Materials Science Approach Research Articles

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

Fabrication and Fluorescence Analysis of Rhodamine Dyes in Polycarbonate Serpentine Microfluidic System.

The synthesis of rhodamine dyes (R6G and R1010) and their fluorescence characterization within polymer-based microfluidics, offers an exciting and novel approach in materials science and chemical analysis. This work investigates the emission of polycarbonate substrates (PC) by UV-visible. The ablation threshold (16mj.sec-1) of PC at 193nm wavelength after that ablation process continued to produce microfluidic serpentine channels on PC by using G-Code. The fluorescence characteristics of Rhodamine 6G and Rhodamine 101 are investigated. Absorption and emission at peak wavelength were analyzed against R6G and R101 concentrations. Furthermore, the refractive indices of both R6G and R101 vis concentrations are examined. As a result at low concentrations, there was the highest overlapping, and at high concentrations, there was the smallest overlapping. R101 showed better photostability and a more consistent diffusion, whereas R6G had a faster diffusion and stronger fluorescence intensity. These differences were caused by the different molecular structures of the dyes and their interactions with the PCmicrochannel. Incorporating R6G and R101 dyes into a polycarbonate PC microfluidic chip would enhances both the resolution and sensitivity of fluorescence detection. The limited microfluidic setup facilitates ultra-high-resolution investigation and minimizing sample volumes, making it suitable for applications requiring precise measurements. The innovation relies on the utilization of the unique fluorescence characteristics of R6G (Rhodamine 6G) and R101 (Rhodamine 101) dyes to enhance the performance of polycarbonate microfluidic devices. R6G has high fluorescence quantum yield and stability, rendering it suitable for sensitive detection, while R101 offers superior brightness and improved resistance to photobleaching. Incorporation of these dyes into polymeric microfluidics improves sensitivity and facilitates real-time, dynamic sample analysis. This method offers a portable, economical solution with high-throughput capabilities, greatly enhancing both analytical and process accuracy across a variety of applications.

Heteroatom Immobilization Engineering toward High-Performance Metal Anodes.

Heteroatom immobilization engineering (HAIE) is becoming a forefront approach in materials science and engineering, focusing on the precise control and manipulation of atomic-level interactions within heterogeneous systems. HAIE has emerged as an efficient strategy to fabricate single-atom sites for enhancing the performance of metal-based batteries. Despite the significant progress achieved through HAIE in metal anodes for metal-based batteries, several critical challenges such as metal dendrites, side reactions, and sluggish reaction kinetics are still present. In this review, we delve into the fundamental principles underlying heteroatom immobilization engineering in metal anodes, aiming to elucidate its role in enhancing the electrochemical performance in batteries. We systematically investigate how HAIE facilitates uniform nucleation of metal in anodes, how HAIE inhibits side reactions at the metal anode-electrolyte interface, and the role of HAIE in promoting the desolvation of metal ions and accelerating reaction kinetics within metal-based batteries. Finally, we discuss various strategies for implementing HAIE in electrode materials, such as high-temperature pyrolysis, vacancy reduction, and molten-salt etching and anchoring. These strategies include selecting appropriate heteroatoms, optimizing immobilization methods, and constructing material architectures. They can be utilized to further refine the performance to enhance the capabilities of HAIE and facilitate its widespread application in next-generation metal-based battery technologies.

Direct Calculations of the Dielectric and Optical Parameters of Some Compound Semiconductors: A Study Using the Lorentz Oscillator and Wemple-DiDomenico Models

The Lorentz single oscillator model is widely utilized to describe the electron-phonon interaction in solids. The use of this model to calculate the dielectric function and optical parameters of compound semiconductors represents a novel approach in materials science which may lead to develop new materials with tailored optical properties and consequently can offer vast applications in areas such as quantum computing and quantum information processing. This work presents direct calculations of the complex dielectric function and some optical parameters, such as refractive index, extinction coefficient, and reflectivity for some selected III-V and II-VI compound semiconductors using Lorentz oscillator model. The dielectric and optical dispersion parameters among the infrared and visible electromagnetic spectra follows the so-called Transmission-Absorption-Reflection-Transmission (TART) trend. The TART curve provides information about how a material interacts with light at different wavelengths, which is crucial for understanding and optimizing various optoelectronic devices. Moreover, dielectric loss functions such as loss tangent, surface energy loss function and volume energy loss function are investigated. The refractive index dispersion is analyzed using Wemple–DiDomenico single effective oscillator model and Sellmeier equation. The energy of the effective single oscillator (Eo) and the dispersion energy (Ed) are estimated.

