Hasil untuk "Materials Science"

Shuxin Li, Wenfei Shen, Shuhan Guo et al.

Considered a pivotal advancement for commercial applications, blade coating technology for large area photovoltaic devices has emerged as a forefront research area in the field of polymer solar cells (PSCs). Herein, a high-performance PM6:L8-BO device is fabricated with the blade-coating method in ambient air. Meanwhile, Eu3+-induced diblock polymer aggregates (EIPAs) and Tb3+-induced diblock polymer aggregates (TIPAs) with excellent fluorescent properties were synthesized through self-assembly and incorporated as an additive into the PM6:L8-BO system to increase the ultraviolet light absorption and enhance BC-PSC light harvesting. By employing this strategy, the blade-coating device's power conversion efficiency (PCE) was improved from 12.25 % to 13.63 %, and the relative efficiency was enhanced by 11.3 %. In addition to the performance improvement, the stability of the devices was also enhanced by 19 %, indicating the effectiveness of this approach in producing more efficient and durable PSCs.

Chao MA, Yanqiu ZHAO, Xufeng KANG et al.

To address the problem of uneven pre-welding assembly gaps widely existing in the laser welding process of complex thin-walled aviation components, adaptive laser welding experiments were conducted on 1.2 mm thick TC4 titanium alloy plates with unequal gaps. A three-dimensional transient thermal-fluid coupling model of the welding process was established to analyze the influence of different welding gaps on the dynamic behavior of the molten pool. The results indicate that to balance welding quality with the linearly increasing weld gap, the laser energy density shows a gradually decreasing trend while matching the corresponding laser spot radius. As the welding gap increases, and the laser energy density decreases, the surface tension and recoil pressure acting on the inner wall of the keyhole decrease significantly, making it difficult to maintain the high-temperature and high-speed Marangoni circulation required for a long conical molten pool. The fluid velocity at the keyhole tip decreases markedly, and the keyhole shape transforms from a long conical shape to a flat gyroscope-like shape, which fails to exert a strong impact on the molten pool bottom. Consequently, the vortex at the molten pool bottom disappears, and the weld profile transitions from an X-shape at the initial section to a Y-shape at the final section.

Iman Peivaste, Salim Belouettar, Francesco Mercuri et al.

Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed.

R.A. Pitts, A. Loarte, T. Wauters et al.

To mitigate the impact of technical delays, provide a more rationalized approach to the safety demonstration and move forward as rapidly as possible to a reactor relevant materials choice, the ITER Organization embarked in 2023 on a significant re-baselining exercise. Central to this strategy is the elimination of beryllium (Be) first wall (FW) armour in favour of tungsten (W), placing plasma-wall interaction (PWI) centre stage of this new proposal. The switch to W comes with a modified Research Plan in which a first “Start of Research Operation” (SRO) campaign will use an inertially cooled, temporary FW, allowing experience to be gained with disruption mitigation without risking damage to the complex water-cooled panels to be installed for later DT operation. Conservative assessments of the W wall source, coupled with integrated modelling of W pedestal and core transport, demonstrate that the elimination of Be presents only a low risk to the achievement of the principal ITER Q = 10 DT burning plasma target. Primarily to reduce oxygen contamination in the limiter start-up phase, known to be a potential issue for current ramp-up on W surfaces, a conventional diborane-based glow discharge boronization system is included in the re-baseline. First-of-a-kind modelling of the boronization glow is used to provide the physics specification for this system. Erosion simulations accounting for the 3D wall geometry provide estimates both of the lifetime of boron (B) wall coatings and the subsequent B migration to remote areas, providing support to a simple evaluation which concludes that boronization, if it were to be used frequently, would dominate fuel retention in an all-W ITER. Boundary plasma (SOLPS-ITER) and integrated core–edge (JINTRAC) simulations, including W erosion and transport, clearly indicate the tendency for a self-regulating W sputter source in limiter configurations and highlight the importance of on-axis electron cyclotron power deposition to prevent W core accumulation in the early current ramp phase. These predicted trends are found experimentally in dedicated W limiter start-up experiments on the EAST tokamak. The SOLPS-ITER runs are used to formulate W source boundary conditions for 1.5D DINA code scenario design simulations which demonstrate that flattop durations of ∼100 s should be possible in hydrogen L-modes at nominal field and current (Ip = 15 MA, BT = 5.3 T) which are one of the principal SRO targets. Runaway electrons (RE) are considered to be a key threat to the integrity of the final, actively cooled FW panels. New simulations of RE deposition and subsequent thermal transport in W under conservative assumptions for the impact energy and spatial distribution, conclude that there is a strong argument to increase the W armour thickness in key FW areas to improve margins against cooling channel interface damage in the early DT operation phases when new RE seeds will be experienced for the first time.

