Hasil untuk "Materials Science"

M. Brongersma, N. Halas, P. Nordlander

Frederic E. Bock, R. Aydin, C. Cyron et al.

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

Weitong Lin, Liutao Chen, Si-Mian Liu et al.

In-situ nanopillar compression tests were conducted to evaluate slip strengths of prismatic <a> and pyramidal <c+a> in Zr-Nb alloys. The resolved shear stress (RSS) for prismatic <a> slip in Zr-2.5Nb is about five times that of Zr-1.0Nb at 298 K, and RSS for pyramidal <c+a> slip of Zr-2.5Nb is about twice that of Zr-1.0Nb at 623 K, indicating that a higher density of β-Nb precipitates can appreciably enhance slip resistance. Moreover, RSS for prismatic <a> slip in Zr-2.5Nb at 623 K is approximately one-tenth of that at 298 K, suggesting that the strengthening effect substantially reduces at reactor operating temperatures.

Ayenew Amare, Mezigebu Belay, Zewudu Wondimagegn

Abstract The use of aluminium matrix composites (AMCs) in advanced engineering applications has increased due to their improved mechanical properties, such as hardness, ultimate tensile strength, toughness, wear, and corrosion resistance. Although synthetic ceramic particles enhance the mechanical properties of aluminium matrix composites, they also increase weight and cost due to their high density. Using inexpensive and lightweight reinforcements like industrial and agro-waste can reduce cost and weight, but may compromise some mechanical properties. This study investigates the mechanical properties and microstructure of AA6063 aluminium alloy reinforced with corn cob ash (CCA) and Al2O3 particles. The composites were produced using the two-step stir casting method, incorporating varying weight fractions of CCA and Al2O3 (ranging from 5% to 15% in 5% intervals). Optimal properties were achieved using Taguchi with grey relational analysis. Mechanical testing was carried out, such as tensile, compression, and hardness. Microstructural analysis and phase identification were performed using an optical microscope and X-ray diffraction (XRD), respectively. The findings demonstrate that incorporating CCA and Al2O3 reinforcements leads to significant improvements in the mechanical properties of AA6063 aluminium alloy, with ultimate tensile strength increasing by 49% at 10% CCA and Al2O3, compressive strength by 44.4% at 15% CCA and Al2O3, and hardness by 31% at 15% CCA and Al2O3. Although there was a slight decrease in ultimate tensile strength at 15% CCA and Al2O3, it remained higher than that of the unreinforced AA6063 alloy. The microstructural analysis images revealed the uniform distribution of the reinforcements and the positive influence of Al2O3-CCA reinforcements on the mechanical properties. Furthermore, XRD confirmed the presence of reinforced Al2O3-CCA particles in the produced composite samples. Therefore, the mechanical properties of AA6063 aluminium alloy were significantly improved by incorporating Al2O3-CCA reinforcements, suggesting its potential for enhanced applications in engineering.

Zhonghui Shen, Hanyan Liu, Yang Shen et al.

With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution of the materials research paradigm. Here, taking dielectric capacitors and lithium‐ion batteries as two representative examples, we review substantial advances of machine learning in the research and development of energy storage materials. First, a thorough discussion of the machine learning framework in materials science is presented. Then, we summarize the applications of machine learning from three aspects, including discovering and designing novel materials, enriching theoretical simulations, and assisting experimentation and characterization. Finally, a brief outlook is highlighted to spark more insights on the innovative implementation of machine learning in materials science.

Tengyue Pan, Chengming Jiang, Xinmin Shen et al.

In today’s data-driven age, the thermal properties of computer transistors play an important role. In this research, finite element simulation is employed to construct the structural model of the primary components within a computer chassis, and the thermal performance is evaluated based on ambient temperature, thermal conductivity, and heat dissipation rate. By combining the particle swarm optimization algorithm with numerical simulation for joint simulation and structural optimization, the component layout was optimized to reduce the working temperature. The results show that when the background temperature, that is, the ambient temperature, rises from −20 °C to 60 °C, the maximum operating temperature of the computer is approximately 88 °C. The maximum temperature is mainly in the transistor core and the minimum temperature is in the intake grille, and the operating temperature of the optimized structure decreases by approximately 10 °C. The research shows that the operating temperature is most sensitive to the change of background temperature, and the transistor core is the main heating source. The maximum temperature can be reduced by rationally adjusting the position of the components. This study provides a reference for analyzing the thermal performance of computers and optimizing structures.

Irfan Tahir, Christopher Foley, Rachael Floreani

Innovative changes to our current food system are needed, and one solution is cultivated meat, which uses modern engineering, materials science, and biotechnology to produce animal protein. This article highlights the advantages of incorporating whey protein isolate (WPI) and β-lactoglobulin (β-LG) into hydrogel networks to aid cell growth on cultivated meat scaffolds. The protein and polysaccharide (i.e., alginate) components of the scaffolds are food-grade and generally regarded as safe ingredients, enabling the transition to more food-safe, edible, and nutritious scaffolds. The impact of WPI and varying properties on cell performance was evaluated; alginate concentration and the addition of proteins into the hydrogels significantly altered their stiffness and strength. The results of this study demonstrate the innocuous nature of novel scaffolds and reveal enhanced cell proliferation on WPI and β-LG-modified groups compared to standard biomaterial controls. This work serves as a stepping stone for more comprehensive analyses of WPI, β-LG, and alginate scaffolds for use in cultivated meat research and production.

