Hasil untuk "Materials Science"

Z. Seh, J. Kibsgaard, Colin F. Dickens et al.

M. M. Ali, Feng Li, Zhiqing Zhang et al.

Xiaogang Li, Dawei Zhang, Zhiyong Liu et al.

H. Bunge

A. Schwartz, Mukul Kumar, B. Adams

W. Callister, D. Rethwisch

M. Zare, K. Mikkonen

Phase change materials (PCMs) are a class of thermo‐responsive materials that can be utilized to trigger a phase transition which gives them thermal energy storage capacity. Any material with a high heat of fusion is referred to as a PCM that is able to provide cutting‐edge thermal storage. PCMs are commercially used in many applications like textile industry, coating, and cold storage typically for heat control. These intriguing substances have recently been rediscovered and employed in a broad range of life science applications, including biological, human body, biomedical, pharmaceutical, food, and agricultural applications. Benefiting from the changes in physicochemical properties during the phase transition makes PCMs also functional for barcoding, detection, and storage. Paraffin wax and polyethylene glycol are the most commonly studied PCMs due to their low toxicity, biocompatibility, high thermal stability, high latent enthalpy, relatively wide transition temperature range, and ease of chemical modification. Current challenges in employing PCMs for life science applications include biosafety and/or engineering difficulties. The focus of this review article is on the life science applications, evaluation, and safety aspects of PCMs. Herein, the advances and the potential of employing PCMs as a versatile platform for various types of life science applications are highlighted.

Qiguang Liu, Yanyun Li, Zhenghao Wu et al.

ABSTRACT In the era of artificial intelligence (AI)‐driven high‐performance computing, phase change materials (PCMs) are critical for high‐flux thermal management. PCMs are evolving toward high enthalpy, low interfacial thermal resistance (ITR), and high reliability. Herein, we design double‐brush phase‐change polymer (PVBS‐TMCn) crosslinked by B─O─B and Si─O─B dynamic bonds, characterized by the ultra‐fast relaxation time of 0.8 s under 80°C and closed‐loop cycling. This architecture enhances the content of phase‐change units for elevated theoretical enthalpy, while inherent multiple dynamic bonds and ultra‐low entanglement minimize enthalpy loss, resulting in a record enthalpy of 240.7 J·g−1. Furthermore, a composite of flexibility PVBS‐TMC14/24 and graphene foam films (PVBS‐TMC/GF) is fabricated as thermal interface materials using a stacking‐cutting strategy, which self‐adaptively modulates low‐ITR in response to temperature, owing to phase transition properties, ultra‐low modulus, and adaptive filling capability of dynamic polymer matrix. PVBS‐TMC/GF significantly generates better thermal management efficiency compared to commercial products. The topology design of double‐brush polymer dynamic networks and interfacial contact mechanisms provide fundamental insights for developing phase‐change adaptive materials and advancing thermal management.

Max Großmann, Malte Grunert, Erich Runge

In materials science, data are scarce and expensive to generate, whether computationally or experimentally. Therefore, it is crucial to identify how model performance scales with dataset size and model capacity to distinguish between data- and model-limited regimes. Neural scaling laws provide a framework for quantifying this behavior and guide the design of materials datasets and machine learning architectures. Here, we investigate neural scaling laws for a paradigmatic materials science task: predicting the dielectric function of metals, a high-dimensional response that governs how solids interact with light. Using over 200,000 dielectric functions from high-throughput ab initio calculations, we study two multi-objective graph neural networks trained to predict the frequency-dependent complex interband dielectric function and the Drude frequency. We observe broken neural scaling laws with respect to dataset size, whereas scaling with the number of model parameters follows a simple power law that rapidly saturates.

