Hasil untuk "Materials Science"

L. Ebert

Tzu-Chieh Tang, B. An, Yuanyuan Huang et al.

An Chen, Xu Zhang, Zhen Zhou

With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery. In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energy-related fields, including catalysis, batteries, solar cells, and gas capture. More-over, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science.

N. I. Nayem, S. Ahmed, Md. A. Rashed et al.

ABSTRACT Dopamine (DA) plays a vital role as a neurotransmitter in the central nervous system (CNS), and its accurate quantification is essential for diagnosing neurological disorders. However, selective and sensitive detection of DA in complex biological matrices remains a challenge due to interference from coexisting biomolecules. In this study, a platinum nanoparticle‐decorated carbon nanotubes/polypyrrole‐carbon (Pt@CNTs/PPy‐C) nanocomposite was synthesized via a facile two‐step process involving ultrasonication and photo‐reduction, eliminating the need for stabilizers or dispersants. Structural and morphological analysis confirmed the uniform distribution of Pt nanoparticles within the CNTs/PPy‐C matrix, enhancing electrocatalytic activity. Electrochemical kinetic studies revealed that DA electro‐oxidation on the nanocomposite‐modified glassy carbon electrode (GCE) follows adsorption‐controlled kinetics, with a transfer coefficient (α) of 0.51 and a heterogeneous rate constant of 8.37 s−1. Differential pulse voltammetry (DPV) demonstrated a high sensitivity of 3.45 µA µM−1 cm−2 over a linear range of 2.0–24.0 µM with a detection limit of 0.034 µM. The sensor exhibited outstanding selectivity for DA in the presence of various interfering species, along with excellent reproducibility, repeatability and stability. Additionally, the sensor demonstrated high accuracy and reliability in detecting DA in a commercial pharmaceutical formulation, with recovery rates ranging from 96.72% to 101.40%. These findings highlight the potential of the Pt@CNTs/PPy‐C nanocomposite as a promising electrocatalyst for DA detection, contributing to the development of highly efficient electrochemical sensors for biomedical and pharmaceutical applications.

Michiko Yoshitake, Yuta Suzuki, Ryo Igarashi et al.

We present MaterialFigBench, a benchmark dataset designed to evaluate the ability of multimodal large language models (LLMs) to solve university-level materials science problems that require accurate interpretation of figures. Unlike existing benchmarks that primarily rely on textual representations, MaterialFigBench focuses on problems in which figures such as phase diagrams, stress-strain curves, Arrhenius plots, diffraction patterns, and microstructural schematics are indispensable for deriving correct answers. The dataset consists of 137 free-response problems adapted from standard materials science textbooks, covering a broad range of topics including crystal structures, mechanical properties, diffusion, phase diagrams, phase transformations, and electronic properties of materials. To address unavoidable ambiguity in reading numerical values from images, expert-defined answer ranges are provided where appropriate. We evaluate several state-of-the-art multimodal LLMs, including ChatGPT and GPT models accessed via OpenAI APIs, and analyze their performance across problem categories and model versions. The results reveal that, although overall accuracy improves with model updates, current LLMs still struggle with genuine visual understanding and quantitative interpretation of materials science figures. In many cases, correct answers are obtained by relying on memorized domain knowledge rather than by reading the provided images. MaterialFigBench highlights persistent weaknesses in visual reasoning, numerical precision, and significant-digit handling, while also identifying problem types where performance has improved. This benchmark provides a systematic and domain-specific foundation for advancing multimodal reasoning capabilities in materials science and for guiding the development of future LLMs with stronger figure-based understanding.

Shu-Yu Liang, Run-Qiu Zhu, Hong Xia et al.

Laser micro-nano processing technologies have been developed to address challenges that are otherwise difficult to solve in industrial applications and diverse scientific fields. These technologies offer designable patterning, arraying capabilities, three-dimensional (3D) processing, and high precision. Recent advancements in laser technologies have demonstrated their effectiveness as powerful tools for micro-nano processing of optoelectronic materials. By utilizing various laser techniques—such as laser-induced polymerization, laser ablation, laser-induced transfer, laser-directed assembly, and laser-assisted crystallization—broad applications in image sensors, displays, solar cells, lasers, anti-counterfeiting, and information encryption have been enabled. This review comprehensively summarizes recent progress in the laser micro-nano processing of optoelectronic materials, including the technologies used for preparation, patterning, arraying, and modification. These laser fabrication methods uniquely provide capabilities such as annealing, phase transitions, and ion exchange in optoelectronic materials. We also discuss the perspectives and challenges for future developments, including the advantages, disadvantages, and potential applications of different laser micro-nano processing technologies. With the rapid advancements in laser micro-nanofabrication, we foresee significant growth in advanced, high-performance optoelectronic applications. This review aims to provide researchers with insights into the current state and future prospects of laser-based micro-nano processing, encouraging further exploration and innovation in this promising field.

