Design Of Materials Research Articles

Noise-Aware Active Learning to Develop High-Temperature Shape Memory Alloys with Large Latent Heat.

Shape memory alloys (SMAs) with large latent heat absorbed/released during phase transformation at elevated temperatures benefit their potential application on thermal energy storage (TES) in high temperature environment like power plants, etc. The desired alloys can be designed quickly by searching the vast component space of doped NiTi-based SMAs via data-driven method, while be challenging with the noisy experimental data. A noise-aware active learning strategy is proposed to accelerate the design of SMAs with large latent heat at elevated phase transformation temperatures based on noisy data. The optimal noise level is estimated by minimizing the model error with incorporation of a range of noise levels as noise hyper-parameters into the noise-aware Kriging model. The employment of this strategy leads to the discovery of the alloy with latent heat of -36.08 J g-1, 9.2% larger than the best value (-33.04 J g-1) in the original training dataset within another four experiments. Additionally, the alloy represents high austenite finish temperature (481.71°C) and relatively small hysteresis. This promotes the latent heat TES application of SMAs in high temperature circumstance. It is expected that the noise-aware approach can be convenient for the accelerated materials design via the data-driven method with noisydata.

Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics

Textile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 214=16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics–woven or knitted, weft knit or warp knit, polymeric or metallic–and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN. Statement of significanceTextile structures are rapidly gaining popularity in many biomedical applications including tissue engineering, wound healing, and surgical repair. A precise understanding of their unique mechanical properties is critical to tailor them to their specific functions. Here we integrate mechanical testing and machine learning to automatically discover the best models for knitted polypropylene fabrics. We show that warp knitted fabrics possess a complex symmetry with three distinct microstructural directions. Along these, the behavior is dominated by an exponential linear term that characterize the initial flexibility of the virgin mesh and the nonlinear stiffening as the loops of the fabric tighten. We expect that our technology will generalize naturally to other fabrics and enable the robust discovery of complex anisotropic models for a wide variety of textile structures.

A Two-Phase Hydrogenation Membrane for Contaminants Reduction at High Hydrogen Reagent Utilization Efficiency.

Heterogeneous hydrogenation is surging as a promising strategy for selective removal of water pollutants, yet numerous efforts rely on catalyst design to advance catalytic activity. Herein, we enhanced the mass transfer and the utilization of hydrogen reagent through construction of a two-phase flow-through membrane reaction device (Pd/SiC-MR). Pd/SiC-MR displays high efficiency and selectivity toward removal of multiple pollutants. For instance, rapid (∼0.35 s) and exclusive hydrogenation (>99%) of carbon-chlorine bond in organohalogens were realized at high water flux (220 L/m2/h). More importantly, the two-phase Pd/SiC-MR reaction system achieved 31.4% utilization of hydrogen reagent, 1-3 orders of magnitude higher than those by classical slurry or fixed-bed reactor. The high hydrogenation performance is attributed to the close proximity of the hydrogen source, reactive hydrogen atom, and pollutant under high molecular collision frequency in membrane pores. Our study opens an approach for improved hydrogen reagent utilization while reserving the high pollutant removal efficiency through altering operating conditions, beyond complex material design limitations in hydrogenation water purification.

Digital Mechanical Metamaterial with Programmable Functionality.

Digitization has brought a new era to the world, liberating information from physical media. The material structure-property relation is high-dimensional and nonlinear, and the digitization of structure-property relations may bring unprecedented functional programmability and diversity. Here, a new concept of digital mechanical metamaterial (DMM) is presented, where property design is realized by programming the digital states of the DMM to decouple the design of the structure and property. Transforming the binary stable states of a curved beam to the digital bit, one unit cell of DMM manifests three distinct deformation responses under compression, i.e., compression-twist coupling (CTC), compression-shear coupling (CSC), and pure compression (PC). These deformation modes show notable differences in motion and stiffness, which, by digitally programming a series of DMM, can yield a spectrum of functionalities, including information encryption, stress-strain relation customization, energy absorption in cushioning, effective vibration isolation, and tunable force transmission. This study pioneers a versatile material design paradigm that provides much greater freedom for the property design of intelligent mechanical metamaterials.

