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A Comprehensive Review on plant and animal fiber reinforced composites: Experimental and theoretical approaches to interfacial strength optimization and potential applications

Hybrid Advances

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Hybrid Advances 2025年 10卷

作者： Olanrewaju, Olajesu Oladele, Isiaka Oluwole Adelani, Samson Oluwagbenga Department of Materials Science and Engineering Iowa State University Ames IA United States Department of Metallurgical and Materials Engineering Federal University of Technology PMB Akure 704 Nigeria Centre for Nanomechanics and Tribocorrosion School of Metallurgy Chemical and Mining Engineering University of Johannesburg Johannesburg South Africa Materials Science and Engineering Program University of Colorado Boulder 80303 CO United States

Natural fiber-reinforced composites (NFRCs) have become vital in various engineering applications due to their exceptional properties and ease of manufacturing. Properties such as lightweight, sustainability, design flexibility, microstructure, durability, and advanced fabrication techniques have expanded their use across industries. Also, NFRCs are preferred because the extensive reliance on synthetic fibers presents significant challenges in recycling and waste management. Despite their excellent properties, NFRCs have three main challenges: fiber degradation, water degradation, and weak interfacial strength (incompatibility of fibers with the matrix). Consequently, research efforts have been directed at combating these challenges using different surface treatment techniques. However, research has been skewed towards experimental approaches for improving the interfacial strength in plant fiber polymer matrix composites (PMCs). Hence, there exists a dearth of information on the computational approaches for optimizing the interfacial properties of NFRCs. Hence, this review provides experimental and computational approaches (machine learning) for comprehensive optimizing strategies for different natural fibers (plant, animal, and microorganism) and matrices (polymers, metals, and ceramics). This review also highlights the importance of theoretical approaches and numerical modeling in analyzing and optimizing NFRCs. Finally, the review highlights recent advancements in NFRCs, their mechanical properties, potential applications, and future directions. © 2025 The Authors

关键词： Animal fiber Interfacial shear strength Machine-learning Natural fiber-reinforced composites Plant fiber

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Machine learning of microstructure–property relationships in materials with robust features from foundational vision transformers

arXiv

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arXiv 2025年

作者： Whitman, Sheila E. Latypov, Marat I. Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States

Machine learning of microstructure–property relationships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure–property relationship. We propose utilizing pre-trained foundational vision transformers for the extraction of task-agnostic microstructure features and subsequent light-weight machine learning of a microstructure-dependent property. We demonstrate our approach with pre-trained state-of-the-art vision transformers (CLIP, DINOV2, SAM) in two case studies on machine-learning: (i) elastic modulus of two-phase microstructures based on simulations data;and (ii) Vicker’s hardness of Ni-base and Co-base superalloys based on experimental data published in literature. Our results show the potential of foundational vision transformers for robust microstructure representation and efficient machine learning of microstructure–property relationships without the need for expensive task-specific training or fine-tuning of bespoke deep learning models. © 2025, CC BY.

关键词： Elastic moduli

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Recent advances in natural fiber reinforced metal/ceramic/polymer composites: An overview of the structure-property relationship for engineering applications

Hybrid Advances

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Hybrid Advances 2025年 8卷

Advances in the material science world drive the birth of a new generation of sustainable materials. Natural fiber (NFs) biocomposites are emerging as valuable alternatives to conventional materials for engineering applications such as construction, automobiles, and aerospace due to their exceptional mechanical properties, lightweight, low carbon footprint, energy efficiency, and cost-effectiveness. These outstanding properties have made them more desirable for engineering applications. However, the focus has been on lignocellulosic fibers, neglecting other sources, such as keratinous, fibrinous, and microorganism fibers. Only a few reviews have considered the structure-property relationship of all classes of natural fibers, such as lignocellulosic (plant), keratinous and fibrinous (animal), and microorganism (mycelium) fibers. Although NFs have outstanding potential from an economic, technical, environmental, and green credentials perspective, they have limitations, such as fiber-matrix incompatibility and moisture damage. These limitations make using NFs as reinforcement for different material systems challenging. Surface alterations are usually necessary to improve the adherence between fiber and matrix. With the upgrade, NFs have the potential as sustainable replacement for conventional materials for engineering applications. Future research should aim at reconciling the mechanical properties of NFs to improve composite quality and fiber-matrix adherence and performance. © 2025 The Authors

关键词： Biocomposites Fibrinous Keratinous Lignocellulosic Microorganism Natural fiber

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Machine Learning for Identifying Grain Boundaries in Scanning Electron Microscopy (SEM) Images of Nanoparticle Superlattices

arXiv

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arXiv 2025年

作者： Paruchuri, Aanish Thrasher, Carl Hart, A.J. Macfarlane, Robert Jayaraman, Arthi Science in Data Science Program University of Delaware NewarkDE19713 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering University of Delaware 150 Academy St NewarkDE19713 United States Department of Materials Science and Engineering University of Delaware NewarkDE19713 United States Data Science Institute University of Delaware NewarkDE19713 United States

Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes. © 2025, CC BY.

