Hasil untuk "Materials Science"

Wonhee Ko, Seoung‐Hun Kang, Qiangsheng Lu et al.

Abstract Quantum materials with novel spin textures from strong spin‐orbit coupling (SOC) are essential components for a wide array of proposed spintronic devices. Topological insulators have a necessary strong SOC that imposes a unique spin texture on topological states and Rashba states that arise on the boundary, but there is no established methodology to control the spin texture reversibly. Here, it is demonstrated that functionalizing Bi2Se3 films by altering the step‐edge termination directly changes the strength of SOC and thereby modifies the Rashba strength of 1D edge states. Scanning tunneling microscopy/spectroscopy shows that these Rashba edge states arise and subsequently vanish through the Se functionalization and reduction process of the step edges. The observations are corroborated by density functional theory calculations, which show that a subtle chemical change of edge termination fundamentally alters the underlying electronic structure. Importantly, fully reversible and repeatable switching of Rashba edge states across multiple cycles at room temperature is experimentally demonstrated. The results imply Se functionalization as a practical method to control SOC and spin texture of quantum states in topological insulators.

Yuting Zheng, Zijian Chen, Qi Jia

Unraveling the hierarchical structure-property relationships is the central challenge of materials science, necessitating the interpretation of data across vast physical scales from micro to macro. Despite the rapid integration of Large Multimodal Models (LMMs) into scientific workflows, existing scientific benchmarks primarily focus on general chart interpretation or isolated common-sense reasoning, failing to capture reasoning ability across intricate physical dimensions. To address this, we introduce CSMBench, a dataset comprising 1,041 high-quality figures curated from premier journals up to September 2025. CSMBench categorizes data into four scientifically distinct regimes: atomic, micro, meso, and macro scales, strictly aligning with the focus and definitions in materials study. Through open-ended figure description and multiple-choice caption matching tasks, we evaluate state-of-the-art open-source and closed-source models. Our analysis identifies that performance varies significantly across physical scales due to the distinct visual characteristics, highlighting the limitations of current generalist models and identifying critical directions for achieving hierarchical and accurate understanding in materials research. The CSMBench is publicly released at: https://huggingface.co/datasets/lututu/CSMBench.

Ranjan Datta, Sneha Kobri M., Sudip Mahato

In this review article fundamentals of aberration corrected phase contrast transmission electron microscopy for the structural characterization of materials at atomic length scale is presented. The word structure entails atomic arrangement as well as electronic structure information of the materials. The article summarily covers a range of topics on the basics of aberrations, aberration correctors, direct image interpretation with negative Cs phase contrast microscopy, a discussion in comparison with the competitive atomic resolution phase contrast methods for example, off-axis electron holography, electron ptychography, differential phase contrast microscopy. Additionally, various examples of quantitative imaging of materials at atomic length scale, associated image simulation and reconstruction methods for retrieving the phase information are presented. With the tremendous advancement in instrumentation and recording devices, potential future perspective of such tools and methods in solving challenging materials science problems are outlined.

Kaustav Das, Ishita Ghosh, Soumalya Chakraborty et al.

Abstract Altering surface chemistry of functional materials is an attractive route to enable large property enhancements without sacrificing overall structural-order, appealing to diverse fields of application sciences; however, the same remains unexplored for organic crystalline materials. Herein, piezoelectricity in pharmaceutical crystals is reported to show colossal surface charges driven by mechanical fracture — where a collection of dipoles arranged in polar head-to-tail fashion generates opposite surface charges on freshly fractured faces — causing them to actuate large distances over 75 µm in milliseconds. Kelvin probe force microscopy is leveraged to show many-fold surface potential enhancement in fractured surfaces relative to the pristine crystals. Further, complementarity of the surface potentials in a pair of fractured crystal shards and asymptotic decay behaviour with time are observed. Newly formed surfaces of the pharmaceutical crystals show long-lasting charges despite their relatively lower piezo-response confirmed by bulk piezometry. To establish the generality of surface phenomena, statistical analyses (≈50 samples) of post-fracture-attraction behaviour of crystals are performed. Finally, the application of fracture-driven surface charges in industrial processes is achieved by investigating flow-property and tablet-strength of bulk pharmaceutical materials. This multiscale approach unveils the symmetry-dependency of surface charges in fractured materials, and probes the same for utilisation in bulk-property engineering.