Biomimetic Aramid Nanofiber/β-FeOOH Composite Coating for Polypropylene Separators in Lithium-Sulfur Batteries.

Aramid nanofibers (ANFs), with attractive mechanical and thermal properties, have attracted much attention as key building units for the design of high-performance composite materials. Although great progress has been made, the potential of ANFs as fibrous protein mimetics for controlling the growth of inorganic materials has not been fully revealed, which is critical for avoiding phase separation associated with typical solution blending. In this work, we show that ANFs could template the oriented growth of β-FeOOH nanowhiskers, which enables the synthesis of ANFs/β-FeOOH hybrids as composite coatings for polypropylene (PP) separators in Li-S batteries. The modified PP separator exhibits enhanced mechanical properties, heightened thermal performance, optimized electrolyte wettability, and improved ion conductivity, leading to superior electrochemical properties, including high initial specific capacity, better rate capability, and long cycling stability, which are superior to those of the commercial PP separators. Importantly, the addition of β-FeOOH to ANFs could further contribute to the suppression of lithium polysulfide shuttling by chemical immobilization, inhibition of the growth of lithium dendrites because of the intrinsic high modulus and hardness, and promotion of reaction dynamics due to the catalytic effect. We believe that our work may provide a potent biomimetic pathway for the development of advanced battery separators based on ANFs.

A fast forward prediction framework for energy material design based on machine learning method

A fast forward prediction framework for energy material design based on machine learning method

Influence of non-stoichiometric design concepts on the optical properties of Sm-doped Gd2Zr2O7 transparent ceramics

The concept of non-stoichiometric design, as a historically significant strategy in material design, plays a crucial role in the development and innovation of materials. Particularly in the domain of red-orange fluorescence, the importance of the rare earth Sm element has been increasingly emphasized. This study successfully prepared non-stoichiometric Gd2Zr2O7 transparent ceramics doped with Sm using combustion and vacuum sintering. X-ray diffraction analysis indicates that the obtained powders are composed of fine defect fluorite structure grains, while the final ceramics exhibit pyrochlore/defect fluorite phase. Initially, non-stoichiometric design optimizes the optical transmittance of ceramics. The maximum transmittance of the X = -0.1 ceramic exhibits the highest transmittance 82.4% @ 1134 nm. Furthermore, photoelectrons captured by native defects energy levels can transfer to the Sm excited states, optimizing the fluorescence property of ceramics. The fluorescence intensity of the X = 0.8 ceramic is about five times that of the X = 0 ceramic.

Hopping and crawling DNA-coated colloids

Understanding the motion of particles with multivalent ligand-receptors is important for biomedical applications and material design. Yet, even among a single design, the prototypical DNA-coated colloids, seemingly similar micrometric particles hop or roll, depending on the study. We shed light on this problem by observing DNA-coated colloids diffusing near surfaces coated with complementary strands for a wide array of coating designs. We find colloids rapidly switch between 2 modes: They hop-with long and fast steps-and crawl-with short and slow steps. Both modes occur at all temperatures around the melting point and over various designs. The particles become increasingly subdiffusive as temperature decreases, in line with subsequent velocity steps becoming increasingly anticorrelated, corresponding to switchbacks in the trajectories. Overall, crawling (or hopping) phases are more predominant at low (or high) temperatures; crawling is also more efficient at low temperatures than hopping to cover large distances. We rationalize this behavior within a simple model: At lower temperatures, the number of bound strands increases, and detachment of all bonds is unlikely, hence, hopping is prevented and crawling favored. We thus reveal the mechanism behind a common design rule relying on increased strand density for long-range self-assembly: Dense strands on surfaces are required to enable crawling, possibly facilitating particle rearrangements.

