Thermogravimetry-synchronized, reference-free quantitative mass spectrometry for accurate compositional analysis of polymer systems without prior knowledge of constituents.

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.

Decoding thermal properties in polymer-inorganic heat dissipators: a data-driven approach using pyrolysis mass spectrometry.

Polymeric materials can boost their performances by strategically incorporating inorganic substances. Heat dissipators are a representative class of such composite materials, where inorganic fillers and matrix polymers contribute to high thermal conductivity and strong adhesion, respectively, resulting in excellent heat dissipation performance. However, due to the complex interaction between fillers and polymers, even slight differences in structural parameters, e.g. dispersion/aggregation degree of fillers and crosslink density of polymers, may significantly impact material performance, complicating the quality management and guidelines for material developments. Therefore, we introduce pyrolysis mass spectra (MS) as material descriptors. On the basis of these spectra, we construct prediction models using a data-driven approach, specifically focusing on thermal conductivity and adhesion, which are key indicators for heat dissipating performance. Pyrolysis-MS observes thermally decomposable polymers, which occupy only 0.1 volume fraction of the heat dissipators; nevertheless, the physical states of non-decomposable inorganic fillers are implicitly reflected in the pyrolyzed fragment patterns of the matrix polymers. Consequently, pyrolysis-MS provides sufficient information to construct accurate models for predicting heat dissipation performance, simplifying quality management by substituting time-consuming performance evaluations with rapid pyrolysis-MS measurements. Furthermore, we elucidate that higher crosslinking density of the matrix polymers enhances thermal conductivity. This data-driven method promises to streamline the identification of key functional factors in complex composite materials.

Using pyrolysis-MS as a material descriptor allows for the prediction of composite materials' heat dissipation capabilities and the identification of key factors influencing these properties.

Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning.

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.

Text-to-Microstructure Generation Using Generative Deep Learning.

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although recent advances in deep generative networks have enabled the inverse design of material microstructures, most studies involve property-conditional generation and focus on a specific type of structure, resulting in limited generation diversity and poor human-computer interaction. In this study, a pioneering text-to-microstructure deep generative network (Txt2Microstruct-Net) is proposed that enables the generation of 3D material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct-Net model is trained on a large microstructure-caption paired dataset that is extensible using the algorithms provided. Moreover, the model is sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance is also demonstrated in the inverse design of material microstructures and metamaterials. It has promising potential for interactive microstructure design when associated with large language models and could be a user-friendly tool for material design and discovery.

De-icing performance evolution with increasing hydrophobicity by regulating surface topography.

It is of great significance to grasp the role of surface topography in de-icing, which however remains unclear yet. Herein, four textured surfaces are developed by regulating surface topography while keeping surface chemistry and material constituents same. Specifically, nano-textures are maintained and micro-textures are gradually enlarged. The resultant ice adhesion strength is proportional to a topography parameter, i.e. areal fraction of the micro-textures, owing to the localized bonding strengthening, which is verified by ice detachment simulation using finite element method. Moreover, the decisive topography parameter is demonstrated to be determined by the interfacial strength distribution between ice and test surface. Such parameters vary from paper to paper due to different interfacial strength distributions corresponding to respective situations. Furthermore, since hydrophobic and de-icing performance may rely on different topography parameters, there is no certain relationship between hydrophobicity and de-icing.

The role of surface topography in de-icing is verified to be determined by the interfacial strength distribution between ice and surface experimentally and numerically, unveiling the relationship between hydrophobicity and de-icing.

Revealing factors influencing polymer degradation with rank-based machine learning.

The efficient treatment of polymer waste is a major challenge for marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing polymer materials in the future. The small number of available datasets on degradability and the diversity of their experimental means and conditions hinder large-scale analysis. In this study, we have developed a platform for evaluating the degradability of polymers that is suitable for such data, using a rank-based machine learning technique based on RankSVM. We then made a ranking model to evaluate the degradability of polymers, integrating three datasets on the degradability of polymers that are measured by different means and conditions. Analysis of this ranking model with a decision tree revealed factors that dominate the degradability of polymers.