LI Xu, YUAN Jiachi, ZHANG Zhibin et al.

Adding appropriate amounts of Al and Cu atoms to high-entropy alloys （HEAs） can significantly improve mechanical properties of the alloys， but there are few research reports on the corrosion resistance of Al and Cu atoms in HEAs. To reveal the influence of Al and Cu atoms on the corrosion behavior of HEAs， this study focuses on FeCoNi based medium entropy alloys with excellent mechanical properties. FCC single-phase Fe25Co25Ni25Al10Cu15（Al10Cu15） alloy and BCC+FCC dual-phase Fe25Co25Ni25Al15Cu10（Al15Cu10） and Fe25Co25Ni25Al20Cu5（Al20Cu5） alloys are designed using empirical formulas for high-entropy alloy composition design. XRD analysis shows that the amount of FCC phase decreases and the amount of BCC increases with the increase of Al content， which is consistent with the theoretical calculation. SEM microstructure and EDS analysis show that increasing the amount of Al added and decreasing the amount of Cu added result in a transformation of the grain morphology from dendritic （Al10Cu15， Al15Cu10） to equiaxed （Al20Cu5）， and the composition of the interdendritic also changes significantly. The Al10Cu15 interdendritic microstructure is a Cu-rich FCC phase， the Al15Cu10 interdendritic microstructure is an Al-， Ni- and Cu-rich BCC phase， and the Al20Cu5 grain boundaries microstructure is a Fe- and Co-rich FCC phase. The potentiodynamic polarization（PDP） experiments show that alloys with high Al content have a dual-phase structure and are prone to galvanic corrosion during long-term immersion. The integrity of the passivation film is easily damaged， resulting in poor corrosion resistance of the alloy. The electrochemical impedance spectroscopy （EIS） tests show that the reaction resistance of alloys with higher Al additions decreases significantly with the prolongation of immersion time， which is consistent with the results of PDP analysis. Static immersion experiments at room temperature show that compared with Al10Cu15 alloy， Al15Cu10 and Al20Cu5 alloys are more susceptible to galvanic corrosion under prolonged immersion. It can be concluded that the addition of an excessive amount of Al atoms induced by the second phase significantly deteriorates the corrosion resistance of the material. Ensuring the homogeneity of alloy structure composition is an effective means to improve the corrosion resistance of materials.

Mohamed A. M. El-Tabakh, Ahmed Z. I. Shehata, Ahmed M. Sadek et al.

Abstract To develop economically viable and environmentally benign methodologies for organic reactions and reveal the practical utility of transitional natural compounds and their derivatives. In addition, a new research method to conduct docking studies against nuclear factors sheds light on the theoretical mechanism of action of Phlomis aurea extracts as antioxidant, antimicrobial, anticancer, and repellent. The pharmacological potential of Phlomis aurea is investigated in this research by analysing its aqueous and petroleum ether extracts. So, to evaluate antioxidant activity, the DPPH scavenging test was used and compared against ascorbic acid; aqueous extract showed noteworthy activity. Both extracts demonstrated noteworthy efficacy against various pathogens, such as Enterococcus faecalis, Staphylococcus aureus, and Candida albicans. The anti-cancer activity was also assessed using in-vitro assay on a standard cell line (Wi38) and two cancer cell lines (MDA and HepG2). The sensitivity of starving female An. pharoensis to the studied extracts was higher than that of Cx. pipiens, suggesting that these extracts may have potential applications in vector control. Docking study against nuclear factor erythroid 2–related factor 2 (Nrf2) (PDB ID: 3wn7), topoisomerase IV (PDB ID: 7lhz), COX protein (PDB ID: 6y3c), and Odorant Binding Protein 7 (OBP7) (PDB ID: 3r1o), to shed light on the theoretical mechanism expected as anti-oxidant, anti-microbial, anti-cancer and repellent effects against mosquitoes respectively, for galic acid as most significantly quantifying compounds on both extracts; highlighting the predicted mechanism of the proposed in-vitro assay, and confirming the present result.

