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Compositional analysis (CA)—identification and quantification of the system constituents—is the most fundamental and decisive approach for investigating the system of interest. Pyrolysis mass spectrometry (MS) with a high resolution of over 10 000 is very effective for chemical identification and is directly applicable to polymer materials regardless of their solubilities. However, it is less helpful for quantification, especially when the references, i.e., pure constituents, are unknown, non-isolable and thus cannot be prepared. To compensate for this weakness, herein we propose reference-free quantitative mass spectrometry (RQMS) with enhanced quantification accuracy assisted by synchronized thermogravimetry (TG). The key to success lies in correlating the instantaneous weight loss from TG with the MS signal, enabling the quantitative evaluation of the distinct ionization efficiency for each fragment individually. The determined ionization efficiencies allow the conversion of MS signal intensities of pyrolyzed fragments into weight abundances. In a benchmark test using ternary polymer systems, this new framework named TG-RQMS demonstrates accurate CA within ±1.3 wt% errors without using any prior knowledge or spectra of the references. This simple yet accurate and versatile CA method would be an invaluable tool to investigate polymer materials whose composition is hardly accessible via other analytical methods. [ABSTRACT FROM AUTHOR]

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa. [ABSTRACT FROM AUTHOR]

Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young's moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young's modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young's modulus. An inverse design framework for 3D Voxelized geometries using a 3D conditional generative adversarial network (3D-CGAN) is proposed. The trained 3D-CGAN is capable of generating 3D Voronoi lattices with the desired target relative density and Young's modulus. The mechanical properties of 3D-CGAN generated Voronoi lattices were experimentally and numerically validated. [ABSTRACT FROM AUTHOR]

Machine learning is extensively used in material research and development, including adhesion technology. However, it requires a large dataset to train the models for optimizing, developing, and designing new adhesives. This study proposes a novel testing machine that enables quick high-throughput measurements of the shear strength of adhesively bonded joints. A small cylindrical butt shear joint (BSJ) specimen placed in a holder was pushed by a metal specimen pusher until failure; during this process, the force and displacement were recorded. This testing machine can be used to quickly conduct the measurement by simply placing the specimen in a holder and pushing it. A comparison of the average shear strength measured by this method and that measured by single-lap shear tests, coupled with stress analysis using finite element simulation suggested that the proposed method can measure the shear strength more accurately, where a higher level of pure shear can be achieved in the adhesive layers with a lower degree of stress concentration and smaller peeling stress at the extremities of the adhesives. This indicates that the shear strength of adhesively bonded joints can be measured quickly using the proposed testing method, thereby facilitating the development of new adhesives using machine learning. [ABSTRACT FROM AUTHOR]

In recent years, there has been considerable research into functional materials inspired by living things. Much attention has been paid to the development of adhesive materials that mimic the adhesive proteins secreted by a mussel's foot. These mussel-inspired materials have superior adhesiveness to various adherents owing to the non-covalent interactions of their polyphenolic moieties, e.g., hydrogen bonding, electrostatic interactions, and even hydrophobic interactions. Various factors significantly affect the adhesiveness of mussel-inspired polymers, such as the molecular weight, cross-linking density, and composition ratio of the components, as well as the chemical structure of the polyphenolic adhesive moieties, such as l-3,4-dihydroxyphenylalanine (l-Dopa). However, the contributions of the position and distribution of the adhesive moiety in mussel-inspired polymers are often underestimated. In the present study, we prepared a series of mussel-inspired alkyl methacrylate copolymers by controlling the position and distribution of the adhesive moiety, which are known as "forced gradient copolymers". We used a newly designed gallic-acid-bearing methacrylate (GMA) as the polyphenolic adhesive moiety and copolymerized it with 2-ethylhexyl methacrylate (EHMA). The resulting forced gradient adhesive copolymer of GMA and EHMA (poly(GMA-co-EHMA), Poly1) was subjected to adhesion and dispersion tests with an aluminum substrate and a BaTiO3 nanoparticle in organic solvents, respectively. In particular, this study aims to clarify how the monomer position and distribution of the adhesive moiety in the mussel-inspired polymer affect its adhesion and dispersion behavior on a flat metal oxide surface and spherical inorganic oxide surfaces of several tens of nanometers in diameter, respectively. Here, forced gradient copolymer Poly1 consisted of a homopolymer moiety of EHMA (Poly3) and a random copolymer moiety of EHMA and GMA (Poly4). The composition ratio of GMA and the molecular weight were kept constant among the Poly1 series. Simultaneous control of the molecular lengths of Poly3 and Poly4 allowed us to discuss the effects on the distribution of GMA in Poly1. Poly1 exhibited apparent distribution dependency with regard to the adhesiveness and the dispersibility of BaTiO3. Poly1 showed the highest adhesion strength when the composition ratio of GMA was approximately 9 mol% in the portion of the Poly4 segment. In contrast, the block copolymer consisting of the Poly3 segment and Poly4 segment with only adhesive moiety 1 showed the lowest viscosity for dispersion of BaTiO3 nanoparticles. These results indicate that copolymers with mussel-inspired adhesive motifs require the proper design of the monomer position and distribution in Poly1 according to the shape and characteristics of the adherend to maximize their functionality. This research will facilitate the rational design of bio-inspired adhesive materials derived from plants that outperform natural materials, and it will eventually contribute to a sustainable circular economy. [ABSTRACT FROM AUTHOR]