AIPHAD, an active learning web application for visual understanding of phase diagrams

Phase diagrams provide considerable information that is vital for materials exploration. However, the determination of multidimensional phase diagrams typically requires a significant investment of time, cost, and human resources owing to the necessity of numerous experiments or simulations. Machine learning and artificial intelligence techniques present a viable solution for expediting phase diagrams investigations. Additionally, effective visualization is critical for understanding phase diagrams. This study reports the development of AIPHAD (Artificial Intelligence technique for PHAse Diagram), an open-source web application to assist in the investigation and visual understanding of phase diagrams using active learning. AIPHAD employs PDC (Phase Diagram Construction) algorithm, which operates on the principle of uncertainty sampling in active learning. The AIPHAD application facilitates the examination of five diagram types: two-variable diagrams, three-variable diagrams, ternary sections, ternary phase diagrams, and quaternary sections. The efficacy of the application is demonstrated in the study of the Fe-Ti-Sn ternary system, where it efficiently identified the presence of the Heusler phase. The integration of machine learning tools with traditional materials science approaches showcased in this study has the potential to drive groundbreaking advancements in materials exploration and discovery.

Higher‐Order Behaviours in Bio‐Inspired Materials

AbstractBio‐inspired approaches in materials science and systems chemistry are yielding a variety of stimuli‐responsive and dynamic materials that are gradually changing our everyday life. However, the ability to chemically program these materials to exhibit macroscopic higher‐order behaviours such as self‐assembly, contractility, swarming, taxis, chemical communication, or predator‐prey dynamics remains an ongoing challenge. While still in its infancy, the successful fabrication of bio‐inspired materials displaying higher‐order behaviours not only will help bridging the gap between living and non‐living matter, but it will also contribute to the development of advanced materials for potential applications ranging from tissue engineering and biotechnology, to soft robotics and regenerative medicine. Our Mini‐Review will systematically discuss the higher‐order behaviours developed thus far in bio‐inspired systems, namely (i) polymer networks (ii) microbots, (iii) protocells, and (iv) prototissues. For each system it will provide key examples and highlight how the emergent behaviour could be chemically programmed.

Unsaturated polyester resin based composites: A case study of lignin valorisation

Materials from green resources boast a low carbon footprint, forming the foundation of the circular economy approach in materials science. Thus, in this study, waste poly(ethylene terephthalate) (PET) was subjected to depolymerization using propylene glycol (PG), and subsequent polycondensation with bio-based maleic anhydride (MA) produced unsaturated polyester resin (b-UPR). Bio-derived acryloyl-modified Kraft lignin (KfL-A) served as a vinyl reactive filler in the b-UPR matrix to create b-UPR/KfL-A composites. The structural characterization of KfL-A and b-UPR involved the use of FTIR and NMR techniques. The mechanical properties of the newly fabricated composites were assessed through tensile strength, Vickers microhardness, and dynamic mechanical tests. The addition of KfL-A to the rigid b-UPR matrix enhanced material flexibility, resulting in less stiff and hard materials while preserving composite toughness. For instance, incorporating 10 wt% of KfL-A in b-UPR led to a 17% reduction in hardness, a 48% decrease in tensile strength, and a 20% reduction in toughness. Positive environmental impact was achieved by incorporation of 64 wt% of renewable and recycled raw material. Analogously prepared b-UPR/KfL composites showed structural inhomogeneity and somewhat better mechanical properties. Transmission (TEM) and scanning (SEM) electron microscopies revealed a suitable relationship between mechanical and structural properties of composites in relation to the extent of KfL-A addition. The UL94V flammability rating confirmed that flame resistance increased proportionally with the KfL-A addition. Once deposited in a landfill, these composites are expected to disintegrate more easily than PET, causing less harm to the environment and contributing to sustainability in the plastics cycle.

Solving Intractable Chemical Problems by Tensor Decomposition.

