Multiple Materials Research Articles

Data-Driven Materials Research and Development for Functional Coatings.

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time-consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data-driven approaches are gaining traction in materials science. In this paper, recent advances in data-driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data-driven processes. It is demonstrated how data-driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data-driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data-driven materials R&D for functional coatings, shedding light on its potential and future developments.

Interface reinforced by polymer binder for expandable carbon fiber structural lithium-ion battery composites

Carbon fiber (CF) composite structural battery (SB) is a novel energy storage device that integrates electrochemical energy storage with mechanical load-bearing capability. Carbon fiber's inherent conjugated carbon network possesses excellent electronic conductivity, thus serving as a current collector for electrode active materials. The bonding between active materials and carbon fibers relies on their manufacturing processes, requiring optimization of their electrochemical performance and addressing the challenges of large-scale production. In this work, a LiFePO4/PEO-LiTFSI/CF composite cathode was fabricated using the direct coating method. Polyethylene oxide (PEO) acted as a binder to form a stable LiFePO4 (LFP) coating on the carbon fiber woven fabric, while multi-walled carbon nanotube (MWCNT) were employed to construct a conductive network. This significantly enhanced both the charge-discharge performance and interface stability of the composite cathode. This composite cathode achieved a first-cycle discharge-specific capacity of 133.21 mAh·g−1 at 0.1 C rate and retained 93.7 % of its capacity after 200 cycles at 1 C rate. Based on this composite cathode, multiple active material coatings were integrated onto a carbon fiber woven fabric to manufacture an expandable multi-cell structural battery. Its advantage lies in the ability of multiple battery cells to disperse stress under load, thus achieving a higher capacity retention rate under bending loads. The structural battery possesses a high degree of customizability, allowing for adjustment of the area, position, and quantity of active material coatings to meet the demands of practical conditions.

Composition driven machine learning for unearthing high-strength lightweight multi-principal element alloys

High-strength, lightweight alloys are highly desired in the aerospace, automotive and marine industries. The optimization of such alloys is a multifaceted process, characterized by the consideration of diverse objectives and constraints. Various optimization methodologies exist, spanning from intricate first-principle approaches to the application of sophisticated machine learning algorithms. These algorithms might incorporate input features encompassing elemental composition, microstructural attributes, and thermodynamic properties to enhance prediction accuracies. In this work, we aim to streamline this complexity by employing solely the alloy's elemental composition as the input feature for the machine learning algorithm, improving the hardness while reducing the density of the alloy. We have curated a comprehensive database comprising 544 multi-principal element alloys and developed a robust surrogate model based on these compositions. This composition-driven model is subsequently coupled with principal component analysis (PCA) to facilitate the selection process. Remarkably, through a mere three iterations involving 14 samples, we successfully identified an alloy with an effective specific hardness surpassing the training database maximum by 8.6 %. The proposed composition-driven machine learning delineates a simplified approach for conducting optimization across multiple target material properties.

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network

The accurate estimation of battery state of health (SOH) is critical for ensuring the safety and reliability of devices. Considering the variation in health degradation across different types of lithium-ion battery materials, this paper proposes an SOH estimation method based on a graph perceptual neural network, designed to adapt to multiple battery materials. This method adapts to various battery materials by extracting crucial features from current, voltage, voltage–capacity, and temperature data, and it constructs a graph structure to encapsulate these features. This approach effectively captures the complex interactions and dependencies among different battery types. The novel technique of randomly removing features addresses feature redundancy. Initially, a mutual information graph structure is defined to illustrate the interdependencies among battery features. Moreover, a graph perceptual self-attention mechanism is implemented, integrating the adjacency matrix and edge features into the self-attention calculations. This enhancement aids the model’s understanding of battery behaviors, thereby improving the transparency and interpretability of predictions. The experimental results demonstrate that this method outperforms traditional models in both accuracy and generalizability across various battery types, particularly those with significant chemical and degradation discrepancies. The model achieves a minimum mean absolute error of 0.357, a root mean square error of 0.560, and a maximum error of 0.941.