Koopmans' Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules.

Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule. Herein, we optimize the parameters of range-separated functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO) to satisfy Koopmans' theorem. Our results demonstrate the effectiveness of the BO in optimizing functional parameters. Particularly, Koopmans' theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to the experimental UV-absorption values. Furthermore, we prepared an optimized parameter dataset of KTLC-BLYP for over 3000 molecules through BO for satisfying Koopmans' theorem. We have developed a machine learning model on this dataset to predict the parameters of the LC-BLYP functional for a given molecule. The prediction model automatically predicts the appropriate parameters for a given molecule and calculates the corresponding values. The approach in this paper would be useful to develop new functionals and to update the previously developed functionals.

Bioinspired Scalable Lubricated Bicontinuous Porous Composites with Self-Recoverability and Exceptional Outdoor Durability.

Lubricant-impregnated surfaces (LIS) are promising as efficient liquid-repellent surfaces, which comprise a surface lubricant layer stabilized by base solid structures. However, the lubricant layer is susceptible to depletion upon exposure to degrading stimuli, leading to the loss of functionality. Lubricant depletion becomes even more pronounced in exposed outdoor conditions, restricting LIS to short-term lab-scale applications. Thus, the development of scalable and long-term stable LIS suitable for practical outdoor applications remains challenging. In this work, we designed "Lubricated Bicontinuous porous Composites" (LuBiCs) by infusing a silicone oil lubricant into a bicontinuous porous composite matrix of tetrapod-shaped zinc oxide microfillers and poly(dimethylsiloxane). LuBiCs are prepared in the meter scale by a facile drop-casting inspired wet process. The bicontinuous porous feature of the LuBiCs enables capillarity-driven spontaneous lubricant transport throughout the surface without any external driving force. Consequently, the LuBiCs can regain liquid-repellent function upon lubricant depletion via capillary replenishment from a small, connected lubricant reservoir, making them tolerant to lubricant-degrading stimuli (e.g., rain shower, surface wiping, and shearing). As a proof-of-concept, we show that the large-scale "LuBiC roof" retains slippery behavior even after more than 9 months of outdoor exposure.

A data-driven sequencer that unveils latent "codons" in synthetic copolymers.

The recent emergence of sequence engineering in synthetic copolymers has been innovating polymer materials, where short sequences, hereinafter called "codons" using an analogy from nucleotide triads, play key roles in expressing functions. However, the codon compositions cannot be experimentally determined owing to the lack of efficient sequencing methods, hindering the integration of experiments and theories. Herein, we propose a polymer sequencer based on mass spectrometry of pyrolyzed oligomeric fragments. Despite the random fragmentation along copolymer main-chains, the characteristic fragment patterns of the codons are identified and quantified via unsupervised learning of a spectral dataset of random copolymers. The codon complexities increase with their length and monomer component number. Our data-driven approach accommodates the increasing complexities by expanding the dataset; the codon compositions of binary triads, binary pentads and ternary triads are quantifiable with small datasets (N < 100). The sequencer allows describing copolymers with their codon compositions/distributions, facilitating sequence engineering toward innovative polymer materials.

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices.

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.

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.

Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated molecules, CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure organic molecules (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivatives to quinone derivatives. In addition, CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chemical synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional molecules.

Evolution of and Disparity among Biomimetic Superhydrophobic Surfaces with Gecko, Petal, and Lotus Effect.

It is desirable to turn one kind of superhydrophobic (SHPO) surfaces into another by changing surface topography alone and attaining solid surfaces with tunable properties. Herein, gecko-, petal-, and lotus-like SHPO surfaces, composed of ZnO tetrapods and polydimethylsiloxane, are realized by adjusting the roughness factor and length scale of roughness, while keeping the surface chemistry the same. Afterward, water droplet sliding and impacting are investigated. The surfaces behave similarly in spreading but deviate from each other in sliding, receding, jetting, and rebounding due to their different adhesive properties. Moreover, the disparity between surfaces with petal and lotus effects is well explained by Furmidge's and Young-Dupre equations. On the other hand, these formulas fail to elucidate the surface with gecko effect because of its inside sealed air that produces negative pressure upon droplet motion. This paper provides a facile topography evolution path and a manifest correlation between topography and performance in water droplet dynamics for SHPO surfaces with gecko, petal, and lotus effects.