Nita Bandyopadhyay, Tuhin Das, Suvra Mondal

Background. Yoga, an ancient practice rooted in Indian culture, has gained global recognition for its physical and mental health benefits. Among its practices, Surya Namaskar (SN) stands out as a holistic yogic Sun Salutation exercise combining postures, breathing, and mindfulness, offering physical vitality, mental calmness, and a practical solution to the challenges posed by modern sedentary lifestyles. Objectives. The objective of the present systematic review was to analyze the effect of SN on overall health and wellness of healthy adults. Materials and methods. A comprehensive search was conducted in five major databases, namely Scopus, PubMed, PubMed Central, Web of Science, and ScienceDirect, using the terms such as “Surya Namaskar”, “Sun Salutation”,“Surya Namaskar and physical fitness”, “Surya Namaskar for adults”, “Sun Salutation for overall health and wellness”,and “Surya Namaskar and sedentary lifestyle”. The articles published in English between 2011 and 2024 were considered in the current review. The systematic search and reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Quality Assessment Tool for Quantitative Studies was used to analyze the methodological quality of the included articles. Results. Based on the inclusion and exclusion criteria, 117 articles were initially retrieved, out of which 11 were finally included, encompassing data from 445 healthy adults from three countries, aged between 18 and 65 years. The duration of the SN intervention varied from four to 24 weeks, with session frequency ranging from three days per week to daily, and a diverse number of cycles. The methodological quality analysis revealed that two articles were of strong, six of moderate, and the remaining three of weak quality. Conclusions. This systematic review concludes that the practice of the yogic Sun Salutation exercise (SN) is beneficial for improving and maintaining physical fitness, physiological health, and psychological well-being, which determine the overall health and wellness of healthy adults.

Ling Wang, Xing Liu, Guangcai Zhang et al.

Abstract Neuromorphic computing systems convert multimodal signals to electrical responses for artificial intelligence recognition. Energy is consumed during both the response enhancement and depression, making the systems suffer from high energy consumption. This study presents a neuromorphic computing pathway based on supercapacitors. MXene Ti₃C₂Tx supercapacitors are fabricated and convert current stimuli to voltage responses. The response enhancement and depression are tunable through adjusting charging and discharging current stimuli, thus exhibiting synaptic plasticity. Typical synaptic behaviors are demonstrated, including short‐term memory, long‐term memory, paired‐pulse facilitation, and learning experience. Next, the voltage responses are used to recognize Braille numbers represented by 3 × 4 arrays. A charging/discharging current pulse train representing each Braille array is applied to the supercapacitor. The voltage responses are collected and converted to 12‐pixel greyscale images. Once the images representing Braille numbers 0–9 are input into artificial neural networks and deep diffraction neural networks, 100% accuracy can be achieved for recognizing the ten numbers. Because energy is stored during response enhancement in the supercapacitor and released once the response declines, this research demonstrates the potential applications of energy storage devices in neuromorphic computing, providing an innovative way to develop energy‐efficient brain‐like computing systems.

Guoqiang Liang, Jingqian Gong, Mengxuan Li et al.

Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course towards AGI and emerging as a central issue in the global technological race. This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning. Additionally, it traces the historical development of Science of Science (SciSci) and presents a forward looking perspective on the potential applications of LLMs within the scientometric domain. Furthermore, it discusses the prospect of an AI agent based model for scientific evaluation, and presents new research fronts detection and knowledge graph building methods with LLMs.

Hyun-Ae Cha, Su-Jin Ha, Min-Gi Jo et al.

Ruicheng Bai, Pei Zhang, Xihai Wang et al.