Bhanu Prakash Vangala, Sajid Mahmud, Pawan Neupane et al.

Artificial Intelligence (AI), particularly Large Language Models (LLMs), is transforming scientific discovery, enabling rapid knowledge generation and hypothesis formulation. However, a critical challenge is hallucination, where LLMs generate factually incorrect or misleading information, compromising research integrity. To address this, we introduce HalluMatData, a benchmark dataset for evaluating hallucination detection methods, factual consistency, and response robustness in AI-generated materials science content. Alongside this, we propose HalluMatDetector, a multi-stage hallucination detection framework that integrates intrinsic verification, multi-source retrieval, contradiction graph analysis, and metric-based assessment to detect and mitigate LLM hallucinations. Our findings reveal that hallucination levels vary significantly across materials science subdomains, with high-entropy queries exhibiting greater factual inconsistencies. By utilizing HalluMatDetector verification pipeline, we reduce hallucination rates by 30% compared to standard LLM outputs. Furthermore, we introduce the Paraphrased Hallucination Consistency Score (PHCS) to quantify inconsistencies in LLM responses across semantically equivalent queries, offering deeper insights into model reliability.

Y. -C. Hu, J. Tian

Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be quantitatively characterized at the atomic scale. This greatly deepens our understanding of Nature and provides incredible insights supplementing experimental observations. Hitherto, a plethora of literature discusses the computational discoveries in studying glasses in which positional disorder is inherent in their configurations. Motivated by active research and knowledge sharing, we developed a data analysis library in Python for computational materials science research. We hope to help promote scientific progress and narrow some technical gaps for the wide communities. The toolkit mainly focuses on physical analyses of glassy properties from the open-source simulator LAMMPS. Nevertheless, the code design renders high flexibility, with functionalities extendable to other computational tools. The library provides data-driven insights for different subjects and can be incorporated into advanced machine-learning workflows. The scope of the data analysis methodologies applies not only to materials science but also to physics, chemistry, and beyond.

Shaobo WEI, Bing WANG, Ying XIONG

Ultra-nanocrystalline diamond (UNCD) films were prepared by microwave plasma chemical vapour deposition (MPCVD) at different temperature conditions by adjusting the microwave power. The effects of the activation power of the reaction source and effects of the temperature of the substrate on the growth and composition of the UNCD films were compared and analysed in order to obtain the technique to rapidly grow high-quality UNCD films. SEM, XRD and Raman methods were used to characterise the morphological structure, phase composition and growth rate of the UNCD films, while OES spectroscopy was used to monitor the state of the growth groups during the deposition of the UNCD films. The results showed that the deposition temperature of the UNCD films ranged from 450 to 650 ℃; that the peak intensity of CN and C2 groups in the OES spectra increased with the increase of power and substrate temperature; that the growth rate increased from 0.82 μm/h to 6.62 μm/h; and that the grain size in the films increased. The average grain size was less than 10.00 nm, and the surface was flatter and smoother, forming a surface profile more favourable to the mechanical properties. Therefore, the use of diisopropylamine liquid small molecules as the reaction source, together with the application of higher microwave power and deposition at higher substrate temperatures, is an effective way to mushroom high-quality UNCD films.

C. Hübner, M. Vadalà, K. Voges et al.

Abstract Freeze-casting consists of freezing a liquid suspension (aqueous or other), followed by sublimation of the solidified state to the gas state under reduced pressure, and subsequent sintering of the remaining scaffold to consolidate and densify the struts and walls. The structure is very porous with the pores being a replica of the solvent crystals. The technique is rather versatile and the use of a liquid solvent (water most of the time) as a pore forming agent is a strong asset. Freeze-casting has also been developed as a near net shape forming route yielding dense ceramics. In this work we report on porous composite materials synthesized via the ice templating method. Poly(vinyl alcohol) (PVA) is used as matrix and nano-silica (SiO2), nanoclay (NC) and microfibrillated cellulose (MFC) are used as fillers to improve the mechanical stability of the PVA scaffold. We show our results on the porosity and mechanical stability and consider these porous nanocomposites as potential insulation materials with low thermal conductivity and superior mechanical properties.