Many complex chemical problems encoded in terms of physics-based models become computationally intractable for traditional numerical approaches due to their unfavorable scaling with increasing molecular size. Tensor decomposition techniques can overcome such challenges by decomposing unattainably large numerical representations of chemical problems into smaller, tractable ones. In the first two decades of this century, algorithms based on such tensor factorizations have become state-of-the-art methods in various branches of computational chemistry, ranging from molecular quantum dynamics to electronic structure theory and machine learning. Here, we consider the role that tensor decomposition schemes have played in expanding the scope of computational chemistry. We relate some of the most prominent methods to their common underlying tensor network formalisms, providing a unified perspective on leading tensor-based approaches in chemistry and materials science.

Mechanical Properties, Tissue Structure, and Elemental Composition of the Walking Leg Tips of Coconut Crabs

The coconut crab, Birgus latro, has black protrusions on the tops of its walking legs and claw fingers. In addition, there are regularly aligned small black protrusions on parts of the exoskeleton surface of the claws and leg. In this study, the elemental composition, crystal structure, tissue structure, and mechanical properties of these protrusions were studied using a materials science approach, and the results were compared with those of mineralized cuticle. These leg tips were found to be a non-calcified fibrous tissue of α-chitin connected to the mineralized cuticle. The tip of the second walking leg was elongated and had a pointed shape with an oval cavity at its center that was more than 1000 times larger than the pore tubes (100–350 nm) of the mineralized cuticle. It was very soft, with a hardness of 0.4 GPa, corresponding to 11–12% of the hardness of the hard exocuticle and 55–57% of the hardness of the soft endocuticle. The elastic modulus of 8.0 GPa obtained by means of nanoindentation testing was consistent with that of α-chitin fibers of shrimp shells obtained by means of tensile testing. These soft protrusions provide a secure grip on the surfaces of trees or rocks and protect the claw fingertips. It was concluded that the black protrusions are related to a unique ecological (engaging in vertical movements, entering and exiting limestone caves, and escape behavior) aspect of the coconut crab, the largest terrestrial crustacean.

Numerical treatment of reactive diffusion using the discontinuous Galerkin method

AbstractThis work presents a new finite element variational formulation for the numerical treatment of diffusional phase transformations using the discontinuous Galerkin method (DGM). Steep concentration and property gradients near phase boundaries require particular focus on a sound numerical treatment. There are different ways to tackle this problem ranging from (i) the well-known phase field method (PFM) (Biner et al. in Programming phase-field modeling, Springer, Berlin, 2017, Emmerich in The diffuse interface approach in materials science: thermodynamic concepts and applications of phase-field models, Springer, Berlin, 2003), where the interface is described continuously to (ii) methods that allow sharp transitions at phase boundaries, such as reactive diffusion models (Svoboda and Fischer in Comput Mater Sci 127:136–140, 2017, 78:39–46, 2013, Svoboda et al. in Comput Mater Sci 95:309–315, 2014). Phase transformation problems with continuous property changes can be implemented using the continuous Galerkin method (GM). Sharp interface models, however, lead to stability problems with the GM. A method that is able to treat the features of sharp interface models is the discontinuous Galerkin method. This method is well understood for regular diffusion problems (Cockburn in ZAMM J Appl Math Mech 83(11):731–754, 2003). As will be shown, it is also particularly well suited to model phase transformations. We discuss the thermodynamic background by review of a multi-phase, binary system. A new DGM formulation for the phase transformation problem with sharp interfaces is then introduced. Finally, the derived method is used in a 2D microstructural evolution simulation that features a binary, three-phase system that also takes the vacancy mechanism of solid body diffusion into account.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining.

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Digital Characteristics of Microstructure of Diamond—Silicon Carbide Composites

As an example of the implementation of digital materials science approaches based on statistical processing of electron micrographs with the analysis of fractal parameters, the digital characteristics of microstructure of diamond–silicon carbide ceramic composite material are calculated. The lacunarity parameter characterizing the non-uniform distribution of filler particles in the matrix is found. Based on lacunarity values calculated at different scales, scale invariance parameter characterizing the dependence of lacunarity on the scale is evaluated. Voronoi entropy characterizing the structure based on the quantity of information is also calculated and used to determine the average number of neighboring particles and average distance between them. For the composites with high mechanical properties, the number of nearest neighbors approaches six, indicating an almost closest packing.