Paula Palacios, Abdelrahman M. Askar, Francisco Pasadas et al.

Abstract This work presents a 2D tellurium (Te)‐based diode that exploits the doping achieved by atomic layer deposited (ALD) Al2O3 to enhance its rectifying performance. The proposed device comprises a Schottky junction that is dielectric‐doped to significantly reduce the reverse bias current. A boosted current responsivity four times higher compared to that of undoped devices is achieved, maximizing the performance for radio frequency (RF) power detectors. The application measurement results demonstrate sensitivities as low as −45 dBm, and at −30 dBm RF input power outstanding tangential responsivities up to 6.5 kV W–1, 4.3 kV W–1, and 650 V W–1 at 0.5, 1, and 2.5 GHz, respectively, while reaching linear dynamic range (DR) of over 30 dB. These are the highest reported values for 2D‐based material devices by almost two orders of magnitude. Furthermore, the DR is ≈10 dB larger compared to state‐of‐the‐art power detectors based on bulk semiconductors.

Lingmei Kong, Yun Luo, Qianqian Wu et al.

Abstract Light-emitting diodes (LEDs) based on perovskite semiconductor materials with tunable emission wavelength in visible light range as well as narrow linewidth are potential competitors among current light-emitting display technologies, but still suffer from severe instability driven by electric field. Here, we develop a stable, efficient and high-color purity hybrid LED with a tandem structure by combining the perovskite LED and the commercial organic LED technologies to accelerate the practical application of perovskites. Perovskite LED and organic LED with close photoluminescence peak are selected to maximize photon emission without photon reabsorption and to achieve the narrowed emission spectra. By designing an efficient interconnecting layer with p-type interface doping that provides good opto-electric coupling and reduces Joule heating, the resulting green emitting hybrid LED shows a narrow linewidth of around 30 nm, a peak luminance of over 176,000 cd m−2, a maximum external quantum efficiency of over 40%, and an operational half-lifetime of over 42,000 h.

Hans H Falk, Stefanie Eckner, Konrad Ritter et al.

The chalcopyrite alloy (Ag,Cu)(In,Ga)Se _2 is a highly efficient thin film solar cell absorber, reaching record efficiencies above 23%. Recently, a peculiar behavior in the bond length dependence of (Ag,Cu)GaSe _2 was experimentally proven. The common cation bond length, namely Ga–Se, decreases with increasing Ag/(Ag + Cu) ratio even though the crystal lattice expands. This is opposite to the behavior observed for Cu(In,Ga)Se _2 , where all bond lengths increase with increasing lattice size. To better understand this peculiar bond length behavior, element-specific bond lengths of (Ag,Cu)InSe _2 and Ag(In,Ga)Se _2 alloys are determined using extended x-ray absorption fine structure spectroscopy. They show that the peculiar bond length dependence occurs only for (Ag,Cu) alloys, independent of the species of common cation (In or Ga). The bond lengths are used to determine the anion displacements and to estimate their contribution to the bandgap bowing. Again, both behaviors differ significantly depending on the type of alloyed cation. A valence force field approach, relaxing bond lengths and bond angles, is used to describe the structural distortion energy for a comprehensive set of I–III–VI _2 and II–IV–V _2 chalcopyrites. The model reveals bond angle distortions as main driving factor for the tetragonal distortion and reproduces the literature values with less than 10% deviation. In contrast, the peculiar bond length dependence is not reproduced, demonstrating that it originates from electronic effects beyond the scope of this structural model. Thus, a fundamental understanding of bond length behavior and tetragonal distortion is achieved for chalcopyrite materials, benefiting their technological applications such as high efficiency thin film photovoltaics.

Cameron J. Hargreaves, Michael W. Gaultois, Luke M. Daniels et al.

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.

Mingxuan Zhang, Zhipeng Ma, Dai Geng et al.

In this work, we present the capillary filling behavior of Sn-9Zn molten solder on the surface of SiC ceramic with different initial contact angles with and without electromagnetic ultrasonic action. The results showed that the capillary filling behavior did not easily occur in the native state. Electromagnetic ultrasonic action could promote the capillary filling behavior of the Sn-9Zn solder on the surface of SiC ceramic. When the peak current was 10 A, the maximum value of the capillary filling length of the solder at a contact angle of 90° was 11.56 mm. When the peak current was 100 A, the solder on the left side of the gap broke. The surrounding air induced an alternating magnetic field whose direction changed periodically; with opposite directions of the magnetic field generated by the left and right coils. The direction of the Lorentz force on the left side of the solder was opposite to the right side, and the alternating magnetic field influenced the direction of the Lorentz force inside the solder, where the Lorentz force inside the solder was directly proportional to the peak current. The sound pressure inside the solder also changed to positive and negative pressure in one cycle, where the maximum value was 1.67 × 105 Pa.

