Hasil untuk "Materials Science"

Shifa Zhong, Kai Zhang, M. Bagheri et al.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

W. Sames, F. List, S. Pannala et al.

Logan T. Ward, Alex Dunn, Alireza Faghaninia et al.

Abstract As materials data sets grow in size and scope, the role of data mining and statistical learning methods to analyze these materials data sets and build predictive models is becoming more important. This manuscript introduces matminer, an open-source, Python-based software platform to facilitate data-driven methods of analyzing and predicting materials properties. Matminer provides modules for retrieving large data sets from external databases such as the Materials Project, Citrination, Materials Data Facility, and Materials Platform for Data Science. It also provides implementations for an extensive library of feature extraction routines developed by the materials community, with 47 featurization classes that can generate thousands of individual descriptors and combine them into mathematical functions. Finally, matminer provides a visualization module for producing interactive, shareable plots. These functions are designed in a way that integrates closely with machine learning and data analysis packages already developed and in use by the Python data science community. We explain the structure and logic of matminer, provide a description of its various modules, and showcase several examples of how matminer can be used to collect data, reproduce data mining studies reported in the literature, and test new methodologies.

V. Nicolosi, M. Chhowalla, M. Kanatzidis et al.

J. Hafner

Chunfeng Wang, Chonghe Wang, Zhenlong Huang et al.

Soft electronics are intensively studied as the integration of electronics with dynamic nonplanar surfaces has become necessary. Here, a discussion of the strategies in materials innovation and structural design to build soft electronic devices and systems is provided. For each strategy, the presentation focuses on the fundamental materials science and mechanics, and example device applications are highlighted where possible. Finally, perspectives on the key challenges and future directions of this field are presented.

M. Lemme, D. Akinwande, C. Huyghebaert et al.

Graphene and two-dimensional materials (2DM) remain an active field of research in science and engineering over 15 years after the first reports of 2DM. The vast amount of available data and the high performance of device demonstrators leave little doubt about the potential of 2DM for applications in electronics, photonics and sensing. So where are the integrated chips and enabled products? We try to answer this by summarizing the main challenges and opportunities that have thus far prevented 2DM applications. Graphene and related two-dimensional (2D) materials have remained an active field of research in science and engineering for over fifteen years. Here, the authors investigate why the transition from laboratories to fabrication plants appears to lag behind expectations, and summarize the main challenges and opportunities that have thus far prevented the commercialisation of these materials.

J. P. Birkner, Ginnie Renz Print, GildePrint Contact et al.

Materials with new or improved functionalities have always had great importance for humanity, evidenced by the fact that certain periods of time have been named after the available materials which shaped life (e.g. the Stone Age, Bronze Age, and Iron Age). Nowadays, rapid technological advances are resulting in material innovations at an even faster pace, i.e. the so-called advanced materials – a term that has been in use in the field of materials science for more than thirty years1 – which bring about new functionalities and thus new possibilities, but also uncertainties in terms of their effects on humans and the environment. Because of their novel and advanced properties, it has often been pointed out that advanced materials can provide attractive solutions to global challenges, such as the need for continuous renewable energy, clean water, and transition to a low-carbon economy.

Chi Chen, Yunxing Zuo, Weike Ye et al.

Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

Amit Kumar, H. Biebuyck, G. Whitesides

K. Chawla

Jiajia Yang, Xinfang Zhang, Xuan Zhang et al.

Infrared (IR) adaptation phenomena are ubiquitous in nature and biological systems. Taking inspiration from natural creatures, researchers have devoted extensive efforts for developing advanced IR adaptive materials and exploring their applications in areas of smart camouflage, thermal energy management, biomedical science, and many other IR‐related technological fields. Herein, an up‐to‐date review is provided on the recent advancements of bioinspired IR adaptive materials and their promising applications. First an overview of IR adaptation in nature and advanced artificial IR technologies is presented. Recent endeavors are then introduced toward developing bioinspired adaptive materials for IR camouflage and IR radiative cooling. According to the Stefan‐Boltzmann law, IR camouflage can be realized by either emissivity engineering or thermal cloaks. IR radiative cooling can maximize the thermal radiation of an object through an IR atmospheric transparency window, and thus holds great potential for use in energy‐efficient green buildings and smart personal thermal management systems. Recent advances in bioinspired adaptive materials for emerging near‐IR (NIR) applications are also discussed, including NIR‐triggered biological technologies, NIR light‐fueled soft robotics, and NIR light‐driven supramolecular nanosystems. This review concludes with a perspective on the challenges and opportunities for the future development of bioinspired IR adaptive materials.

W. Callister, D. Rethwisch

Y. Yampolskii, I. Pinnau, B. Freeman

Xiaoming Zhai, Kent Crippen

The integration of artificial intelligence (AI) into science education is transforming the design and function of learning materials, offering new affordances for personalization, authenticity, and accessibility. This chapter examines how AI technologies are transforming science learning materials across six interrelated domains: 1) integrating AI into scientific practice, 2) enabling adaptive and personalized instruction, 3) facilitating interactive simulations, 4) generating multimodal content, 5) enhancing accessibility for diverse learners, and 6) promoting co-creation through AI-supported content development. These advancements enable learning materials to more accurately reflect contemporary scientific practice, catering to the diverse needs of students. For instance, AI support can enable students to engage in dynamic simulations, interact with real-time data, and explore science concepts through multimodal representations. Educators are increasingly collaborating with generative AI tools to develop timely and culturally responsive instructional resources. However, these innovations also raise critical ethical and pedagogical concerns, including issues of algorithmic bias, data privacy, transparency, and the need for human oversight. To ensure equitable and meaningful science learning, we emphasize the importance of designing AI-supported materials with careful attention to scientific integrity, inclusivity, and student agency. This chapter advocates for a responsible, ethical, and reflective approach to leveraging AI in science education, framing it as a catalyst for innovation while upholding core educational values.