Yosia Gopas Oetama Manik, Boon Tong Goh, Rikson Siburian et al.

Jeongwon Yeh, Hyun Gi Min, Myoung-Gyu Lee et al.

CrMnFeCoNi quinary complex concentrated alloys (CCAs) exhibit excellent mechanical properties due to the complexity of their atomic environment, attracting significant attention as potential structural materials. However, to effectively utilized CCAs in structural applications, a comprehensive understanding of their plasticity under various loading conditions is imperative for CCAs with various deformation mechanisms. In this study, quinary CCAs were systematically designed to exhibit constant yield strength by controlling the electronegativity difference and tailoring deformation mechanisms through the Gibbs energy difference between γ-austenite and ε-martensite. Uniaxial tensile tests and limit dome height tests were conducted to evaluate plasticity under both uniaxial and biaxial loading conditions. The normalized strain and displacement values revealed a significant reduction in plasticity for TRIP and TADP CCAs under biaxial loading. To elucidate this phenomenon, we compared the maximum Schmid factors of dislocation glide and martensitic transformation for random orientations. As a result, from a geometrical perspective, TRIP is not effectively activated under biaxial loading condition. These findings offer novel insights into the role of the Schmid factor in plasticity of CCAs, focusing on the critical impact of loading conditions on martensitic transformation. Consequently, our results establish effective guidelines for designing CCAs with enhanced plasticity under various stress states.

Musanna Galib, Mewael Isiet, Mauricio Ponga

As the atomistic simulations of materials science move from traditional potentials to machine learning interatomic potential (MLIP), the field is entering the second phase focused on discovering and explaining new material phenomena. While MLIP development relies on curated data and flexible datasets from ab-initio simulations, transitioning seamlessly between ab-initio workflows and MLIP frameworks remains challenging. A global survey was conducted to understand the current standing (progress and bottleneck) of the machine learning-guided materials science research. The survey responses have been implemented to design an open-source software to reduce the access barriers of MLIP models for the global scientific community. Here, we present AtomProNet, an open-source Python package that automates obtaining atomic structures, prepares and submits ab-initio jobs, and efficiently collects batch-processed data for streamlined neural network (NN) training. Finally, we compared empirical and start-of-the-art machine learning potential, showing the practicality of using MLIPs based on computational time and resources.

Kunpeng Liu, Haitao Gong, Binbin Jiao et al.

This study aims to evaluate the therapeutic potential of cationic liposomal neostigmine bromide (NB), a novel drug delivery system, for the treatment of detrusor underactivity. By comparing the characteristics of NB‐liposomes (NLP), NB‐β‐cyclodextrin inclusion complex liposomes (NCLP), and NB‐mesoporous silica nanoparticle@CaCO3 liposomes (NMCLP), NMCLP is selected as the main research subject. It has an average particle size and zeta potential of 100 nm and +50 mV, and its encapsulation efficiency and loading capacity of NB are 14.75% and 12.8%, respectively. Most importantly, NMCLP shows the best in vitro release performance among the three liposomes, demonstrating its ability in sustained release of NB. During cell and animal assays, efficient cellular uptake of liposomes through liposome‐specific pathways is observed, facilitating targeted drug delivery, and in vivo experiments demonstrate the efficacy of NMCLP in improving bladder function in mice. Urodynamic measurements show increased bladder capacity and reduced voiding pressure, indicating enhanced bladder muscle activity. Histological analysis reveals the distribution and deep penetration of NMCLP within bladder tissues, supporting its localized drug effect. Therefore, NMCLP holds promise as a targeted and effective therapeutic strategy for detrusor underactivity.

Cornelia-Ioana Ilie, Angela Spoiala, Cristina Chircov et al.