Design of carbon materials with controllable morphology for efficient adsorption of Cu(II) ion and electromagnetic wave absorption

Design of carbon materials with controllable morphology for efficient adsorption of Cu(II) ion and electromagnetic wave absorption

VBO3/V2O3@C heterogeneous anode material with high-specific-capacity and high-stability for lithium-ion batteries

The design and synthesis of high-performance anode materials is the key to further improve the performance of lithium-ion batteries. In this study, VBO3/V2O3 heterogeneous material with high crystallinity was prepared in vacuum quartz tubes by a facile high-temperature solid-phase method, and the electron transport capacity of VBO3/V2O3 was further improved by ball-milling with conductive carbon. As a result, the obtained VBO3/V2O3@C heterogeneous anode material delivers an initial discharge specific capacity of 927 mAh·g−1 and good cycling stability. Even at −20 °C, VBO3/V2O3@C still presents a discharge specific capacity of 432 mAh·g−1. In addition, ex-situ XRD and ex-situ XPS measurements reveal the conversion reaction and (de)intercalation reaction mechanism during charge/discharge. This work deepens the understanding of the structure-activity relationship of borate/oxide heterogeneous anode materials.

First principles design of two dimensional TiSSe Janus drug delivery system for nitrosourea.

In this paper, we delved into the exploration of a novel drug delivery platform for nitrosourea, leveraging a Janus-structured two-dimensional material, TiSSe, as the carrier. Our approach was grounded in a comprehensive application of first-principles computational methods. By evaluating the adsorption energies across a spectrum of potential configurations, we demonstrated the favorable attributes of TiSSe as a carrier for nitrosourea. Our in-depth examination of the electronic structure unveiled intriguing insights. The Janus nature of TiSSe imparts distinct adsorption profiles to nitrosourea molecules at the sulfur (S) and selenium (Se) terminated surfaces. This disparity in electronic properties not only facilitates precise detection but also helps the design of more intriguing two-dimensional materials.

In the Educational Nexus: Understanding the Sequential Influence of Big Five Personality Traits, Major Identity, and Self-Esteem on Academic Outcomes through Clustering Algorithms

This study investigates the relationship between the Big Five personality traits, major identity, self-esteem, and academic outcomes in education. It uses clustering techniques to examine the impact of these factors on students’ academic performance. The research reveals unique patterns when considering personality traits, major identity formation, and self-esteem. The findings highlight the importance of considering these factors when understanding academic attainment trajectories. The study uses popular clustering methods like K-means, DBSCAN, and Hierarchical clustering to reveal latent clusters and provide unique profiles with different combinations of major identity orientations, personality traits, and self-esteem levels. The performance of clustering algorithms is also evaluated using standard assessment metrics. The findings offer insights into the sequential influence of these factors on academic outcomes, guiding the design of student-centric learning materials and providing a framework for promoting successful academic results through an all-encompassing strategy for student development.

Optimizing the effect of heat treatment on the mechanical properties (tensile Strength and hardness) of Hyphaene Thebaica nut; A machine learning and Taguchi approach

The demand for innovative and sustainable biomaterials in healthcare and biotechnology has fueled the need for advancements in its engineering performance. However, the limited properties of biomaterials without the integration of synthetic reinforcements or supports pose a challenge. Consequently, there is a growing necessity for the development of full cycle eco-friendly materials. This study focuses on enhancing the mechanical properties of Doum palm nuts through heat treatment for engineering applications. The research employs the Taguchi optimization technique and genetic algorithms to determine the optimal combination of heating temperature and holding time, aiming to achieve the highest tensile strength and hardness. The results showcase the effectiveness of the proposed approach, revealing the optimal tensile strength of 7.32 MPa at a treatment temperature of 50 °C and a holding time of 120 min. Similarly, the optimum hardness of 60.3 Hv is attained at a heat treatment temperature of 100 °C and a holding time of 120 min. The study further investigates chemical changes through Fourier transform infrared analysis and optical micrographs. Regression models for tensile strength and hardness are developed, and the global optimum is found using a genetic algorithm solver. The findings of this study demonstrate significant and remarkable improvements in the mechanical properties of biomaterials, highlighting their potential for demanding engineering applications. The optimized heat treatment parameters provide a path towards enhanced biomaterials performance and offer promising avenues for the development of tougher and more resilient materials. This research contributes to the field of biomaterials engineering, paving the way for the design and production of eco-friendly materials with superior mechanical properties, thus addressing the ongoing demand for innovative and sustainable solutions in various industries.