Jonathan Staaf Scragg

Self-driving laboratories (SDLs), by combining automation with machine learning-guided experiment selection, have the potential to transform experimental materials science. To date, most SDLs have been optimisation-driven, designed to rapidly converge on performance metrics, by embedding multiple mechanistic layers within platform-specific surrogate models. Such approaches excel at process tuning yet offer limited insight into the underlying physics governing synthesis-property relationships. Here we articulate a complementary paradigm: the exploration-driven, or scientific, SDL, whose primary purpose is the generation of data for data-driven science. We exemplify this concept for the case of inorganic optoelectronic materials, arguing that defect physics, which forms the central mechanistic link between synthesis conditions and functional properties, provides the foundation for designing a suitable SDL. Because defect populations and their spatial organisation cannot generally be resolved directly - nor fully predicted from first principles - the task of the SDL is to generate datasets in which thermodynamic and kinetic synthesis variables are systematically perturbed and defect-sensitive observables measured in parallel. From this basis, we propose a set of design principles for scientific SDLs that will enable them to operate close to the physics of optoelectronic materials, thereby generating transferrable and reusable datasets offering radical insight. We use Cu2ZnSn(S,Se)4 as a case study, both to show the scale of the task of defect-aware materials exploration as well to highlight as the deficiencies in the current paradigm. We propose that properly designed SDLs can generate the structured datasets necessary to enable mechanistic inference and advance synthesis-aware materials design.

Tim Mueller, A. Kusne, R. Ramprasad

Tsuyoshi Ueno, T. Rhone, Z. Hou et al.

Camilo E. Calderon, J. Plata, C. Toher et al.

The Automatic-Flow (AFLOW) standard for the high-throughput construction of materials science electronic structure databases is described. Electronic structure calculations of solid state materials depend on a large number of parameters which must be understood by researchers, and must be reported by originators to ensure reproducibility and enable collaborative database expansion. We therefore describe standard parameter values for k-point grid density, basis set plane wave kinetic energy cut-off, exchange–correlation functionals, pseudopotentials, DFT+U parameters, and convergence criteria used in AFLOW calculations.

Xin Guan, Yang Liu, Lianwei Li et al.

Abstract Particle assembly at liquid–liquid interfaces presents a promising bottom‐up strategy for creating supramolecular materials with advanced functionalities. However, the significantly lower interfacial tension observed in immiscible organic phases compared to traditional oil–water systems has hindered the effective adsorption and assembly of particles at oil–oil interfaces. In this work, a versatile and effective strategy is presented that utilizes the assembly and jamming of microgels and polymer ligands at non‐aqueous liquid–liquid interfaces to create non‐aqueous Pickering emulsions and reconfigurable droplet networks. The resulting microgel‐polymer complexes form an asymmetric interfacial bilayer with high surface coverage, which effectively minimizes interfacial energy and improves interfacial elasticity. Through a combination of systematic interfacial measurements and molecular dynamics simulations, the underlying mechanisms governing interfacial self‐assembly are elucidated. Notably, the stimuli‐responsive nature of the microgel‐polymer complexes allows for precise control over the interfacial assembly and disassembly by introducing competitive molecules. Furthermore, it is demonstrated that these non‐aqueous Pickering emulsions serve as excellent templates for the fabrication of heterogeneous organogels and microgel‐based colloidosomes through both covalent and non‐covalent crosslinking strategies. This work underscores the potential of non‐aqueous interfaces in advancing materials science and opens new avenues for developing multifunctional materials.

Krina Parmar, Pauline Dufour, Emma Texier et al.

Abstract The nature of lead zirconate, the historical antiferroelectric material, has recently been challenged. In PbZrO3 epitaxial films, thickness reduction engenders competition among antiferroelectric, ferrielectric and ferroelectric phases. All studies so far on PbZrO3 films have utilized commercially-available oxide single crystals with large compressive lattice mismatch, causing the films to undergo strain relaxation. First-principles calculations have predicted that tensile strain can stabilize antiferroelectricity down to the nanometre scale. Here we use tensile strain imposed by artificial substrates of LaLuO3 to stabilize a pure antiferroelectric phase in PbZrO3. Sharp double hysteresis loops of polarization vs electric field show zero remanent polarization, and polar displacement maps reveal the characteristic up-up-down-down antipolar pattern down to 9 nanometre film thicknesses. Moreover, the electron beam can move this antipolar pattern through the nucleation and annihilation of translational boundaries. These results highlight the critical role of coherent epitaxial strain in the phase stability of PbZrO3.

Exsaris Januar, Nurhizrah Gistituati , Yanti Fitria et al.