The gut microbiota dysbiosis that often occurs in cancer therapy requires more efficient treatment options to be developed. In this concern, the present research approach is to develop drug delivery systems based on magnetite nanoparticles (MNPs) as nanocarriers for bioactive compounds. First, MNPs were synthesized through the spraying-assisted coprecipitation method, followed by loading bee pollen or bee bread extracts and an antitumoral drug (5-fluorouracil/5-FU). The loaded-MNPs were morphologically and structurally characterized through transmission electron microscopy (TEM), selected area electron diffraction (SAED), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Dynamic Light Scattering (DLS), and thermogravimetric analysis. UV-Vis spectroscopy was applied to establish the release profiles and antioxidant activity. Furthermore, the antibacterial and antitumoral activity of loaded-MNPs was assessed. The results demonstrate that MNPs with antioxidant, antibacterial, antiproliferative, and prebiotic properties are obtained. Moreover, the data highlight the improvement of 5-FU antibacterial activity by loading on the MNPs’ surface and the synergistic effects between the anticancer drug and phenolic compounds (PCs). In addition, the prolonged release behavior of PCs for many hours (70–75 h) after the release of 5-FU from the developed nanocarriers is an advantage, at least from the point of view of the antioxidant activity of PCs. Considering the enhancement of <i>L. rhamnosus</i> MF9 growth and antitumoral activity, this study developed promising drug delivery alternatives for colorectal cancer therapy.

Neda Sadat Aghayan, Maede Sadat Abbasi, Anna Etemadi Razlighi et al.

Bionanocomposite active films made from tapioca starch and bovine gelatin, with the addition of roselle calyx extract (RCE) and zinc oxide nanorod (ZnO-N), were created as packaging material to protect walnuts against mold, yeast contamination, and lipid oxidation. Three types of packaging were produced: tapioca starch and bovine gelatin (control sample), tapioca starch, bovine gelatin, and RCE, and tapioca starch, bovine gelatin, ZnO-N, and RCE. Approximately 30 grams of walnuts were packed using each type of packaging and evaluated for acidity value, mold and yeast count, peroxide value, and sensory tests. After 90 days, the mold and yeast count of walnuts packed with RCE/ZnO-N and RCE was 4.49 and 4.65 log cfu/g respectively, compared to 4.95 log cfu/g in the control sample. At the end of the conservation period, the aroma score was 3.59 for walnuts packed with RCE/ZnO-N, compared to 2.5 for those packed with tapioca starch and bovine gelatin. The acidity value indicated that walnuts packed with RCE and RCE/ZnO-N had a positive effect on acidity, with the lowest value found in walnuts packed with RCE/ZnO-N. The study showed that bionanocomposite packaging films containing RCE and ZnO-N are effective in protecting walnuts against fungal contamination and oxidation.

Yaoda Liu, Lei Li, Li Wang et al.

Abstract Metal-support electronic interactions play crucial roles in triggering the hydrogen spillover (HSo) to boost hydrogen evolution reaction (HER). It requires the supported metal of electron-rich state to facilitate the proton adsorption/spillover. However, this electron-rich metal state contradicts the traditional metal→support electron transfer protocol and is not compatible with the electron-donating oxygen evolution reaction (OER), especially in proton-poor alkaline conditions. Here we profile an Ir/NiPS3 support structure to study the Ir electronic states and performances in HSo/OER-integrated alkaline water electrolysis. The supported Ir is evidenced with Janus electron-rich and electron-poor states at the tip and interface regions to respectively facilitate the HSo and OER processes. Resultantly, the water electrolysis (WE) is efficiently implemented with 1.51 V at 10 mA cm–2 for 1000 h in 1 M KOH and 1.44 V in urea-KOH electrolyte. This research clarifies the Janus electronic state as fundamental in rationalizing efficient metal-support WE catalysts.

Figen Algül, Hasan Algül

This study investigates the effect of mechanical activation parameters such as mechanical activation rotation speed (0-550 rpm), mechanical activation time (15-75 min), and solid/ball ratio (1/20-1/50) on the leaching efficiencies in the recycling of lithium-ion batteries. In addition to mechanical activation, the study explores the use of organic acids, specifically citric acid, as leaching agents to enhance metal recovery. A green and innovative recycling process is developed, focusing on optimal conditions of 15 minutes activation time, 450 rpm rotational speed, and a 1/20 solid/ball ratio. The synergistic effect of mechanical activation and organic acid leaching is examined to optimize the process for sustainability and efficiency in recovering valuable metals from lithium-ion batteries. Results indicate that these parameters significantly influence leaching efficiencies, with the highest yields achieved under the identified conditions. This research contributes to advancing sustainable practices in battery recycling by integrating mechanical activation and organic acid leaching as effective and environmentally friendly approaches. The findings highlight the potential of these methods in advancing green technology and materials science, paving the way for more efficient and eco-friendly battery recycling processes.