Xiaowei He, Sifei Zhuo, Lidong Tian et al.

Abstract To follow up on the performance of lithium‐ion batteries (LIBs), transition metal sulfides (TMSs) have been developed as promising carbon alternatives for sodium‐ion batteries (SIBs). Although attractive, it is still a great challenge to fulfill their capacity utilization with high cycling performance. Herein, a nanoemulsion‐directed method has been developed to control the spherical arrangement of ZnS@C units with both penetrating macropores from the center to the surface and inner mesopores distributed among the bulks. With respect to ion diffusion, the penetrating macropores could serve as the built‐in ion‐buffer reservoirs to keep a steady flow of electrolyte, while the inner mesopores facilitate the ion diffusion across the whole bulks. In terms of stability, the radical porous structure could work as self‐supported vertical bones to accommodate the volume change from both lateral and vertical sides. Besides, the localized carbon distributed among the ZnS nanoparticles not only acts as binding agents to join the numerous ZnS nanoparticles but also endows the radical bones with effective electron transmission capability. As a proof of concept, such hydrangea‐like ZnS@C nanospheres deliver sodium storage performance with high‐rate and long‐cycling capability. This nanoemulsion‐directed approach is anticipated for other TMSs with penetrating pores for post‐lithium‐ion batteries applications.

Sheng Gong, Shuo Wang, Taishan Zhu et al.

The widespread application of multimodal machine learning models like GPT-4 has revolutionized various research fields including computer vision and natural language processing. However, its implementation in materials informatics remains underexplored, despite the presence of materials data across diverse modalities, such as composition and structure. The effectiveness of machine learning models trained on large calculated datasets depends on the accuracy of calculations, while experimental datasets often have limited data availability and incomplete information. This paper introduces a novel approach to multimodal machine learning in materials science via composition-structure bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet) is designed to enhance learning and predictions of experimentally measured materials properties that have incomplete structure information. Bimodal learning significantly reduces prediction errors across distinct materials properties including Li conductivity in solid electrolyte, band gap, refractive index, dielectric constant, energy, and magnetic moment, surpassing composition-only learning methods. Furthermore, we identified that data augmentation based on modal availability plays a pivotal role in the success of bimodal learning.

Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings et al.

Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify and consolidate chemical knowledge, the case for models with potential to generalize across different tasks within materials science - so-called "foundation models" - grows with ambitions. This manuscript reviews our recent progress with development of Open MatSci ML Toolkit, and details experiments that lay the groundwork for foundation model research and development with our framework. First, we describe and characterize a new pretraining task that uses synthetic data generated from symmetry operations, and reveal complex training dynamics at large scales. Using the pretrained model, we discuss a number of use cases relevant to foundation model development: semantic architecture of datasets, and fine-tuning for property prediction and classification. Our key results show that for simple applications, pretraining appears to provide worse modeling performance than training models from random initialization. However, for more complex instances, such as when a model is required to learn across multiple datasets and types of targets simultaneously, the inductive bias from pretraining provides significantly better performance. This insight will hopefully inform subsequent efforts into creating foundation models for materials science applications.

V. A. Traag

Citations in science are being studied from several perspectives, among which approaches such as scientometrics and science of science. In this chapter I briefly review some of the literature on citations, citation distributions and models of citations. These citations feature prominently in another part of the literature which is dealing with research evaluation and the role of metrics and indicators in that process. Here I briefly review part of the discussion in research evaluation. This also touches on the subject of how citations relate to peer review. Finally, I conclude by trying to integrate the two literatures. The fundamental problem in research evaluation is that research quality is unobservable. This has consequences for conclusions that we can draw from quantitative studies of citations and citation models. The term ``indicators'' is a relevant concept in this context, which I try to clarify. Causality is important for properly understanding indicators, especially when indicators are used in practice: when we act on indicators, we enter causal territory. Even when an indicator might have been valid, through its very use, the consequences of its use may invalidate it. By combining citation models with proper causal reasoning and acknowledging the fundamental problem about unobservable research quality, we may hope to make progress.