关键词： Grain boundaries

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Polarization switching in ferroelectric HfO2 from first-principles lattice mode analysis

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Physical Review B 2025年第13期111卷 134106-134106页

作者： Yubo Qi Sobhit Singh Karin M. Rabe Department of Physics University of Alabama at Birmingham Birmingham Alabama 35294 USA Department of Mechanical Engineering University of Rochester Rochester New York 14627 USA Materials Science Program University of Rochester Rochester New York 14627 USA Department of Physics and Astronomy Rutgers University Piscataway New Jersey 08854 USA

In this paper, we introduce a first-principles-based lattice mode analysis method to investigate the competition between different polarization switching paths in HfO2. Because the stability of the polar orthorhombic Pca21 phase of HfO2 arises from a trilinear coupling, polarization switching requires the flipping of not only the polar Γ15Z mode, but also at least one zone-boundary antipolar mode. This means that each polarization state has multiple variants, leading to multiple possible switching paths connecting up- and down-polarized states, which can be systemically enumerated within this framework for efficient identification of the optimal switching path. Our lattice mode analysis also explains why the activation energy of propagation of the most widely studied domain-wall structure in HfO2, which requires the reversal of the X2− mode, is much larger than that of propagation of domain-wall structures with a uniform sign for the X2− mode. This approach deepens our understanding of distinctive properties of ferroelectric HfO2 related to polarization switching and domain-wall motion, including sluggish domain-wall motion, robust ferroelectricity in thin films, and the observation that the antipolar Pbca phase can hardly be transformed to the ferroelectric Pca21 phase by an electric field. Our mode analysis method can be more generally applied to any improper or hybrid improper ferroelectric, in which polarization switching requires changes of nonpolar distortions, for systematic and efficient prediction of optimal switching paths and estimation of coercive fields.

关键词： Ferroelectricity

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Lambda Theta Reflectometry for protein biosensing 17

Lambda Theta Reflectometry for protein biosensing

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Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： Klose, Alanna M. Katz, Joseph D. Boni, Robert Miller, Benjamin L. Department of Dermatology University of Rochester RochesterNY14627 United States Materials Science Program University of Rochester RochesterNY14627 United States Laboratory of Laser Energetics University of Rochester RochesterNY14627 United States Department of Biomedical Engineering University of Rochester RochesterNY14627 United States

ISBN: (纸本)9781510684249

Label-free reflectometry sensors detect proteins by measuring the reflectance properties of a thin film on a substrate. As light encounters a thin film layer stack, reflections from the layer boundaries interfere with each other encoding information about layer properties onto the reflected light. Existing reflectometric techniques measure changes in thin film thickness by observing a change in the interference spectrum, polarization state or reflectance relative to an initial antireflective condition at fixed angle and wavelength. These techniques are used for biosensing by detecting changes in film thickness as analyte binds to surface-bound ligand. Existing approaches are limited by low multiplexing capability, limited sensitivity, or by requiring high manufacturing tolerances of the detector substrates. Lambda Theta Reflectometry (LTR) is a novel reflectometric technique that measures changes in film thickness by measuring the location of null reflectivity as a function of wavelength (lambda) and angle of incidence (theta). The substrate is simultaneously illuminated with a range of angles and wavelengths and reflected light is angularly and spectrally resolved. The optical thickness of the thin film is retrieved by fitting the location of the null in lambda theta space. A prototype LTR reflectometer has demonstrated the ability to measure SiO2 layer thickness with milli-Ångstrom precision. LTR measurements of SiO2/Si oxide films are in excellent agreement with spectroscopic ellipsometry for films thickness ranging from 1390-1465 Å. LTR enables sensitive measurements across a range of analyte concentrations without requiring stringent control over initial layer stack thickness. © 2025 SPIE.

关键词： Ellipsometry

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A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion

arXiv

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arXiv 2025年

作者： Vendrell-Gallart, Oriol Negarandeh, Nima Foumani, Zahra Zanjani Amiri, Mahsa Valdevit, Lorenzo Bostanabad, Ramin Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States Materials and Manufacturing Technology Program University of California IrvineCA92697 United States Department of Materials Science and Engineering University of California IrvineCA92697 United States Department of Civil and Environmental Engineering University of California IrvineCA92697 United States

Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the potential to dramatically accelerate process optimization and, in turn, design of next generation materials. A major challenge that impedes the construction of foundation process-property models is data scarcity. To understand the impact of this challenge, and since foundation models rely on data fusion, in this work we conduct controlled experiments where we focus on the transferability of information across different material systems and properties. More specifically, we generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF) where we measure the effect of five process parameters on porosity and hardness. We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties. Through extensive cross-validation studies and probing the GPs’ interpretable hyperparameters, we study the intricate relation among data size and dimensionality, complexity of the process-property relations, noise, and characteristics of machine learning models. Our findings highlight the need for structured learning approaches that incorporate domain knowledge in building foundation process-property models rather than relying on uninformed data fusion in data-limited applications. © 2025, CC BY.