Sui-An Chou, Chen Chang, Bo-Hong Wu et al.

Abstract The development of p-type WSe2 transistors has lagged behind n-type MoS2 because of challenges in growing high-quality, large-area WSe2 films. This study employs an alkali-assisted CVD (AACVD) method by using KOH to enhance nucleation on sapphire substrates, effectively promoting monolayer growth on c-plane sapphire and enabling controlled bilayer seeding on miscut surfaces with artificial steps. With AACVD, we achieve 2-inch monolayer and centimeter-scale bilayer WSe2 films with defect densities as low as 1.6 × 1012 cm−2 (monolayer) and 1.8 × 1012 cm−2 (bilayer), comparable to exfoliated WSe2. Bilayer WSe2 transistors exhibit hole/electron mobilities of 119/34 cm²/Vs, while monolayers achieve 105/22 cm²/Vs with suitable metal contacts. Additionally, bilayer WSe2 demonstrates lower contact resistance for both n-type and p-type transistors, making it highly promising for future high-performance electronic applications.

Laura Nistor, Cătălin Lisa, Tsuyoshi Michinobu et al.

Background: 2-[4-(Dimethylamino)phenyl]-3-([4-(dimethylamino)phenyl]ethynyl)buta-1,3-diene-1,1,4,4-tetracarbonitrile (DDMEBT) is a thermally robust organic material of interest for applications requiring controlled volatility. Understanding its thermal stability, decomposition mechanism, and sublimation behavior is critical for optimizing deposition conditions in industrial processes. Methods: A comprehensive set of techniques was employed, including thermogravimetric analysis coupled with mass spectrometry and FTIR spectroscopy (TG/MS/FTIR), differential scanning calorimetry (DSC), ATR-FTIR spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), dynamic vapor sorption (DVS) analysis, polarized light microscopy (POM), and molecular modeling. Sublimation kinetics were investigated under isothermal conditions (130–150 °C) using anthracene as reference. Significant findings: DDMEBT exhibits a sequential three-step degradation mechanism, independent of heating rate, with high thermal stability (final residue ∼77 %) attributed to its nonplanar architecture and intermolecular π–π/dipole–dipole interactions. Thermal analysis revealed melting at ∼190 °C, structural rearrangements (196–230 °C), and an amorphous-to-crystalline transition at 270 °C. Sublimation proceeds via zero-order kinetics with low volatility (0.178 μg/min at 130 °C) and an activation energy of 66.5 kJ/mol. The determined vapor pressure (1998–4000 Pa) and transport coefficients confirm a thermally activated, hydrodynamically stable process. These findings establish a reliable basis for sublimation modeling and provide guidelines for optimizing material processing in high-temperature, low-volatility applications.

Mohammed Saleh Alshaikh

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.

Dylan Bissuel, Léo Orveillon, Benjamin Arrondeau et al.

A computing solution combining the GNU Guix functional package manager with the Apptainer container system is presented. This approach provides fully declarative and reproducible software environments suitable for computational materials science. Its versatility and performance enable the construction of complete frameworks integrating workflow managers such as AiiDA, and Ewoks that can be deployed on HPC infrastructures. The efficiency of the solution is illustrated through several examples: (i) AiiDA workflows for automated dataset construction and analysis as well as path-integral molecular dynamics based on ab initio calculations; (ii) workflows for the training of machine-learning interatomic potentials; and (iii) an Ewoks workflow for the automated analysis of coherent X-ray diffraction data in large-scale synchrotron facilities. These examples demonstrate that the proposed environment provides a reliable and reproducible basis for computational and data-driven research in materials science.

Ahmed G. Sayed, Ali Hajjiah, Mehdi Tlija et al.

In this paper, we introduce a comprehensive theoretical study to obtain an optimal highly sensitive fluidic sensor based on the one-dimensional phononic crystal (PnC). The mainstay of this study strongly depends on the high impedance mismatching due to the irregularity of the considered quasi-periodic structure, which in turn can provide better performance compared to the periodic PnC designs. In this regard, we performed the detection and monitoring of the different concentrations of lead nitrate (Pb(NO₃)₂) and identified it as being a dangerous aqueous solution. Here, a defect layer was introduced through the designed structure to be filled with the Pb(NO₃)₂ solution. Therefore, a resonant mode was formed within the transmittance spectrum of the considered structure, which in turn shifted due to the changes in the concentration of the detected analyte. The numerical findings demonstrate the role of the different sequences such as Fibonacci, Octonacci, Thue–Morse, and double period on the performance of the designed PhC detector. Meanwhile, the findings of this study show that the double-period quasi-periodic sequence provides the best performance with a sensitivity of 502.6 Hz/ppm, a damping rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.9</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula>, a maximum quality factor of 8463.5, and a detection limit of 2.45.