In this work, bilayer nanocoatings were designed and constructed on high-performance aromatic polysulfonamide (PSA) fibers for robust electric conduction and electromagnetic interference (EMI) shielding. More specifically, PSA fibers were first endowed with necessary electric conductivity via electroless nickel (Ni) or nickel alloy (Ni-P-B) plating. Afterward, silver electroplating was carried out to further improve the performance of the composite. The morphology, microstructure, environmental stability, mechanical properties, and EMI shielding performance of the proposed cladded fibers were thoroughly investigated to examine the effects of electrodeposition on both amorphous Ni-P-B and crystalline Ni substrates. The acquired results demonstrated that both PSA@Ni@Ag and PSA@Ni-P-B@Ag composite fibers had high environment stability, good tensile strength, low electric resistance, and outstanding EMI shielding efficiency. This indicates that they can have wide application prospects in aviation, aerospace, telecommunications, and military industries. Furthermore, the PSA@Ni-P-B@Ag fiber configuration seemed more reasonable because it exhibited smoother and denser silver surfaces as well as stronger interfacial binding, leading to lower resistance (185 mΩ cm⁻¹) and better shielding efficiency (82.48 dB in the X-band).

Rahele Sadeghzadeh, Fatemeh Rafieian, Mahdi Keshani et al.

Pseudomonas aeruginosa is a Gram-negative human pathogenic bacterium that has the ability to form multicellular biofilm (BF) communities. Due to the presence of extracellular polymeric substances, BF protects bacteria from unfavorable environmental conditions and causes their resistance to antimicrobial substances. The presence of BF in the food industry has become a great threat to food safety. Conventional disinfection technologies are inappropriate for effective BF control due to the resistances created to them and the toxic residues for humans and the environment that they leave behind. Therefore, it is necessary to understand more about the formation and development of BF and environmentally friendly methods to remove BF from food and equipment in contact with food. This review article describes BF formation, its resistance mechanisms to antimicrobial agents, and BF development. Also, novel and effective strategies involved in BF removal are discussed including physical methods (plasma, pulsed electric field and ultrasonication), physicochemical method (electrolyzed water), biological methods (enzymes and bacteriophages), natural compounds such as essential oils, and application of nanomaterials.

Dan Zhao, Dan Li, Yong Xiao et al.

Tailoring the phase constitutions of the interfacial reaction layers under the assistance of ultrasonic vibration is a convenient method to fabricate high-strength Al/Cu brazing joints. In this study, 1060-Al and T2-Cu dissimilar metals were ultrasonically brazed with Zn-3Al (wt. %) filler metals. Effects of ultrasonic brazing time on the microstructure and mechanical properties of joints were investigated. Results showed that the CuZn5 intermetallic compound (IMC) layer and Cu-based diffusion layer were created on the Cu substrate surface in the joint ultrasonically brazed at 400 ℃ for 2 s. However, the CuZn5 IMC layer was gradually transformed into a thin Al4.2Cu3.2Zn0.7 IMC layer by increasing the ultrasonic vibration time to 15 s. A well-matched coherent interface was formed between the Al4.2Cu3.2Zn0.7 ternary phase and the Cu-based diffusion layer. The phase transition of the Cu-side interfacial layer correlated closely with the acoustic cavitations induced super-saturation regions near the Cu substrate surface. The measured tensile strength of the Al/Zn-3Al/Cu joint ultrasonically brazed for 15 s was 89.3 MPa, which was approximately 2.5 times higher than that brazed for 2 s, and the tensile failure mainly occurred at the interface between the Al4.2Cu3.2Zn0.7 layer and the Cu-based diffusion layer.

Hongchen Wang, Kangming Li, Scott Ramsay et al.

Large Language Models (LLMs) have the potential to revolutionize scientific research, yet their robustness and reliability in domain-specific applications remain insufficiently explored. In this study, we evaluate the performance and robustness of LLMs for materials science, focusing on domain-specific question answering and materials property prediction across diverse real-world and adversarial conditions. Three distinct datasets are used in this study: 1) a set of multiple-choice questions from undergraduate-level materials science courses, 2) a dataset including various steel compositions and yield strengths, and 3) a band gap dataset, containing textual descriptions of material crystal structures and band gap values. The performance of LLMs is assessed using various prompting strategies, including zero-shot chain-of-thought, expert prompting, and few-shot in-context learning. The robustness of these models is tested against various forms of 'noise', ranging from realistic disturbances to intentionally adversarial manipulations, to evaluate their resilience and reliability under real-world conditions. Additionally, the study showcases unique phenomena of LLMs during predictive tasks, such as mode collapse behavior when the proximity of prompt examples is altered and performance recovery from train/test mismatch. The findings aim to provide informed skepticism for the broad use of LLMs in materials science and to inspire advancements that enhance their robustness and reliability for practical applications.