Stamping cleaning conception on cross-contamination free multi-ceramic bioglass-tricalcium phosphate core-shell structure fabrication

Multi-ceramic additive manufacturing has always been a topic of discussion, considering that implementing multiple materials in one structure can bring innovative functionalities to it. However, the realization of the fabrication system is still plagued by cross-contamination when alternating materials. Therefore, we developed a new machine and suggested a stamping cross-contamination prevention system to address this issue. The contaminant removal mechanism was understood, efficiency was improved, and the stamping process parameters were optimized. To demonstrate, a bone hierarchical structure naturally modeled with advanced functionality to withstand mechanical loads and assist metabolic reactions was replicated. The material combination chosen included chemically identical calcium phosphates and bioglass. First, specific properties such as compressive strength were measured, and in-vitro bioactivity was evaluated to assign appropriate roles. Subsequently, the functionality of the hierarchical structure was controlled and fabricated using the previously optimized parameters. Finally, cross-contamination and multi-functionality of the generated structure were confirmed again for conclusion.

Why heterogeneous cloud particles matter. Iron-bearing species and cloud particle morphology affect exoplanet transmission spectra

The possibility of observing spectral features in exoplanet atmospheres with space missions like the James Webb Space Telescope (JWST) and Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) necessitates the accurate modelling of cloud particle opacities. In exoplanet atmospheres, cloud particles can be made from multiple materials and be considerably chemically heterogeneous. Therefore, assumptions on the morphology of cloud particles are required to calculate their opacities. The aim of this work is to analyse how different approaches to calculate the opacities of heterogeneous cloud particles affect the optical properties of cloud particles and how this may influence the interpretation of data observed by JWST and future missions. We calculated cloud particle optical properties using seven different mixing treatments: four effective medium theories (EMTs; Bruggeman, Landau-Lifshitz-Looyenga (LLL), Maxwell-Garnett, and Linear), core-shell, and two homogeneous cloud particle approximations. We conducted a parameter study using two-component materials to study the mixing behaviour of 21 commonly considered cloud particle materials for exoplanets. To analyse the impact on observations, we studied the transmission spectra of HATS-6b, WASP-39b, WASP-76b, and WASP-107b. Materials with large refractive indices, like iron-bearing species or carbon, can change the optical properties of cloud particles when they comprise less than 1<!PCT!> of the total particle volume. The mixing treatment of heterogeneous cloud particles also has an observable effect on transmission spectroscopy. Assuming core-shell or homogeneous cloud particles results in less muting of molecular features and retains the cloud spectral features of the individual cloud particle materials. The predicted transit depths for core-shell and homogeneous cloud particle materials are similar for all planets used in this work. If EMTs are used, cloud spectral features are broader and the cloud spectral features of the individual cloud particle materials are not retained. Using LLL leads to fewer molecular features in transmission spectra than when using Bruggeman.

Construction of PANI@V2O5-ZnIn2S4 Z-scheme heterojunction photocatalysts for highly effective degradation of industrial dyes and antibiotics

From an environmental protection, the highly effective photodegradation of organic dyes and antibiotics in wastewater needs to be solved expeditiously. Fortunately, the construction of Z-scheme heterojunction for photocatalytic degradation is considered to be an effective method. In this paper, after the fabrication of V2O5 by calcination and loading of PANI on its surface by aniline polymerization, a novel PANI@V2O5-ZnIn2S4 composite photocatalysts prepared by one-pot synthesis method and used for the degradation of industrial dyes (crystalline violet, Congo red) and antibiotics (tetracycline-TC). Under the optimal photodegradation conditions, the 30 wt% PANI@V2O5-ZnIn2S4 heterojunction photocatalysts resulted in more than 95 % degradation of CR, CV and TC solutions after 32 min of irradiation. The optimal photodegradation conditions of the simulated pollutants in real aqueous environments catalyzed by the as-synthesized photocatalysts were explored via response surface methodology (RSM). Multiple material characterization techniques, such as FESEM, HRTEM, XPS valence band spectrum, photoelectrochemistry, radical trapping experiments and EPR tests verified the existence of Z-scheme heterojunction. Especially, the coating of conductive PANI on the V2O5 surface can further improve the separation and transfer efficiency of photogenerated charge carriers, thereby enhancing the photocatalytic activity of the semiconductor photocatalyst. This study will provide a robust option for the design and construction of Z-scheme heterojunctions photocatalysts for environmental pollution remediation.