Paola C. Torche, Andrea Silva, Denis Kramer et al.

We present a multi-scale computational framework suitable for designing solid lubricant interfaces fully in silico. The approach is based on stochastic thermodynamics founded on the classical thermally activated two-dimensional Prandtl-Tomlinson model, linked with First Principles methods to accurately capture the properties of real materials. It allows investigating the energy dissipation due to friction in materials as it arises directly from their electronic structure, and naturally accessing the time-scale range of a typical friction force microscopy. This opens new possibilities for designing a broad class of material surfaces with atomically tailored properties. We apply the multi-scale framework to a class of two-dimensional layered materials and reveal a delicate interplay between the topology of the energy landscape and dissipation that known static approaches based solely on the energy barriers fail to capture.

S.K. Verma, A. Thirumurugan, P.K. Panda et al.

Recent advancement in nanotechnology seeks exploration of new techniques for improvement in the molecular, chemical, and biological properties of nanoparticles. In this study, carbon modification of octahedral-shaped magnetic nanoparticles (MNPs) was done using two-step chemical processes with sucrose as a carbon source for improvement in their electrochemical application and higher molecular biocompatibility. X-ray diffraction analysis and electron microscopy confirmed the alteration in single-phase octahedral morphology and carbon attachment in Fe3O4 structure. The magnetization saturation and BET surface area for Fe3O4, Fe3O4/C, and α-Fe2O3/C were measured as 90, 86, and 27 emu/g and 16, 56, and 89 m2/g with an average pore size less than 7 nm. Cyclic voltammogram and galvanostatic charge/discharge studies showed the highest specific capacitance of carbon-modified Fe3O4 and α-Fe2O3 as 213 F/g and 192 F/g. The in vivo biological effect of altered physicochemical properties of Fe3O4 and α-Fe2O3 was assessed at the cellular and molecular level with embryonic zebrafish. Mechanistic in vivo toxicity analysis showed a reduction in oxidative stress in carbon-modified α-Fe2O3 exposed zebrafish embryos compared to Fe3O4 due to despaired influential atomic interaction with sod1 protein along with significant less morphological abnormalities and apoptosis. The study provided insight into improving the characteristic of MNPs for electrochemical application and higher biological biocompatibility.

Oleksiy Mazurenko, Inna Tiutiunyk

This paper summarizes the arguments and counterarguments within the scientific discussion on the generalization of the main vectors of the tax competitiveness theory’s development. The main purpose of the article is to analyze and systematize the research of scientists on the formation of tax competitiveness of the country, to identify the relationship of tax competitiveness with other economic categories, to determine the most promising areas of research on this issue. The results of trend analysis of scientific publications on tax competitiveness, indexed by Scopus and Web of Science scientometric databases, show a gradual increase in the relevance of these issues. The average growth rate of the number of publications on tax competitiveness in the Scopus database exceeds 12%, and in the Web of Science database – 45%. The methodological tools of the bibliometric analysis are VOSViewer v.1.6.10 and Scopus and Web of Science database analysis tools. The object of analysis is 4,598 publications indexed in the Web of Science database and 4,898 publications indexed in the Scopus database. The issues of international tax competitiveness became most relevant in 2003-2005, which coincided with the period of aggravation of the global economic crisis, which was accompanied by a significant reduction in tax revenues to budgets. The article identifies the top 10 Journals, most of which are indexed simultaneously by two databases and are part of the first quarter, in which the issue of tax competitiveness was considered most often. The study empirically confirms and theoretically proves the intersectoral nature of the study of the problem of the country’s tax competitiveness. According to the Web of Science database, issues of tax competitiveness were most often considered within the subject areas of Economics (39% of publications); Business Finance (6%); Environmental Studies (6%); Political Science (5%); Law (4%); Urban Studies (3%); Business (3%); Management (3%); Environmental Sciences (2%); Public Administration (2%); Regional Urban Planning (2%); International Relations (2%); Operations Research Management Science 2%) and others (21%), while according to the Scopus database – Economics, Econometrics and Finance (published 28% of all papers); Social Sciences (21%); Business, Management and Accounting (13%); Engineering (7%); Environmental Science (7%); Medicine (5%); Energy (4%); Computer Science (2%); Arts and Humanities (2%); Decision Sciences (2%); Earth and Planetary Sciences (1%); Materials Science (1%); Agricultural and Biological Sciences (1%); Others (6%). The paper clusters international research networks on tax competitiveness by geographical area and identifies 5 clusters of cooperation of scientists in the preparation of publications indexed in the Web of Science database and 4 clusters – in the preparation of publications indexed in the Scopus database. According to the results of the analysis of metadata of publications devoted to the tax competitiveness, 14672 keywords, the frequency of use of which exceeds 5, were identified and grouped into 5 patterns. Most often, the concept of tax competitiveness is associated with the concepts of tax, economics, competition, costs, taxation.