Investigation of metallurgical properties of Al-Si-Mg casting alloys with integrated computational materials engineering for wheel production

In this study, integrated computational materials engineering, which is one of the new generation approaches in materials science, was used in the production of aluminum alloy wheels by low pressure die casting method. In casting alloys, the efficiency of grain refinement provided by master alloys added to the melt decreases with increasing silicon content of the alloy. In this context, as-cast properties of silicon reduced (Si: 5.0 wt.%) alloys with different Mg ratios (Mg: 3.0, 5.0, 7.0 wt.%) are discussed using integrated computational materials engineering approaches. It has been evaluated whether the examined alloys can be an alternative to the AlSi7Mg0.3 alloy, which is currently used traditionally in the production of aluminum-based wheels, with their microstructural and mechanical properties. The study consists of three stages which are computer-aided production, pilot production, testing and characterization studies. In computer-aided production, original sub-eutectic compositions were determined in types and amounts of alloying elements, alloy designs were realized and a database was created with a computational materials engineering software. Then, low pressure die casting analysis were performed in a virtual environment by transferring these data directly to the casting simulation software. Thus, the microstructural and mechanical properties of the wheel were obtained computationally on the basis of the varying alloy composition. In the second stage, the virtually designed alloy compositions were prepared and sample wheels were manufactured by the low pressure die casting method on an industrial scale. In the testing and characterization phase, spectral analyses, macro and microstructural examinations, hardness measurements and tensile tests were carried out. As a result of this study, it was determined that the studied alloys could be used in the production of wheels by the low pressure die casting method considering the metallurgical properties expected from the wheel. In addition, it is thought that the mathematical design of the material with integrated computational materials engineering approaches before casting simulations will play an active role in the competitiveness and sustainability of the aluminum industry in technological conditions.

Temporal nanofluid environments induce prebiotic condensation in water

Water is a problem in understanding chemical evolution towards life’s origins on Earth. Although all known life is being based on water key prebiotic reactions are inhibited by it. The prebiotic plausibility of current strategies to circumvent this paradox is questionable regarding the principle that evolution builds on existing pathways. Here, we report a straightforward way to overcome the water paradox in line with evolutionary conservatism. By utilising a molecular deposition method as a physicochemical probe, we uncovered a synergy between biomolecule assembly and temporal nanofluid conditions that emerge within transient nanoconfinements of water between suspended particles. Results from fluorometry, quantitative PCR, melting curve analysis, gel electrophoresis and computational modelling reveal that such conditions induce nonenzymatic polymerisation of nucleotides and promote basic cooperation between nucleotides and amino acids for RNA formation. Aqueous particle suspensions are a geochemical ubiquitous and thus prebiotic highly plausible setting. Harnessing nanofluid conditions in this setting for prebiotic syntheses is consistent with evolutionary conservatism, as living cells also work with temporal nanoconfined water for biosynthesis. Our findings add key insights required to understand the transition from geochemistry to biochemistry and open up systematic pathways to water-based green chemistry approaches in materials science and nanotechnology.

The Vindolanda Vessel: pXRF and Microphotography of an Enamel-Painted Roman Gladiator Glass

Roman glass is well studied and known to have been produced from a mineral soda source and calcareous sand with variation between elements relating to naturally occurring minerals in the sands. While the common characteristics of colourants and opacifiers used in opaque and translucent glasses are well understood, the diverse elemental composition of colouring agents associated with the highly specialised, and largely unexplored, technique of enamel-painted glass has never been firmly established. There remains a significant gap in knowledge of pigments used for this technological innovation which is here addressed through the deployment of non-invasive portable X-Ray Flourescence (pXRF) analysis and microphotography on a unique Roman enamel-painted gladiator glass from Vindolanda fort. This vanguard research has successfully established, for the first time, a palette of pigments associated with this specialist technique. It is now possible to unravel previously unknown information on complex manufacturing processes and significantly expand the repertoire of the pigments bound up in enamelling recipes used to depict the striking iconographic scenes on the Vindolanda vessel and, potentially, other Roman enamelled glassware. The detection of Cinnabar, Egyptian blue, Orpiment and other pigments are ground-breaking discoveries that will have a transformative impact on early glassmaking studies and push the boundaries of scholarship into new directions of analytical approaches in heritage materials science to complement recent success in this field with Raman spectroscopy and other techniques. The methodology is unprecedented and has been validated through the high quality of the resulting data which permits the extrapolation of elemental compositions of enamelling materials from those associated with the base vessel. This unique approach provides remarkable insights that will revolutionise our understanding of enamelling technologies using the Vindolanda vessel as the investigative platform for forgotten practice.

