Multiple Materials Research Articles

Replacement of Sulfur Dioxide in White, Rosé, and Red Wines by a Blend of Tannins Extracted from Multiple Plant Materials

For a long time, sulfur dioxide (SO2) has been the most universally used additive in winemaking. With its wide range of effects, ease of use, and low cost, sulfur dioxide has not yet been completely replaced by any process or substance. Since the expected trend for the near future is to keep reducing the concentration of sulfites, many investigations focus on alternative chemical, biological, or physical processes. This study aims to evaluate the chemical, antioxidant, and sensory impact of a plant-based product used as sulfur dioxide replacement (SDR) in white, rosé, and red wines produced as a result of the application of different vinification protocols. The physicochemical and sensory evaluation of the different wines produced showed that this plant-based product could be a good candidate, but appropriate winemaking treatments and optimization are needed to limit wine defects.

A Machine Learning-Based Model for Multiple Material Density Prediction Developed by Powder Bed Fusion Additive Manufacturing

A Machine Learning-Based Model for Multiple Material Density Prediction Developed by Powder Bed Fusion Additive Manufacturing

Characteristics and Risk Assessment of Heavy Metal Contamination in Arable Soils Developed from Different Parent Materials

This study analyzes the heavy metal pollution in cultivated soils developed from different parent materials in Yunnan Province and assesses their risk levels. The results show significant regional differences in soil heavy metal pollution, greatly influenced by the type of parent material. Cadmium (Cd) pollution is most severe in multiple parent material soil regions, particularly in areas with carbonate and purple rocks, exhibiting a high pollution risk. Other heavy metals such as zinc (Zn), copper (Cu), and lead (Pb) also show varying degrees of enrichment in different parent material zones, posing potential pollution risks. The soil pollution levels of heavy metals were classified using the geo-accumulation index method. It was found that soils developed from carbonate rocks and purple rocks have the most severe heavy metal pollution, while soils from quartzitic rocks, acidic crystalline rocks, and basalt exhibit relatively lower pollution levels. By analyzing the characteristics of heavy metal pollution in different parent materials, this study provides a scientific basis for regional soil pollution management and sustainable agricultural development.

Hybrid III-V/Silicon Quantum Photonic Device Generating Broadband Entangled Photon Pairs

The demand for integrated photonic chips combining the generation and manipulation of quantum states of light is steadily increasing, driven by the need for compact and scalable platforms for quantum information technologies. While photonic circuits with diverse functionalities are being developed in different single material platforms, it has become crucial to realize hybrid photonic circuits that harness the advantages of multiple materials while mitigating their respective weaknesses, resulting in enhanced capabilities. Here, we demonstrate a hybrid III-V/silicon quantum photonic device combining the strong second-order nonlinearity and direct band gap of the III-V semiconductor platform with the high maturity and CMOS compatibility of the silicon photonic platform. Our device embeds the spontaneous parametric down-conversion (SPDC) of photon pairs into an AlGaAs source and their vertical routing to an adhesively bonded silicon-on-insulator circuitry, within an evanescent coupling scheme managing both polarization states. This enables the on-chip generation of broadband (>40 nm) telecom photons by type-0 and type-2 SPDC from the hybrid device, at room temperature and with internal pair generation rates exceeding 105s−1 for both types, while the pump beam is strongly rejected. Two-photon interference with 92% visibility (and up to 99% upon 5-nm spectral filtering) proves the high energy-time entanglement quality of the produced quantum state, thereby enabling a wide range of quantum information applications on chip, within a hybrid architecture compliant with electrical pumping and merging the assets of two mature and highly complementary platforms in view of out-of-the-lab deployment of quantum technologies. Published by the American Physical Society 2024

Multiple Defect-Induced High-Resolution Near-Infrared Mechanoluminescent Materials for Non-Destructive Detection of Blood Glucose and Lipids.