Photocatalytic scaffolds enhance anticancer performances of bacterial consortium AUN

Microbial metabolism and function are regulated by ambient microenvironment, yet no functional microporous materials are known to regulate anticancer activity of complex bacterial systems. Here we show that a simple scaffold-mediated bacterial culture is useful for enhancing the anticancer therapeutic abilities of a microbial consortium designated as AUN and composed of intratumoural bacteria Proteus mirabilis (A-gyo) and purple photosynthetic bacteria Rhodopseudomonas palustris (UN-gyo). Our discovery of the mechanism by which porous scaffolds influence the bacterial activities of AUN will facilitate further designs of artificial scaffold material for effective treatment against drug-resistant, triple-negative breast cancer models. A polydimethylsiloxane composite artificial scaffold encapsulating light-activatable TiO2 particularly enhanced the therapeutic outcomes of AUN in syngeneic and orthotopic models. These strong anticancer efficacies were synergistically expressed in tumours because of the potent oncolytic ability of AUN with the help of various immune cells. AUN administration to canines showed well tolerability in an exploratory study

Cluster Preface: Synthesis of Energetic Molecules

Haifeng Huang is a professor at Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, P.R. China. He received his Ph.D. in 2012 from Beijing Institute of Technology (BIT) under the guidance of Prof. Zhiming Zhou. Then, he joined Shanghai Institute of Organic Chemistry to start his academic career. His research interests mainly focus on the design and synthesis of new energetic materials. Jun Yang received his Ph.D. at Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences in 2005 under the supervision of Prof. Minzhi Deng. He is now a professor of the Shanghai Institute of Organic Chemistry, CAS. His research interests primarily include the synthesis of energetic materials and energy regulation materials for solid and liquid propellants.

Improving the Lithography Sensitivity of Atomically Precise Tin-Oxo Nanoclusters via Heterometal Strategy.

Tin-oxo clusters are increasingly recognized as promising materials for nanolithography technology due to their unique properties, yet their structural impacts on lithography performance remain underexplored. This work explores the structural impacts of heterometal strategies on the performance of tin-oxo clusters in nanolithography, focusing on various metal dopants and their coordination geometries. Specifically, SnOC-1(In), SnOC-1(Al), SnOC-1(Fe), and SnOC-2 were synthesized and characterized. These clusters demonstrate excellent solubility, dispersibility, and stability, facilitating the preparation of high-quality films via spin-coating for lithographic applications. Notably, this work innovatively employs nano-infrared (nano-IR), neutron reflectivity (NR), and X-ray reflectivity (XRR) measurements to confirm film homogeneity. Upon electron beam lithography (EBL), all four materials achieve 50 nm line patterns, with SnOC-1(In) demonstrating the highest lithography sensitivity. This enhanced sensitivity is attributed to indium dopants, which possess superior EUV absorption capabilities and unsaturated coordination environments. Further studies on exposure mechanisms indicated that Sn-C bond cleavage generates butyl free radicals, promoting network formations that induce solubility-switching behaviors for lithography. These findings underscore the efficacy of tailored structural design and modulation of cluster materials through heterometal strategies in enhancing lithography performance, offering valuable insights for future material design and applications.

Achievable dual-strategy to stabilize Li-rich layered oxide interface by a one-step wet chemical reaction towards long oxygen redox reversibility

Oxygen release and electrolyte decomposition under high voltage endlessly exacerbate interfacial ramifications and structural degradation of high energy-density Li-rich layered oxide (LLO), leading to voltage and capacity fading. Herein, the dual-strategy of CrxB complex coating and local gradient doping is simultaneously achieved on LLO surface by a one-step wet chemical reaction at room temperature. Density functional theory (DFT) calculations prove that stable B–O and Cr–O bonds through the local gradient doping can significantly reduce the high-energy O 2p states of interfacial lattice O, which is also effective for the near-surface lattice O, thus greatly stabilizing the LLO surface. Besides, differential electrochemical mass spectrometry (DEMS) indicates that the CrxB complex coating can adequately inhibit oxygen release and prevents the migration or dissolution of transition metal ions, including allowing speedy Li+ migration. The voltage and capacity fading of the modified cathode (LLO-CrB) are adequately suppressed, which are benefited from the uniformly dense cathode electrolyte interface (CEI) composed of balanced organic/inorganic composition. Therefore, the specific capacity of LLO-CrB after 200 cycles at 1C is 209.3 mA h g–1 (with a retention rate of 95.1%). This dual-strategy through a one-step wet chemical reaction is expected to be applied in the design and development of other anionic redox cathode materials.