Ufaith Qadiri

In this contribution two Alternative fuels in fixed proportions were compared with conventional 100% gasoline fuel on a constant Speed single cylinder based generator. This work defines the complete state of the art work done on computational Simulation Software on AVL Boost. In this work, we have compared the performance and emission characteristics of single cylinder spark Ignition engine constant speed of 3000 rpm fuelled conventional Gasoline 100% with blended Alternative fuel Ethanol15% with 85% Gasoline, and water-Ethanol based micro-emulsion fuel Gasoline 85% Ethanol 10% and H2O 5% on licence based Simulation Software AVL Boost. The performance parameters were checked for all the three types of fuels and emission characteristics were compared with all the three types of fuels. The results were very promising for water-Ethanol based micro-emulsion fuel as far as the emission characteristics are concerned. Ethanol 15% blends with 85% Gasoline also showed very less emissions as compared to conventional 100% Gasoline. The power & Torque has shown slightly more increase for conventional 100% Gasoline fuel as compared to other two Alternative Fuels. However, emissions were far lesser for water-Ethanol based micro-emulsion and Ethanol blended fuel. The main aim of this investigation is to reduce the emissions and trying to meet the future emission standards Euro 7.

Joshuah K. Stolaroff, Simon H. Pang, Wenqin Li et al.

Strategies to remove carbon from the atmosphere are needed to meet global climate goals. Promising strategies include the conversion of waste biomass to hydrogen, methane, liquid fuels, or electricity coupled with CO2 capture and storage (CCS). A key challenge for these projects is the need to connect geographically dispersed biomass supplies with geologic storage sites by either transporting biomass or CO2. We assess the cost of transport for biomass conversion projects with CCS using publicly available cost data for trucking, rail, and CO2 pipelines in the United States. We find that for large projects (order of 1 Mt/yr CO2 or greater), CO2 by pipeline is the lowest cost option. However, for projects that send most of the biomass carbon to storage, such as gasification to hydrogen or electricity production, biomass by rail is a competitive option. For smaller projects and lower fractions of carbon sent to storage, such as for pyrolysis to liquid fuels, CO2 by rail is the lowest cost option. Assessing three plausible example projects in the United States, we estimate that total transport costs range from $24/t-CO2 stored for a gasification to hydrogen project traversing 670 km to $36/t for a gasification to renewable natural gas project traversing 530 km. In general, if developers have flexibility in choosing transport mode and project type, biomass sources and storage sites can be connected across hundreds of kilometers for transport costs in the range of $20-40/t-CO2 stored. Truck and rail are often viable modes when pipelines cannot be constructed. Distances of 1,000 km or more can be connected in the same cost range when shared CO2 pipelines are employed.

Jane Greenberg, Xintong Zhao, Joseph Adair et al.

Introduces HIVE-4-MAT - Helping Interdisciplinary Vocabulary Engineering for Materials Science, an automatic linked data ontology application. Covers contextual background for materials science, shared ontology infrastructures, and reviews the knowledge extraction and indexing process. HIVE-4-MAT's vocabulary browsing, term search and selection, and knowledge extraction and indexing are reviewed, and plans to integrate named entity recognition. Conclusion highlights next steps with relation extraction to support better ontologies.