Mechanoluminescence (ML) materials, known for their ability to convert mechanical energy into light, are increasingly recognized for their potential applications, such as in intelligent stress sensing, in vivo bioimaging, and stress non-destructive monitoring. However, the low signal-to-noise ratio (SNR) and narrow-band emission of single-defect-induced ML materials usually limit their biological-related practical applications. Here, these limitations will be addressed by modulating the microstructure evolution in Y3Ga3MgSiO12:Cr3+ through the [Si4++Mg2+] → [Ga3++Ga3+] chemical substitution strategy. Density functional theory (DFT) calculation reveals the defect types and dynamic charge migration processes. In addition, Y3Ga3MgSiO12: Cr3+ with continuously distributed "shallow-deep" defects (0.68-1.61eV) can avoid persistent luminescence (PersL) and bright-field environment interference. Herein, such high SNR near-infrared broadband ML emission may provide a reliable way for high-quality biological non-destructive sensing and detection. Finally, benefiting from the different absorption of ML signals in glucose/lipid, one may find a novel non-invasive blood glucose/lipid testing technology in patients.

Implementation of Jarimatika Method to Improve Multiplication Calculation Ability in Grade III Elementary School Students

This study aims to describe the use of the jarimatika method to improve multiplication calculation skills in grade III students at SD Negeri 2 Surabaya Utara in the 2023/2024 Academic Year. This research approach is a classroom action research with four stages, namely planning, implementation, observation, and reflection. Data collection techniques in this study used observation, tests and documentation. The data obtained from the research results were then analyzed using data analysis consisting of three stages, namely (1) data reduction, (2) data presentation, (3) drawing conclusions. Based on the initial conditions, the first data obtained were only 11 students (60.4%) whose scores were able to reach the KKTP while 14 students (39.6%) whose scores were below the KKTP. After conducting the research, the results obtained showed that in cycle I there were 19 students (72.48) who were able to achieve the KKTP score and 6 (27.52%) students whose scores were below the KKTP. Meanwhile, in cycle II, there were 2 students (19.28) who were below the KKTP and 23 (80.72%) students who were able to achieve the KKTP score. This shows that learning activities using the jarimatika method can improve student learning outcomes in multiplication material.

Multiscale topology optimization for designing locally resonant acoustic metamaterials with specified low-frequency bandgaps

Locally resonant acoustic metamaterials (LRAMs) have significant potential to isolate low-frequency vibrations and noise, whereas designing them with specified bandgaps is challenging. Compared to the monoscale method, the multiscale method can design microstructures that form equivalent materials to act as scatterers, coatings, and matrices. This may aid in obtaining LRAMs that are sometimes difficult to achieve with the monoscale method. Therefore, a multiscale topology optimization method is presented to design LRAMs with bandgaps that meet specified frequency constraints. Since bandgap optimizations are complicated, many local minimum solutions may exist when using sensitivity-based methods. To mitigate this issue, a macro-parameter optimization stage using a genetic algorithm (GA) is added between the macro and micro optimizations in this top-down multiscale framework. As a result, the method comprises three optimization stages: macro-bandgap optimization, macro-parameter optimization, and micro-optimization. Since the macrostructures consist of multiple microstructures and the microstructures consist of multiple materials, a parameterized level-set-based multi-material topology method is adopted to obtain topologies on both scales. The effectiveness of the proposed method is demonstrated through numerical examples of two optimization problems. The first is to obtain LRAMs with the maximum bandgap width while ensuring their bandgaps encompass specified frequency ranges. The other is to obtain LRAMs with specified bandgaps. Successfully obtained LRAMs with specified bandgaps demonstrate the application prospects of this method in designing structures and devices to address issues related to vibrations and noise.

Impact of Graphene Nanoparticles on DLP-Printed Parts' Mechanical Behavior

Digital Light Processing (DLP) is one of the most promising techniques among the additive manufacturing (AM) technologies for polymer resin. The polymer parts produced through this technique demonstrate a diverse range of characteristics that can be specifically designed for various fields of application. Specific attributes can be attained by utilizing polymer composites composed of multiple materials in numerous ratios. This research delves into evaluating and comparing different properties, including microstructure, surface texture, and mechanical behavior, of resin-based polymer composites fabricated using the DLP 3D printing technology. To achieve this, specimens have been printed using photopolymer resin as the base material, with varying percentages of graphene nanoparticles added to the resin. Tensile tests and particle analysis based on optical microscope images validate that optimizing parameters, especially the energy setting of the printer, significantly impact the printed samples' strength, surface texture, layering, and microstructure. The findings indicate that at a specific percentage of graphene, such as 0.5%, there is an increase in tensile strength by 38.1%, Young’s modulus by 54.7%, and Yield strength by 11.2%, accompanied by an improved surface roughness. A graphene concentration of 0.75% results in diminished tensile strength, yield strength, and Young’s modulus. The significance of fine-tuning printing parameters to achieve desired properties in resin-based polymer composites manufactured via 3D printing is highlighted.

Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation.

This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications.

Model-based design of a mechanically intelligent shape-morphing structure

Soft robotics has significant interest within the industrial applications due to its advantages in flexibility and adaptability. Nevertheless, its potential is challenged by low stiffness and limited deformability, particularly in large-scale application scenarios such as underwater and offshore engineering. The integration of smart materials and morphing structures presents a promising avenue for enhancing the capabilities of soft robotic systems, especially in large deformation and variations in stiffness. In this study, we propose a multiple smart materials based mechanically intelligent structure devised through a model-based design framework. Specifically, the intelligent structure incorporates smart hydrogel and shape memory polymer (SMP). Employing the finite element method (FEM), we simulated the complex interactions among smart material to analyze the performance characteristics of the intelligent structure. The results demonstrate that, utilizing smart hydrogel and shape memory polymer (SMP) can effectively attain large deformation and exhibit variable stiffness due to the shape memory effect. Besides, the shape-morphing structures exhibit customized behaviours including bending, curling, and elongation, all while reducing reliance on external power sources. In conclusion, utilizing multiple smart materials within the model-based design framework offers an efficient approach for developing mechanically intelligent structure capable of complex deformations and variable stiffness, thereby providing support for underwater or offshore engineering applications.

Rapid Prototyping of Multiscale Flexible Printed Circuit Boards with NeXus, a Robotic Additive Manufacturing System

Abstract This paper demonstrates the NeXus system, a multi-scale robotic additive manufacturing platform developed at the Louisville Automation and Robotics Research Institute (LARRI), as a rapid prototyping tool through additively manufacturing a multi-layer flexible printed circuit board (FPC) with a printed strain sensor and soldered Surface Mounted Devices (SMD). Manufacturing of the demonstrator requires the application and curing of multiple materials with specialized properties, tools for automated assembly, and software advances to streamline the process enabling the use of industry-standardized programs to command the NeXus system. Additive manufacturing processes supported by the NeXus include Aerosol Jet printing (AJP) for fine feature silver conducting lines, direct write ink-jet printing for insulating materials, and Intense Pulsed Light (IPL) for curing materials between depositions. The NeXus system transports and manipulates parts using a 5-DOF high-precision positioner. Solder paste deposition and pick-and-place (PnP) procedures are performed by a 4-DOF Selective Compliance Articulated Robot Arm (SCARA). Conversion methods between traditional PCB design software and production-ready command scripts were developed to translate basic drawings into command scripts. Multilayer structures with AJP 50-micron wide lines, an insulating bridge with a thickness of around 100 microns, and SMDs soldered to silver AJP pads, were integrated within the demonstrator. An operational amplifier, and other SMDs, reduce the complexity of the accompanying control circuit and amplifies the sensor?s response by 1,830 times. The successful fabrication of the demonstrator FPC highlights the rapid prototyping ability of the NeXus System.

Co-composting: A Comprehensive Review of Its Potential in Transforming Coir Pith Waste into Nutrient-rich Manure

Coir industries in India generates around 7.5Mt of coir waste yearly, which is unsuitable for agricultural use due to its high C:N ratio, lignin, phenol, cellulose and tannin content. The accumulation of coir pith leads to adverse environmental problems due to the release of phenolic compounds, lignin, tannins etc. Biodegradation or composting is the best way to manage coir waste and thereby the environmental pollution can be avoided. Co-composting is a method of decomposing multiple organic materials, which aims to create stable humus, and overcome the limitations of composting with a single organic source. Co-composting of coir pith reduces the C:N ratio, lignin content, cellulose content and increases the nutrient contents. In co-composting technology more than one feed stock are used for composting. It can be done by treating the coir pith with organic source of nutrients viz., farm yard manure or poultry manure or agro-industrial wastes or green manure crops or leguminous weeds or treating the coir pith with organic wastes and earthworms, or treating the coir pith lignin degrading micro-organisms and or supplementing the coir pith with lime, rock phosphate etc. along with the organic sources of nutrients mentioned above to speed up the composting process. Co-composting of coir pith resulted in the production of nutrient rich organic manure within a short period of time. Application of coir pith compost had significant effect on the growth and yield attributes of crops. Harnessing the potential of coir pith can lead us towards a more sustainable and greener future.