NONLINEAR LEARNING PATH: A SYSTEMATIC REVIEW

Students benefit from a nonlinear learning path, also known as a nonsequential learning path, because it allows them to control the pace and sequence of their learning. However, the dynamics of a nonlinear learning path, particularly within an open learning environment like MOOCs, remain underexplored. The current study aims to map out various nonlinear learning paths and explore the potential for personalisation within these environments. Guided by the PRISMA 2020 Statement, we conducted a comprehensive review of 3,418 articles from three databases, focusing on 30 that were relevant to nonlinear learning paths in open learning environments. We discovered that a nonlinear learning path in MOOCs involves path selection, cyclical paths, or skipped paths, all influenced by the design of the MOOC learning materials. In classrooms utilizing an open learning environment, a nonlinear learning path is facilitated by activities such as face-to- face or online discussions, self-study materials, student-created content, project mentoring or coaching, peer feedback, and co-learning activities. Additionally, personalisation, tailored by educators or technology, is key to preventing students from becoming disoriented within these open learning environments. Our findings highlight the importance of promoting nonlinear learning paths in both classrooms and MOOCs, developing learning path recommender systems, and creating supportive MOOC learning materials. Future research should explore students’ perceptions of nonlinear learning paths within an open learning environment, particularly focusing on the integration and impact of MOOC learning materials.

Improving the Lithography Sensitivity of Atomically Precise Tin‐Oxo Nanoclusters via Heterometal Strategy

Tin‐oxo clusters are increasingly recognized as promising materials for nanolithography technology due to their unique properties, yet their structural impacts on lithography performance remain underexplored. This work explores the structural impacts of heterometal strategies on the performance of tin‐oxo clusters in nanolithography, focusing on various metal dopants and their coordination geometries. Specifically, SnOC‐1(In), SnOC‐1(Al), SnOC‐1(Fe), and SnOC‐2 were synthesized and characterized. These clusters demonstrate excellent solubility, dispersibility, and stability, facilitating the preparation of high‐quality films via spin‐coating for lithographic applications. Notably, this work innovatively employs nano‐infrared (nano‐IR), neutron reflectivity (NR), and X‐ray reflectivity (XRR) measurements to confirm film homogeneity. Upon electron beam lithography (EBL), all four materials achieve 50 nm line patterns, with SnOC‐1(In) demonstrating the highest lithography sensitivity. This enhanced sensitivity is attributed to indium dopants, which possess superior EUV absorption capabilities and unsaturated coordination environments. Further studies on exposure mechanisms indicated that Sn–C bond cleavage generates butyl free radicals, promoting network formations that induce solubility‐switching behaviors for lithography. These findings underscore the efficacy of tailored structural design and modulation of cluster materials through heterometal strategies in enhancing lithography performance, offering valuable insights for future material design and applications.

Modulating Ti coordination environment in Ti-containing materials by sulfation for synergizing with Au sites to facilitate propylene epoxidation

Modulating the coordination environment of Ti sites in Ti-containing materials to synergize with Au sites is a crucial issue to boost the process of propylene epoxidation with H2 and O2 to produce propylene oxide (i.e., HOPO), but still remains a great challenge. Herein, we report a facile strategy to tailor the coordination states and electronic properties of Ti sites. The sulfur-decorated Ti-SiO2 (i.e., Ti sites anchored onto silica) with moderate sulfur loading (Ti-SiO2-S-M) can synergize well with Au sites for the HOPO process with high activity, propylene oxide (PO) selectivity, and H2 efficiency, which are superior to those achieved on the referenced Ti-SiO2 synergizing with Au sites. Multiple characterizations, in-situ diffuse reflectance infrared Fourier transform spectroscopy of propylene and PO, PO conversion experiment, and kinetics behaviors demonstrate that the superior advantages of Ti-SiO2-S-M against Ti-SiO2 result from the increased Ti binding energy, improved hydrophobicity, and weakened acidity, which simultaneously facilitate the adsorption of propylene and promote the desorption of PO. This insight reported here could guide the rational design and optimization of Ti-containing materials to synergize with Au catalysts for propylene epoxidation.