Zirui Zhao, Hai-Feng Li

Understanding and predicting interface diffusion phenomena in materials is crucial for various industrial applications, including semiconductor manufacturing, battery technology, and catalysis. In this study, we propose a novel approach utilizing Graph Neural Networks (GNNs) to investigate and model material interface diffusion. We begin by collecting experimental and simulated data on diffusion coefficients, concentration gradients, and other relevant parameters from diverse material systems. The data are preprocessed, and key features influencing interface diffusion are extracted. Subsequently, we construct a GNN model tailored to the diffusion problem, with a graph representation capturing the atomic structure of materials. The model architecture includes multiple graph convolutional layers for feature aggregation and update, as well as optional graph attention layers to capture complex relationships between atoms. We train and validate the GNN model using the preprocessed data, achieving accurate predictions of diffusion coefficients, diffusion rates, concentration profiles, and potential diffusion pathways. Our approach offers insights into the underlying mechanisms of interface diffusion and provides a valuable tool for optimizing material design and engineering. Additionally, our method offers possible strategies to solve the longstanding problems related to materials interface diffusion.

Hsu-I Mao, Jun-Yuan Hu, Jia-Wei Shiu et al.

A series of electromagnetic interference (EMI) shielding composites composed of an epoxy-based vitrimer matrix and stainless-steel fabric was prepared in this study. The polymer matrix presents tunable mechanical properties and chemical recyclability through adjustment of the content ratio of soft aliphatic sebacic acid (SA) to rigid crosslinker 3-(carboxymethyl)cyclopentane-1,2,4-tricarboxylic acid (TCAA). All the recycled vitrimers exhibit strengths comparable to the original materials, suggesting that similar cross-linking structures were reformulated successfully. Furthermore, a noticeable enhancement in the mechanical strength is observed for the combination of matrix and stainless-steel fabric, indicating that the combination of vitrimer material and fabric is excellent. However, a certain extent of decreased mechanical abilities was observed for the composites after reclaiming, while no apparent differences are noted in the results between multiple recycling processes. This tendency may be attributed to the complex fabric structure, which allows a distinct distribution for the reclaimed matrix that is repolymerized by evaporating the solvent compared to the original polymer. In addition, the original and repeatedly recycled composites reveal comparable EMI shielding abilities of around 70 dB, demonstrating the potential for high performance when applied as EMI shielding materials.

Xianzhe Shi, Xiuxia Wang, Biao Chen et al.

Oxygen has been known as an effective strengthening element in titanium (Ti) and its alloys. However, an over-dose of oxygen can also lead to embrittlement of Ti alloys. To precisely control and push the limit of oxygen in Ti and its alloys, we studied the decomposition process of Ti oxides in pure α-Ti matrix using an in-situ high-temperature scanning electron microscope. The experimental results revealed that TiO particles decomposed in α-Ti at elevated temperatures and the oxygen atoms gradually diffused into the matrix, following the Fick’s second law. Then, the samples with different oxygen contents were produced using the aforementioned strategy, for which the oxygen content, microstructure, and mechanical properties were measured. The results revealed that the oxygen content can be precisely controlled, which can achieve an ultra-high tensile strength of close to 1100 MPa, at no expense of elongation-to-failure, with incorporating 0.87 wt% oxygen. An analysis showed that the strength contribution from oxygen follows the Labusch law. These findings offer a novel approach to design high-performance Ti alloys with non-toxic and cheap elements.

Yu Liu, Tao Liu, Danfeng Jiang

Sweat contains a wealth of health-related biomarkers, which has been a promising resource for personalized real-time monitoring at molecular level. Emergence of non-enzymatic electrochemical sensor that simulates the enzyme catalysis utilizing the functional material further promotes the development of wearable sweat sensor, successfully addressing the limitations of enzyme sensing in sensitivity and stability. Thus, there is an urgent need for centering on the regulation of the nanostructure, combination and preparation method of functional materials to enhance the catalytic activity for enzyme-free detection of sweat biomarkers. This review aims to present the superiors of enzyme-free sensing on wearable sweat sensor, and provides guidance for material innovation, sensor design and system integration. Firstly, we primarily focus on the recent advances of novel functional nanomaterials in wearable non-enzymatic electrochemical sensor, and briefly describe the sensing principles for detecting biomarkers in sweat. Subsequently, the correlation between the electrochemical strategy and functional material is elaborately interpreted by coupling with the diverse molecular structures of the biomarkers and the pH changes of test environments. Finally, challenges and opportunities for wearable non-enzymatic electrochemical sensor in sweat sensing are delineated in the development of future personalized healthcare.

Binh Duong Nguyen, Pavlo Potapenko, Aytekin Dermici et al.

Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn-Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this.