3D-printed microfluidic and millifluidic devices for cell culture and mechanotransduction studies: A review

The practice of conventional cell culture techniques affects the quality of biological research in mechanobiology by not replicating the cell’s microenvironment adequately. Microfluidic devices resembling 3D in vivo cell culture offer viable alternatives for mechanobiology studies and other applications including cell screening, cell separation, and point-of-care diagnostics. This review highlights recent advances in additive manufacturing (AM) to fabricate microfluidic and millifluidic devices through fast design iterations and cost reduction compared with conventional device manufacturing. Key advances in AM technologies such as fused deposition modeling (FDM), stereolithography apparatus (SLA), and digital light processing (DLP) have allowed the direct manufacture of complex microfluidic devices with master molds and sacrificial structures using multiple material components. Currently, available 3D printed devices for the precise fabrication of milli- and microfluidic devices, essential to mimicking the cellular microenvironment, while economically reducing production costs by eliminating expensive clean-room environments and reducing material waste are highlighted.

CoupNeRF: Property‐aware Neural Radiance Fields for Multi‐Material Coupled Scenario Reconstruction

AbstractNeural Radiance Fields (NeRFs) have achieved significant recognition for their proficiency in scene reconstruction and rendering by utilizing neural networks to depict intricate volumetric environments. Despite considerable research dedicated to reconstructing physical scenes, rare works succeed in challenging scenarios involving dynamic, multi‐material objects. To alleviate, we introduce CoupNeRF, an efficient neural network architecture that is aware of multiple material properties. This architecture combines physically grounded continuum mechanics with NeRF, facilitating the identification of motion systems across a wide range of physical coupling scenarios. We first reconstruct specific‐material of objects within 3D physical fields to learn material parameters. Then, we develop a method to model the neighbouring particles, enhancing the learning process specifically in regions where material transitions occur. The effectiveness of CoupNeRF is demonstrated through extensive experiments, showcasing its proficiency in accurately coupling and identifying the behavior of complex physical scenes that span multiple physics domains.

PENERAPAN MEDIA PAPAN HITUNG (PAHIT) DALAM PEMBELAJARAN MATEMATIKA PERMULAAN KELAS 2 DI SDN 22 AMPENAN

Teaching aids are objects that are used to assist in the process of conveying information, demonstrations, learning tools, or demonstrations of a particular concept or concepts. In this article the author will explain one of the teaching aids, namely, the Calculation Board. A counting board or PAHIT is a teaching aid used to calculate repeated multiplication, division, addition and subtraction materials. Using this counting board can train children to think quickly and more easily understand the concepts of multiplication and division. At the concrete operational age (7-11 years), elementary school age children generally have difficulty understanding abstract mathematics. Therefore, learning media is needed that can provide solutions to increase students' interest in learning. This research uses descriptive qualitative methods through observation, interviews and documentation. This writing aims to develop calculating board learning media as a medium that can help students' understanding of starting calculations in mathematics subjects. Through this multiplication board media, it is hoped that class II students will be able to think actively in understanding the concepts of multiplication and division so that students can improve their mathematics learning outcomes.Key Word : arithmetic concept, counting board media, mathematics.

Exploring relationships between L1 and L2 writing strategy use and integrated writing performance: A cross-linguistic perspective

Abstract The ability to produce a well-written text in English using multiple material resources, such as integrated writing (IW), is widely recognised as a crucial literacy skill for tertiary students. While previous researchers have extensively examined the factors contributing to second language (L2) English integrated writing, the impact of students’ first-language (L1) Chinese writing strategic knowledge and skills on L2 writing performance has received limited empirical attention. Therefore, this study aimed to investigate the simultaneous relationship between L1 and L2 writing strategy use, L1 writing skills, and L2 IW performance among 239 Chinese university students. Results from structural equation modelling revealed that L1 IW strategy use could predict L2 IW strategy use. Furthermore, the use of L1 IW strategies indirectly predicted L2 IW performance through the use of L2 IW strategies. This study contributes to the existing literature by extending research on cross-language effects in the IW context. Additionally, based on the findings, pedagogical implications for teaching IW across two languages are proposed.