Topological alternation from structurally adaptable to mechanically stable crosslinked polymer.

Stimuli-responsive polymers with complicated but controllable shape-morphing behaviors are critically desirable in several engineering fields. Among the various shape-morphing materials, cross-linked polymers with exchangeable bonds in dynamic network topology can undergo on-demand geometric change via solid-state plasticity while maintaining the advantageous properties of cross-linked polymers. However, these dynamic polymers are susceptible to creep deformation that results in their dimensional instability, a highly undesirable drawback that limits their service longevity and applications. Inspired by the natural ice strategy, which realizes creep reduction using crystal structure transformation, we evaluate a dynamic cross-linked polymer with tunable creep behavior through topological alternation. This alternation mechanism uses the thermally triggered disulfide-ene reaction to convert the network topology - from dynamic to static - in a polymerized bulk material. Thus, such a dynamic polymer can exhibit topological rearrangement for thermal plasticity at 130°C to resemble typical dynamic cross-linked polymers. Following the topological alternation at 180°C, the formation of a static topology reduces creep deformation by more than 85% in the same polymer. Owing to temperature-dependent selectivity, our cross-linked polymer exhibits a shape-morphing ability while enhancing its creep resistance for dimensional stability and service longevity after sequentially topological alternation. Our design enriches the design of dynamic covalent polymers, which potentially expands their utility in fabricating geometrically sophisticated multifunctional devices.

Light-Induced Topological Patterning toward 3D Shape-Reconfigurable Origami.

Shape-reconfigurable materials are crucial in many engineering applications. However, because of their isotropic deformability, they often require complex molding equipment for shaping. A polymeric origami structure that follows predetermined deformed and non-deformed patterns at specific temperatures without molding is demonstrated. It is constructed with a heterogeneous (dynamic and static) network topology via light-induced programming. The corresponding spatio-selective thermal plasticity creates varied deformability within a single polymer. The kinematics of site-specific deformation allows guided origami deployment in response to external forces. Moreover, the self-locking origami can fix its geometry in specific states without pressurization. These features enable the development of shape-reconfigurable structures that undergo on-demand geometry changes without requiring bulky or heavy equipment. The concept enriches polymer origamis, and could be applied with other polymers having similar chemistries. Overall, it is a versatile material for artificial muscles, origami robotics, and non-volatile mechanical memory devices.

Forced Gradient Copolymer for Rational Design of Mussel-Inspired Adhesives and Dispersants.

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.

Environmentally friendly recycling system for epoxy resin with dynamic covalent bonding.

Recycling of epoxy resin and its composites is extremely difficult due to its thermoset nature. In this study, we proposed the environmentally-friendly recycling system of epoxy resin with dynamic covalent bonding in the assistance of cysteine-containing tripeptide, so-called glutathione. The glutathione attached on the epoxy resin and resulted in the cleavage of dynamic disulfide bonds of epoxy resin through thiol-disulfide exchange reaction between the thiol group of glutathione and disulfide bonding of epoxy resin, followed by the scission of epoxy networks. Therefore, the degraded epoxy residue was dissolved into chloroform. Finally, this resulting product could be reused as reagent for preparation the new epoxy materials with approximately 90% of initial mechanical strength via regeneration of disulfide bonding through heating. This work demonstrated the different aspect to understand the decomposition and recycling of thermosetting networks and the wide application under more environmentally friendly condition.

Global snapshot of the effects of the COVID-19 pandemic on the research activities of materials scientists between Spring and Autumn 2020.