Background/purpose. Study addresses the problem of low scientific and digital literacy among Grade 6 students (Phase C) in the IPAS (Science and Social Studies) subject under the Merdeka Curriculum, particularly in the theme “Our Endangered Earth.” The main purpose of the study is to develop and evaluate the EXARIS learning model (Exploration, Analysis, Reflection, Integration, Sharing) as an innovative solution that enhances both scientific and digital literacy, aligned with 21st-century learning demands and curriculum goals. Materials/methods. The EXARIS model was developed using a systematic instructional design approach based on the ADDIE framework (Analysis, Design, Development, Implementation, and Evaluation). Validation was conducted through expert review, while practicality was assessed by classroom teachers. The model's effectiveness was measured using a quasi-experimental design with a Paired Samples T-Test dan Anacova One Way. Results. The expert validation showed a high level of validity (average score = 3.85 on a 4-point scale). Practicality testing indicated that teachers found the model easy to implement (average score = 3.70). Effectiveness testing revealed significant improvements in students’ digital literacy (Mean Difference = 1.79, p < 0.001) and scientific literacy (Mean Difference = 1.65, p < 0.05). Conclusion. The EXARIS learning model is valid, practical, and effective for improving scientific and digital literacy among Grade 6 students in the IPAS subject within the Merdeka Curriculum. This model offers an innovative pedagogical strategy to strengthen students' foundational competencies and meet the needs of 21st-century education.

Xiu-An Ye, Shu-Hong Lin, Liang-Yu Chen et al.

Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), necessitates early diagnosis for effective patient care. Despite advancements in TB diagnostics, there remains an urgent need to discover innovative non-sputum-based methods to detect Mtb-specific antigens for TB patient identification. We have developed a polymer-based electrochemical biosensor for detecting an Mtb-specific antigen, the 6-kDa early secreted antigenic target (ESAT-6), in blood. Using a gold electrode (Au), the biosensor is created by electropolymerizing poly(3,4-ethylene dioxythiophene) with carboxyl groups (PEDOT-COOH), which is activated with 3-ethylcarbodiimide hydrochloride and N-hydroxysuccinimide (EDC-NHS), conjugated with an ESAT-6 polyclonal antibody (Ab), treated with bovine serum albumin (BSA) to block non-specific binding, forming BSA/Ab-EDC-NHS/PEDOT-COOH/Au. Using differential pulse voltammetry measurements, the electrode demonstrated an excellent linear response (R2 = 0.99) for ESAT-6 detection across a concentration range of 24.2 pM (0.81 ng/mL) to 50 nM (1.69 μg/mL), with a low detection limit of 1.39 pM (0.047 ng/mL) and a rapid detection time of under 4 min. This biosensor for ESAT-6 detection effectively distinguished pulmonary TB patients from healthy individuals, achieving 95.0 % sensitivity and 100 % specificity at a cut-off value of 97.0 ng/mL. It demonstrated a diagnostic accuracy of 97.1 %, outperforming the 82.9 % achieved by a commercial ELISA kit. Moreover, biosensor-detected ESAT-6 levels were significantly higher in smear-positive TB patients compared to the smear-negative group (p = 0.014), whereas ELISA-based detection showed no significant difference (p = 0.197). In conclusion, the PEDOT-COOH biosensor enables rapid and effective detection of plasma ESAT-6, facilitates TB diagnosis, and correlates with Mtb bacterial burden, highlighting its potential for disease monitoring.

Ming Han, Ge Sun, Paul F. Nealey et al.

Abstract Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.

Caichao Ye, Tao Feng, Weishu Liu et al.

New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the "composition-microstructure" paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird's-eye view of the research paradigm evolution from early "process-structure-properties-performance" to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation.

Kazuyoshi Yoshimi, Yuichi Motoyama, Tatsumi Aoyama et al.

The Institute for Solid State Physics (ISSP) at The University of Tokyo has been carrying out a software development project named ``the Project for Advancement of Software Usability in Materials Science (PASUMS)". Since the launch of PASUMS, various open-source software programs have been developed/advanced, including ab initio calculations, effective model solvers, and software for machine learning. We also focus on activities that make the software easier to use, such as developing comprehensive computing tools that enable efficient use of supercomputers and interoperability between different software programs. We hope to contribute broadly to developing the computational materials science community through these activities.