Unveiling the Interplay Between Silicon and Graphite in Composite Anodes for Lithium-Ion Batteries.

Si provides an effective approach to achieving high-energy batteries owing to its high energy density and abundance. However, the poor stability of Si requires buffering with graphite particles when used as anodes. Currently, commercial lithium-ion batteries with Si/graphite composite anodes can provide a high energy density and are expected to replace traditional graphite-based batteries. The different lithium storage properties of Si and graphite lead to different degrees of lithiation and chemical environments for this composite anode, which significantly affects the performance of batteries. Herein, the interplay between Si and graphite in mechanically mixed Si/graphite composite anodes is emphasized, which alters the lithiation sequence of the active materials and thus the cycling performance of the battery. Furthermore, performance optimization can be achieved by changing the intrinsic properties of the active materials and external operating conditions, which are summarized and explained in detail. The investigation of the interplay based on Si/graphite composite anodes lays the foundation for developing long-life and high-energy batteries. The abovementioned experimental methods provide logical guidance for future research on composite electrodes with multiple active materials.

A behavior three-way decision approach under interval-valued triangular fuzzy numbers with application to the selection of additive manufacturing composites

Additive manufacturing composites, also recognized as three-dimensional (3D) printing composites, are highly anticipated for their potential to replace industrial materials due to the availability of multiple printing processes and optional materials. However, research gaps exist in cognitive deficiencies and psychological behaviors of decision-makers, as well as experimental error effects caused by material testing, resulting in material selection as a challenging issue. Therefore, this study proposes a novel behavior three-way decision model under the interval-valued triangular fuzzy number (IVTFN) to settle the selection issue of 3D printing composites. The research contributions are summarized as follows. First, the IVTFN is presented to account for the impacts of cognitive deficiency and experimental errors, based on which the concepts of information entropy and fuzzy measure are further developed to conduct the criterion weights. In addition, by integrating the prospect theory and regret theory, a framework for constructing the behavioral decision matrix is presented. Moreover, a novel behavior three-way decision model with the perspectives of objective and preference is proposed to classify the decision region. This study presents a comprehensive methodology integrating the three-way decision model and multi-criteria decision-making method to achieve both alternative ranking and alternative classifying. Finally, a research case of 3D printing composites reinforced by continuous hybrid fibers is adopted to illustrate the validity of the methodology. Comparative analysis and sensitivity analysis are also performed. This study offers valuable insights and tools for systematically tackling the 3D printing composite material selection issues.

Performance characterization of an additively manufactured mechanical structure produced in single and multiple materials with varying configurations

Performance characterization of an additively manufactured mechanical structure produced in single and multiple materials with varying configurations

A Computational Study on Utilizing Phase Change Material With a Condenser to Improve air Conditioning System Performance

ABSTRACTThe efficacy of employing multiple cylindrical phase change materials (PCM) to enhance the performance of an air conditioning (AC) unit is examined in this study. The objective of the present study is to examine the effects of combining an AC unit with a cylindrical PCM container configuration on the PCM discharge process and the performance of the AC system. The procedure involves the connection of a heat exchanger with a cold energy storage PCM to the condenser of the AC. During the daytime, the warm surrounding air is cooled and then transmitted to the AC unit's condenser. Four different turbulence models, that is, the SST , standard , Realizable and RNG have been considered for the present computational study. The investigation has been performed for different air flow rates, that is, 33.6, 42, and 49 for a constant inlet air temperature of 308.15 K. The present outcomes indicate that as the flow rate rises, the air temperature inside the domain increases and the solid PCM starts melting. It is noted that complete discharging time for multi‐cylindrical PCM reduces as the air flow rate rises which are around 13.36, 11.03, and 9.94 h for airflow rates of 33.6, 42, and 49 , respectively. The maximum achieved increase in the COP is around 94.49%, 88.68%, and 87.57% at airflow rates of 33.6, 42, and 49 L/s, respectively, for the multi‐cylindrical PCM throughout the summer. It is found that for the same temperature, as the airflow rate rises, the consumed power saving rises.