Huan Zhang, Yu Song, Ziyu Hou et al.

The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks for materials science. Many LLMs, however, often struggle with distinct complexities of material science tasks, such as materials science computational tasks, and often rely heavily on outdated implicit knowledge, leading to inaccuracies and hallucinations. To address these challenges, we introduce HoneyComb, the first LLM-based agent system specifically designed for materials science. HoneyComb leverages a novel, high-quality materials science knowledge base (MatSciKB) and a sophisticated tool hub (ToolHub) to enhance its reasoning and computational capabilities tailored to materials science. MatSciKB is a curated, structured knowledge collection based on reliable literature, while ToolHub employs an Inductive Tool Construction method to generate, decompose, and refine API tools for materials science. Additionally, HoneyComb leverages a retriever module that adaptively selects the appropriate knowledge source or tools for specific tasks, thereby ensuring accuracy and relevance. Our results demonstrate that HoneyComb significantly outperforms baseline models across various tasks in materials science, effectively bridging the gap between current LLM capabilities and the specialized needs of this domain. Furthermore, our adaptable framework can be easily extended to other scientific domains, highlighting its potential for broad applicability in advancing scientific research and applications.

Yizhou Dai, Huan Li, Chuanhao Wang et al.

Abstract Electrochemical CO2 conversion to methane, powered by intermittent renewable electricity, provides an entrancing opportunity to both store renewable electric energy and utilize emitted CO2. Copper-based single atom catalysts are promising candidates to restrain C-C coupling, suggesting feasibility in further protonation of CO* to CHO* for methane production. In theoretical studies herein, we find that introducing boron atoms into the first coordination layer of Cu-N4 motif facilitates the binding of CO* and CHO* intermediates, which favors the generation of methane. Accordingly, we employ a co-doping strategy to fabricate B-doped Cu-N x atomic configuration (Cu-N x B y ), where Cu-N2B2 is resolved to be the dominant site. Compared with Cu-N4 motifs, as-synthesized B-doped Cu-N x structure exhibits a superior performance towards methane production, showing a peak methane Faradaic efficiency of 73% at −1.46 V vs. RHE and a maximum methane partial current density of −462 mA cm−2 at −1.94 V vs. RHE. Extensional calculations utilizing two-dimensional reaction phase diagram analysis together with barrier calculation help to gain more insights into the reaction mechanism of Cu-N2B2 coordination structure.

Hayet Elkolli, Meriem Elkolli, Farid S. Ataya et al.

<i>Eucalyptus</i>, a therapeutic plant mentioned in the ancient Algerian pharmacopeia, specifically two species belonging to the <i>Myrtaceae</i> family, <i>E</i>. <i>radiata</i> and <i>E</i>. <i>cinerea</i>, were investigated in this study for their antibacterial, antioxidant, and anti-inflammatory properties. The study used aqueous extracts (AE) obtained from these plants, and the extraction yields were found to be different. The in vitro antibacterial activity was evaluated using a disc diffusion assay against three typical bacterial strains. The results showed that the two extracts were effective against all three strains. Both extracts displayed significant antioxidant activity compared to BHT. The anti-inflammatory impact was evaluated using a protein (BSA) inhibition denaturation test. The <i>E</i>. <i>radiata</i> extract was found to inhibit inflammation by 85% at a concentration of 250 µg/mL, significantly higher than the Aspirin. All phytoconstituents present good pharmacokinetic characteristics without toxicity except very slight toxicity of terpineol and cineol and a maximum binding energy of −7.53 kcal/mol for its anti-TyrRS activity in silico. The study suggests that the extracts and their primary phytochemicals could enhance the efficacy of antibiotics, antioxidants, and non-steroidal anti-inflammatory drugs (NSAIDs). As pharmaceutical engineering experts, we believe this research contributes to developing natural-based drugs with potential therapeutic benefits.

Gianluigi Rozza, Oliver Schütze, Nicholas Fantuzzi

This Special Issue comprises the first collection of papers submitted by the Editorial Board Members (EBMs) of the journal <i>Mathematical and Computational Applications</i> (MCA), as well as outstanding scholars working in the core research fields of MCA [...]