关键词： Data fusion

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Computational inverse design of acoustoplasmonic metasurfaces

arXiv

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arXiv 2025年

作者： Holland, Julia E. Boechler, Nicholas Poulikakos, Lisa V. Department of Mechanical & Aerospace Engineering UC La Jolla San DiegoCA92093-0021 United States Program in Materials Science & Engineering UC La Jolla San DiegoCA92093-0021 United States

Optical and acoustic metasurfaces are two-dimensional arrays of subwavelength elements that locally modulate or phase shift incident waves. Acoustoplasmonic metasurfaces combine the physics of light and sound, producing acoustic wavefronts in response to optical stimuli. Herein, we present a computational inverse acoustoplasmonic metasurface design algorithm for desired optically-generated acoustic wave fields. We consider gold nanoparticles producing spherical acoustic waves in water, and the resulting acoustic wave propagation along the plane containing the nanoparticle array. We demonstrate how our algorithm can be used to design metasurfaces that can be used to achieve complex acoustic wave fields. This includes the design of a single metasurface that produces acoustic wave fields mimicking two different Morse code patterns upon stimulation with two orthogonal polarization states of light. This work provides a new tool for the design of complex optically generated acoustic wavefronts, enabling functionality beyond what would be achievable with off-optical-resonance optoacoustic excitation. Copyright © 2025, The Authors. All rights reserved.

关键词： Acoustic wave propagation

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Textured BaTiO3 nanorod arrays synthesized via hydrothermal method: Enhanced degradation of Rhodamine B with piezocatalysis

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Ceramics International 2025年

作者： Chiu, Yen-Lun Chang, Kao-Shuo Program on Key Materials Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University No.1 University Road Tainan City70101 Taiwan Department of Materials Science & Engineering National Cheng Kung University No.1 University Road Tainan City70101 Taiwan

This paper presents a hydrothermal method for fabricating textured BaTiO3 (BTO) nanorod arrays with high crystallinity on fluorine-doped tin oxide (FTO) substrates by applying a polarization treatment to the samples to explore the feasibility of enhancing their piezoelectric properties. The preparation of highly textured BTO nanorod arrays using a two-step hydrothermal process was investigated in detail, including the spin-coating process for depositing the TiO2 seed layer, the influence of solution acidity on the hydrothermal synthesis of TiO2 nanorod arrays, and the formation mechanism of textured BTO nanostructures. The piezoelectric constants (d33) of the samples were determined using piezoresponse force microscopy. S-BTO(P), which has high crystallinity and preferential orientation, achieves a d33 as high as 85.4 p.m./V after poling, which is 3.2 times higher than that without textured structures and poling. Our findings revealed that chemically controlling and inducing a preferential orientation in the crystal structure of piezoelectric materials with a seed layer and specific hydrothermal conditions can significantly enhance their piezoelectric properties after poling, making them excellent candidates for piezoelectric devices. Especially, this study confirms that the piezoelectric-enhanced charge separation in poled BTO nanorod arrays enables efficient generation of reactive species under ultrasonication, leading to effective degradation of RhB. The strong piezocatalytic performance highlights the material's potential for practical applications in environmental pollutant removal. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Piezoelectricity

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Nucleation effects of coccoliths in portland cement

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Matter 2025年

作者： Beatty, Danielle N. Srubar, Wil V. Materials Science and Engineering Program University of Colorado Boulder 4001 Discovery Drive UCB 027 BoulderCO80303 United States Department of Civil Environmental and Architectural Engineering University of Colorado Boulder 1111 Engineering Drive ECOT 441 UCB 428 BoulderCO80309 United States

This work explores the use of photosynthesized coccoliths (intricate CaCO3 particles) as a nucleation agent or cement replacement in portland cement paste. First, coccoliths were produced and harvested from the marine microalgae Emiliania huxleyi and the particle size, morphology, and mineralogy were characterized. Then, the nucleation effects of E. huxleyi coccoliths as seeding agent (0.5, 1, 3, or 5 wt % additions) and limestone filler (5 and 15 wt % cement replacements) were studied using isothermal conduction calorimetry and compressive strength testing. The results were compared with five commercially available CaCO3 sources used as nucleation agents and fillers. While the high surface area of coccoliths (12.22 m2/g) enhanced water demand, it also enhanced nucleation during cement hydration without accelerating hydration, leading to enhanced early-age strength without detriments to 28-day strength. Results highlight a potential carbon reduction strategy, namely employing photosynthesis as a method of mineral admixture production, for the cement and concrete industry. © 2025 Elsevier Inc.

关键词： Portland cement

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