Dynamic Topology Optimization with Multiple Materials Based on Impedance Mismatching of Wave

Topology optimization with multiple materials studies were commonly adopted to address the issues of complex structures and dynamic loading cases. However, the perturbation excited from the transient load will be propagated and change the local stress level in the structure. Therefore, it is indispensable to consider impedance mismatching of waves at the interfaces of multi-materials layers in the optimization model. In this work, the wave impedance will be regarded as constraint conditions and assessment indicators in the optimization design. Firstly, a multi-material interpolation model using SIMP models is established based on the variable density method. Secondly, the optimization process is applied by combining using Newmark-[Formula: see text] method which is used to solve the dynamic response, an adjoint variable method for sensitivity study, and an optimization criterion method employed to update the design variables. Finally, the simulation examples are used to analyze the influence of impedance mismatching and volume fraction for multilayer dielectric on response. The results indicate that the position and content of the Polymethyl Methacrylate (PMMA) plays a key role in reducing vibration and buffering action.

Unsettling bodies

This article proposes a fundamental new understanding of videographic research as an embodied practice and of the video essay as a “mingled body”: Not only does the video essay fuse multiple film materials and diverging artistic and scientific methods into a new body of media. The video essay also engages the bodies of both its makers and viewers in new and unsettling ways. Via a theoretical discussion of the video essay’s body as well as via two concrete examples of embodied video essays the potentials of videographic research for a more vulnerable, non-normative academia of the future are outlined.

Singularity analysis for V‐notches in a piezoelectric multimaterial and functionally graded piezoelectric materials

AbstractThis study develops a novel singularity analysis method that addresses the limitations posed by piezoelectric material quantity and the variation patterns of property functions in functionally graded piezoelectric materials (FGPMs). Given that piezoelectric composite materials consist of multiple materials, the material properties of FGPMs vary with respect to angle. By introducing the asymptotic assumption that governs the physical fields close to the notch vertex into the static equilibrium equations, a comprehensive set of characteristic equations is formulated. All singularity orders and corresponding characteristic angle functions of the notch are determined by numerically solving the established characteristic equations. The effects of the notch opening angle, piezoelectric material polarization direction, and boundary conditions on the electromechanical field singularity at the notch are assessed. The judicious selection of material variation patterns can alleviate notch singularity in FGPMs.

Numerical study on the combined application of multiple phase change materials and gradient metal foam in thermal energy storage device

Latent heat thermal energy storage (LHTES) offers significant energy-saving benefits, but its application is limited due to the low thermal conductivity of phase change material (PCM). To address this issue, this article studied the combined application of metal foam structures with different porosity gradients and multiple PCMs with different melting points through numerical simulation to accelerate the melting of PCM and enhance system efficiency. The results showed that the application of multiple PCMs improved the heat transfer performance of the system, reducing the complete melting time by 9.18% compared to using a single PCM with uniform metal foam. Based on the multi-PCM storage system with an average porosity of 0.90, this article designed metal foam structures with one-dimensional and two-dimensional porosity gradients and explored their impacts. The results indicated that in the structure with a one-dimensional porosity gradient along the heat flow direction, the positive gradient decreased thermal resistance, further reducing the complete melting time by 6.18%, while the negative gradient increased it by 19.78%. However, the temperature non-uniformity was lowest with the negative gradient and highest with the positive gradient. The optimal two-dimensional porosity gradient multi-PCM storage model not only reduced thermal resistance but also effectively solved the issue of uneven melting, reducing the complete melting time by 17.96% and increasing the energy storage efficiency by 20.16% compared to the single PCM system with uniform porosity. Furthermore, the article conducted a dimensionless analysis of the optimal structure and different gradient structures, establishing formulas for the liquid fraction concerning modified Fourier number, modified Stefan number, and modified Rayleigh number.