We conducted a global survey on the effects of the COVID-19 pandemic on the research activities of materials scientists by distributing a questionnaire on 9 October 2020 with a response deadline of 23 October 2020. The questions covered issues such as access to labs, effectiveness of online conferences, and effects on doctoral students for the period covering the first lockdowns until the relaxation of restrictions in late September 2020 in many countries. The survey also included online interviews with eminent materials scientists who shared their local experiences during this period. The interviews were compiled as a series of audio conversations for The STAM Podcast that is freely available worldwide. Our findings included that the majority of institutes were not prepared for such a crisis; researchers in China, Japan, and Singapore were able to resume research much quicker - for example after approximately one month in Japan - than their counterparts in the US and Europe after the first lockdowns; researchers adapted to using virtual teleconferencing to maintain contact with colleagues; and doctoral students were the hardest hit by the pandemic with deep concerns about completing their research and career prospects. We hope that the analysis from this survey will enable the global materials science community to learn from each other's experiences and move forward from the unprecedented circumstances created by the pandemic.

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning.

Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples) limits the development of chemical intuition from experimentalists, as it constrains the use of machine-learning algorithms for extracting relevant information. We tackle this issue to predict and optimize adhesive materials by combining laboratory experimental design, an active learning pipeline and Bayesian optimization. We start from an initial dataset of 32 adhesive samples that were prepared from various molecular-weight bisphenol A-based epoxy resins and polyetheramine curing agents, mixing ratios and curing temperatures, and our data-driven method allows us to propose an optimal preparation of an adhesive material with a very high adhesive joint strength measured at 35.8 ± 1.1 MPa after three active learning cycles (five proposed preparations per cycle). A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient of determination, root mean square error and mean absolute error of 0.85, 4.0 MPa and 3.0 MPa, respectively. This study demonstrates the important impact of active learning to accelerate the design and development of tailored highly functional materials from very small datasets.

Enhancement of the complex third-order nonlinear optical susceptibility in Au nanorods.

We experimentally determined the dispersion of the complex third-order nonlinear optical susceptibility χ(3) of Au nanorods over a wide bandwidth (370 - 800 nm). Compared to bulk Au, these nanorods exhibit greatly enhanced nonlinearities that can be manipulated by geometrical parameters. Accurately measuring the χ(3) values of nanostructured metals is challenging because χ(3) is strongly influenced by the local field effects. Hence the current published χ(3) values for Au nanorods have huge variations in both magnitude and sign because Z-scan measurements are used almost exclusively. This work combines pump-probe methods with spectroscopic ellipsometry to show that Au nanorods exhibit strong wavelength dependence and enhanced χ(3) in the vicinity of the longitudinal plasmon mode and explains where the regions of SA and RSA exist and how focusing and defocusing affects χ(3). In this context, the results highlight the importance of the dispersion of the quantity χ(3) to design plasmonic platforms for nanophotonics applications.

Durable and Flexible Superhydrophobic Materials: Abrasion/Scratching/Slicing/Droplet Impacting/Bending/Twisting-Tolerant Composite with Porcupinefish-Like Structure.

Superhydrophobic materials with micro/nanotextured surface have attracted tremendous attention owing to their potential applications such as self-cleaning, antifouling, anti-icing, and corrosion prevention. Such a micro/nanotextured surface is a key for high water repellency. However, such a texture is fragile and readily damaged when the material is deformed, scratched, or sliced off. Thus, it is challenging to develop superhydrophobic materials that can sustain high water repellency after experiencing such a mechanical deformation and damage. Here we report abrasion/scratching/slicing/droplet impacting/bending/twisting-tolerant superhydrophobic flexible materials with porcupinefish-like structure by using a composite of micrometer-scale tetrapod-shaped ZnO and poly(dimethylsiloxane). Owing to the geometry of the tetrapod and elasticity of poly(dimethylsiloxane), the composite material exhibits stable water repellency after 1000 abrasion and 1000 bending cycles, or even after their surfaces were sliced off many times. The material maintains superhydrophobicity even under a mechanically deformed state such as bending and twisting. The materials can be painted on a variety of substrates and molded into desired shapes and used in a myriad of applications that require superhydrophobicity.