An alloy-agnostic machine learning framework for process mapping in laser powder bed fusion

PurposeThis study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.Design/methodology/approachA convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.FindingsThis study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.Originality/valueThe “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.

AHP VIKOR framework for selecting wind turbine materials with a focus on corrosion and efficiency

The research objective in the context of the study relates to the major concern of corrosion affecting the wind turbines in operation to find materials with high durability in relation to environmental conditions of operation, strength, and cost. A method is an integration of the Analytical Hierarchy Process (AHP) and VIKOR Multi-Criteria Decision Making (MCDM) techniques that will assess seven different material options on sixteen criteria that comprise corrosion resistance, mechanical properties, cost, and a negative environmental impact. From this result, the AHP method calculated the weights for the indicators and chose potential materials, and finally, the VIKOR method used these materials and compared and ranked them to obtain a compromise solution. The research novelty integrates the AHP and VIKOR MCDM methods to address corrosion in wind turbines. By evaluating seven materials against challenging sixteen criteria—including corrosion resistance, mechanical properties, cost, and toxicity, AHP ranks and weights the criteria, while VIKOR identifies the optimal material choice. This dual approach enhances the selection process, ensuring the chosen material improves turbine performance and durability, offering a significant advancement in the sustainable development of wind energy technology. In conclusion, by integrating AHP and VIKOR, it comprehensively evaluates multiple material options based on corrosion resistance, mechanical properties, cost, and environmental impact. This methodology effectively identifies materials that enhance wind turbine performance and extend their lifespan, addressing a critical industry challenge. The alternative exhibits a similarity to the positive ideal solution (Si) of 0.3704 and a relative closeness to the ideal solution (Ri) of 0.0750. Additionally, its priority ranking (Qi) is 0.001, placing it in the first rank for Carbon Fiber Reinforced Polymers (CFRP) within the selection methodology.

PELATIHAN INOVASI MEDIA AJAR INTERAKTIF UNTUK PEMBELAJARAN PERKALIAN SEDERHANA DI SEKOLAH DASAR

Innovations in teaching media are essential for improving the quality of learning, especially in basic mathematical concepts such as multiplication. This article discusses community service activities in the form of training in creating interactive teaching media for simple multiplication learning. The training is aimed at primary school teachers in Rayung Village, Kepohrejo Village, Kudu District, Jombang Regency with the aim of improving their skills in creating technology-based learning media. The method used in this training is Participatory Action Research (PAR). The results of the activity showed that the participants were able to create and use technology-based interactive teaching media well, and were able to increase students' interest and understanding of multiplication materials. In conclusion, the training is effective in providing teachers with new skills and potentially improving the quality of learning in primary schools.

Organophosphate esters (OPEs) in the air and dust of new vehicle cabins: Concentrations, sources and contributions

Vehicles cabin represents unique indoor environment characterized by its limited space and multiple interior materials. For the first time, OPEs levels in vehicles and OPEs content in cabin materials were measured, and the material contributions were also analyzed. Between May and July in 2023, air, dust and materials samples were collected from 32 new vehicles. The detection rates were as high as 71%–100 % in dust and 100 % in selected cabin materials. The median of ∑OPEs in air-phase (both gas and particles) was significantly higher at 589.26 ng/m3 compared to those in other vehicle investigations. Material content analysis revealed that the top three were cushion textile, mat and lubricant, with ∑OPEs ranging from 85.94 μg/g to 1406.04 μg/g, which were three orders of magnitude higher than those in toys. Chlorinated forms such as TCIPP and TDCIPP were predominant among all air, dust and material samples analyzed. Particle-phase OPEs provided statistic diversity on manufactures and seat materials. Positive Matrix Factorization (PMF) analysis found that the combination of cushion textile, seat leather, and carpet substrate textile contributed significantly, accounting for 68 % of cabin OPEs.