone repair scaffolds.

Synergistic effect of niobium carbide and titanium nanoparticles on the mechanical and microstructural properties of Al-Zn-Mg-Cu alloy hybrid composites

In this study, an Al-Zn-Mg-Cu alloy matrix serves as the matrix material, with Niobium Carbide (NbC) and Titanium (Ti) nanoparticles acting as reinforcements to enhance composite strength. Incorporating nano-structured reinforcements significantly enhance both strength and toughness, surpassing micro-sized counterparts. To prevent nanoparticle agglomeration during manufacturing, we employ vigorous stirring via the stir casting technique to ensure uniform dispersion within the composite matrix. SEM analysis confirms uniform dispersion of NbC and Ti nanoparticles within the matrix. Experimentally, the composite exhibits exceptional tensile strength, with sample B achieving the highest load of 5500 N, correlated with an optimal NbC percentage of 5 wt% and Ti percentage of 2 wt%. However, increasing NbC percentage from 5 wt% to 10 wt% results in decreased ductility and tensile strength for composite C. Similar trends are observed in bending strength, with composite B exhibiting the highest load at 9023.8 N, surpassing samples C and D. Microhardness results demonstrate an increase with NbC content, peaking at 230 HV, but declining to 185 HV with reduced NbC and Ti concentrations. Charpy impact tests reveal a consistent rise in impact energy from samples B to D, attributed to the escalating proportion of NbC relative to Ti. Overall, meticulous material selection and processing optimization are essential for developing high-performance metal matrix composites with enhanced mechanical properties.

Design and Development of a Manual Briquette Making Machine

Abstract: This research paper presents a detailed investigation into the design and development of a manual briquette making machine. With the escalating environmental concerns and the necessity for sustainable energy solutions, briquettes have emerged as a viable alternative to traditional fuels. The manual briquette making machine offers a cost-effective and environmentally friendly solution for producing biomass briquettes. Through meticulous design considerations, material selection, and performance optimization, this paper provides insights into the engineering principles underlying the development of such a machine.

Paper Battery: The Future and Solution for Traditional Battery

Today is the biggest problems are faced by the electronics industry size of batteries gadgets ultra-thin and small day by day.Paper batteries will the replace the li-ion battery and paper battery. Paper-based batteries have emerged as a promising solution for sustainable energy storage owing to their eco-friendly nature, lightweight, flexibility, and cost-effectiveness. This paper presents a comprehensive overview of recent advancements in the field of paper-based batteries, focusing on their fabrication techniques, materials selection, and performance optimization strategies. Various types of paper substrates, conductive materials, and electrolytes employed in paper-based batteries are discussed, along with their impact on the overall performance and environmental sustainability. Additionally, this paper highlights the potential applications of paper-based batteries in portable electronics, medical devices, and wearable technologies, emphasizing their role in promoting the development of green energy storage solutions. Finally, challenges and future directions for the research and development of paper-based batteries are outlined, aiming to inspire further innovation and integration of these eco-friendly energy storage devices into real-world applications. This biodegrable and CNT. Electronics device are classified into two types primary and secondary. The paper are shown in SWCNT and MWCNT are the contain in this type. Title: Advancements in Paper-Based Batteries: A Sustainable Approach towards Energy Storage Keywords: CNT (carbon Nano tube), SWCNT (singled walled carbon Nano tube), MWCNT (multi walled carbon Nano tube, Paper Batteries), Flexibility.

Design and Analysis of Fugitive Dust Emission Collector

This creative project explores the design and analysis of a novel fugitive dust emission collector, with a focus on its application in stone crusher units and crematorium facilities. Recognizing the detrimental health and environmental impacts of dust emissions, this project proposes a multifaceted approach. The core solution involves a combined system, this captures and removes larger dust particles through established methods like cyclones or bag houses. Dry fog dust suppression system, this technology utilizes atomized water droplets to suppress smaller fugitive dust particles, preventing their airborne spread. This project delves into the specific needs of stone crusher units and crematorium facilities. Utilizing CAD software, 3D models of the emission collector will be created, incorporating considerations like material selection, efficiency and airflow optimization, integration with existing infrastructure. Computational fluid dynamics (CFD) analysis by Simulating airflow patterns and dust capture efficiency within the collector. Assessing the collector's ability to withstand operational loads and environmental factors. This project aims to achieve, reduced fugitive dust emissions, enhanced environmental compliance, improved operational efficiency. Overall, this creative project showcases an innovative approach to fugitive dust control, potentially leading to improved environmental and public health outcomes in diverse industrial settings