Nano Urea: A Small Solution with a Big Impact on Sustainable Agriculture

Nano urea is a promising alternative to conventional urea for improving nitrogen use efficiency in agriculture. However, the nitrogen holding capacity of nano urea is a critical factor in its effectiveness as a fertilizer. This paper explores various approaches for increasing the nitrogen holding capacity of nano urea, including the use of coatings, encapsulation, and functionalization with polymers or other additives. The results suggest that these strategies can improve the stability and release characteristics of nano urea, enhancing its nitrogen holding capacity and reducing the risk of nitrogen loss through leaching or volatilization. The paper also discusses the challenges and opportunities associated with the development and commercialization of these technologies, including considerations related to safety, scalability, and cost-effectiveness. The findings of this study have implications for the design and optimization of nano urea products, with potential benefits for sustainable agriculture and environmental conservation. Overall, the paper highlights the potential of increasing the nitrogen holding capacity of nano urea through innovative approaches in materials science and engineering. Keywords: Nano urea, encapsulation, additives, scalability, sustainable agriculture, material science.

Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems

Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.

Tailored disorder—materials design for advanced photonic applications: introduction

Tailored disorder represents an emerging design concept that explores the photonic properties of material composites with deliberately introduced irregularities in their geometry and composition. This rapidly developing field of cutting-edge research addresses various aspects from fundamental scientific questions to various subfields of material science required for technological implementations of optimized material applications. The combination of inspiration from biological systems, results from physics, chemical derivatization approaches, and validation from simulation, together with innovative material science and engineering approaches, enables a new design framework for advanced photonic materials with interesting properties. This finally leads to novel custom-made devices for a variety of photonic applications, their performance being related to the tailored disorder within three-dimensional micro- and nanoarchitectures. This feature issue presents most recent and selected developments in the field.

Composite materials for innovative urban farming of alternative food sources (macroalgae and crickets)

Facing an inexorable growth of the human population along with substantial environmental changes, the assurance of food security is a major challenge of the present century. To ensure responsible food consumption and production (SDG 12), new approaches in the food system are required. Thus, environmentally controlled, sustainable production of alternative food sources are of key interest for both urban agriculture and food research. To face the current challenge of integrating food production systems within existing structures, multidisciplinary discourses are required. Here, we bring together novel technologies and indoor farming techniques with the aim of supporting the development of sustainable food production systems. For this purpose, we investigated the feasibility of 10 composite materials for their innovative use as structural support in macroalgal cultivation (settlement substrates) and cricket rearing (housing). Considering material resistance, rigidity, and direct material-organism interactions, the bio-based composite polylactic acid (PLA) was identified as a suitable material for joint farming. For macroalgae cultivation, PLA sustained the corrosive cultivation conditions and provided a suitable substrate without affecting the macroalgal physiology or nutritional composition (carotenoids and chlorophylls). For cricket rearing, PLA provided a suitable and recyclable shelter, which was quickly accepted by the animals without any observed harm. In contrast, other common composite components like phenolic resin or aramid were found to be unsuitable due to being harmful for the cultivated organisms or instable toward the applied sterilization procedure. This multidisciplinary study not only provides profound insights in the developing field of urban indoor food production from a new perspective, but also bridges material science and farming approaches to develop new sustainable and resilient food production systems.

Manipulating Bacterial Biofilms Using Materiobiology and Synthetic Biology Approaches.

Bacteria form biofilms on material surfaces within hours. Biofilms are often considered problematic substances in the fields such as biomedical devices and the food industry; however, they are beneficial in other fields such as fermentation, water remediation, and civil engineering. Biofilm properties depend on their genome and the extracellular environment, including pH, shear stress, and matrices topography, stiffness, wettability, and charges during biofilm formation. These surface properties have feedback effects on biofilm formation at different stages. Due to emerging technology such as synthetic biology and genome editing, many studies have focused on functionalizing biofilm for specific applications. Nevertheless, few studies combine these two approaches to produce or modify biofilms. This review summarizes up-to-date materials science and synthetic biology approaches to controlling biofilms. The review proposed a potential research direction in the future that can gain better control of bacteria and biofilms.