Highly sensitive flexible pressure sensor based on laser ablated Ag-MWCNT /MXene for human motion detection

The complex and dynamic human environment, higher requirements are put forward for the high-performance flexible pressure sensors. The materials selection and microstructure design are crucial to achieve high sensitivity and stability. In this work, we fabricated pressure sensitive layer with multiple dimensional materials. Using laser ablation in liquid method, Ag nanoparticles were uniformly distributed in surface of multi-walled carbon nanotubes (MWCNT). Combined with the delaminated MXene, highly pressure sensitive composite could be prepared due to the reduction of contact resistance under pressure. The corresponding piezoresistive sensors showed high sensitivity (12.7 kPa−1), excellent response speed (150 ms), recovery speed (100 ms), and distinguished cycle stability (3000 cycles). Based on the performance of the sensor, it can realize the monitoring of tiny pressure on the human body, and further realize the detection of human activities and health. In addition, the sensor array has spatial recognition capability, providing a way of thinking for the development needs of future intelligent wearable electronic devices. The sensors can measure changes in resistance caused by finger bending, slight breathing, pulse, vocal cord vibration, and other movements to realize human activity and health monitoring. At the same time, the 3×3 sensor array system can recognize the layout of pressure in space. This simple preparation and low cost of the sensor can Facilitate the progress towards sophisticated wearable electronic gadgets.

Copper-Catalysed Synthesis of (E)-Allylic Organophosphorus Derivatives: A Low Toxic, Mild, Economical, and Ligand-Free Method.

Organophosphorus compounds are fundamental for the chemical industry due to their broad applications across multiple sectors, including pharmaceuticals, agrochemicals, and materials science. Despite their high importance, the sustainable and cost-effective synthesis of organophoshoryl derivatives remains very challenging. Here, we report the first successful regio- and stereoselective hydrophosphorylation of terminal allenamides using an affordable copper catalyst system. This reaction offers an efficient protocol for the synthesis of (E)-allylic organophosphorus derivatives from various types of P-nucleophiles, such as H-phosphonates, H-phosphinates, and secondary phosphine oxides. Key advantages of this ligand-free and atom-economic strategy include low toxicity of the Cu-based catalyst, cost effectiveness, mild reaction conditions, and experimental simplicity, making it competitive with methods that use toxic and expensive Pd-based catalysts. In an effort to comprehend this process, we conducted extensive DFT calculations on this system to uncover the mechanistic insights of this process.

Infrastructure Transitions in Southern Cities: Organising Urban Service Delivery for Climate and Development

Rapidly growing cities in the Global South are characterised by high levels of vulnerability and informality and are expected to bear a disproportionate share of the costs of a changing climate. The confluence of climate change impacts, inequitable urbanisation processes, and under-development emphasise the need for accelerated urban transitions in Southern cities, yet mainstream theories of urban sustainability transitions have been shown to be insufficient for such contexts. This is particularly relevant with regard to urban infrastructure: While mainstream urban theory tends to regard infrastructure as static, centralised, and heavily engineered, infrastructure configurations in cities of the Global South are often heterogeneous, comprising multiple dynamic social and material flows. Drawing on theory from Southern Urbanism and empirical data of unorthodox infrastructures from 14 cities, this article assesses the potential challenges posed by applying a key transitions framework—namely the Multi-Level Perspective—in Southern contexts. The article closes by suggesting a set of theoretical propositions for future conceptual and empirical research that could advance transitions literature more broadly, and highlights the need for all cities to pursue inclusive service delivery models that are responsive to the complex and shifting landscape of climate impacts.

Gaia and the repositioning of the state territoriality: A dialogue with critical geopolitical ecology

Recent discussions of critical geopolitical ecology provide both a theoretical framework and a methodological approach to the dialogue between state territoriality and the Gaia politics. Specifically, I highlight the intersection of terrestrial territories and critical geopolitical ecology with three key terms, partition, share, and multiple materialities, to reposition the state territoriality in the Gaia politics. Based on the discussions, I argue for an alternative territorial subjectivity for the future Earth.