Chang Liu, Jiaxin Zhang, Xin Zhao et al.

Traditional titanium alloy implant surfaces are inherently smooth and often lack effective osteoinductive properties. To overcome these limitations, coating technologies are frequently employed to enhance the efficiency of bone integration at the implant–host bone interface. Hierarchical zeolites, characterized by their chemical stability, can be applied to 3D-printed porous titanium alloy (pTi) surfaces as coating. The resulting novel implants with a “microporous-mesoporous-macroporous” spatial gradient structure can influence the behavior of adjacent cells; thereby, promoting the integration of bone at the implant interface. Consequently, a thorough exploration of various preparation methods is warranted for hierarchical zeolite coatings with respect to biocompatibility, coating stability, and osteogenesis. In this study, we employed three methods: in situ crystal growth, secondary growth, and layer-by-layer assembly, to construct hierarchical zeolite coatings on pTi, resulting in the development of a gradient structure. The findings of this investigation unequivocally demonstrated that the LBL-coating method consistently produced coatings characterized by superior uniformity, heightened surface roughness, and increased hydrophilicity, as well as increased biomechanical properties. These advantages considerably amplified cell adhesion, spreading, osteogenic differentiation, and mineralization of MC3T3-E1 cells, presenting superior biological functionality when compared to alternative coating methods. The outcomes of this research provide a solid theoretical basis for the clinical translation of hierarchical zeolite coatings in surface modifications for orthopedic implants.

Bo Bi, An-Qi Dong, Miao-Miao Shi et al.

The traditional method for synthesizing NH3 is the Haber–Bosch process which results in high‐fuel consumption and environmental pollution. Therefore, ecofriendly electrochemical synthesis of NH3 through nitrate (NO3−) reduction is a good choice. Herein, an integral Au/Cu electrode to catalyze NO3− reduction to NH3 is introduced. The catalyst exhibits not only the highest NH3 yield rate (73.4 mg h−1 cm−2) up to now but also a very high Faradaic efficiency of 98.02% at −0.7 V at room temperature. It is commonly believed that the transformation of NO3− to nitrite (NO2−) is an obstacle to the NH3 generation from NO3− reduction. Surprisingly, unlike most of the other catalysts, Au/Cu exhibits better activity for NO3− reduction than that for NO2− reduction. Based on the detailed experimental and density functional theory calculations, the excellent performance of Au/Cu for selective NO3− reduction lies in the enhanced adsorption capabilities of Au/Cu to NO3− in the alkaline environment and the lower energy barriers of the electrochemical reduction reaction.

S. Pennycook

Mihaela Savin, Carmen-Marinela Mihailescu, Carmen Moldovan et al.

Nanocomposite materials have seen increased adoption in a wide range of applications, with toxic gas detection, such as carbon monoxide (CO), being of particular interest for this review. Such sensors are usually characterized by the presence of CO absorption sites in their structures, with the Langmuir reaction model offering a good description of the reaction mechanism involved in capturing the gas. Among the reviewed sensors, those that combined polymers with carbonaceous materials showed improvements in their analytical parameters such as increased sensitivities, wider dynamic ranges, and faster response times. Moreover, it was observed that the CO reaction mechanism can differ when measured in mixtures with other gases as opposed to when it is detected in isolation, which leads to lower sensitivities to the target gas. To better understand such changes, we offer a complete description of carbon nanostructure-based chemosensors for the detection of CO from the sensing mechanism of each material to the water solution strategies for the composite nanomaterials and the choice of morphology for enhancing a layers’ conductivity. Then, a series of state-of-the-art resistive chemosensors that make use of nanocomposite materials is analyzed, with performance being assessed based on their detection range and sensitivity.

Zishan Han, Daliang Han, Zhe Chen et al.

Cu metal suffers from unavoidable and uncontrollable surface reconstruction during electrocatalysis. The authors here guide the reconstruction process in a favorable direction using trace amount of electrolyte additives, promoting CO2 electroreduction to CH4.

O. J. I. Kramer, O. J. I. Kramer, O. J. I. Kramer et al.

<p>Natural particles are frequently applied in drinking water treatment processes in fixed bed reactors, fluidised bed reactors, and sedimentation processes to clarify water and to concentrate solids. When particles settle, it has been found that, in terms of hydraulics, natural particles behave differently when compared to perfectly round spheres. To estimate the terminal settling velocity of single solid particles in a liquid system, a comprehensive collection of equations is available. For perfectly round spheres, the settling velocity can be calculated quite accurately. However, for naturally polydisperse non-spherical particles, experimentally measured settling velocities of individual particles show considerable spread from the calculated average values.</p> <p>This work aims to analyse and explain the different causes of this spread. To this end, terminal settling experiments were conducted in a quiescent fluid with particles varying in density, size, and shape. For the settling experiments, opaque and transparent spherical polydisperse and monodisperse glass beads were selected. In this study, we also examined drinking-water-related particles, like calcite pellets and crushed calcite seeding material grains, which are both applied in drinking water softening. Polydisperse calcite pellets were sieved and separated to acquire more uniformly dispersed samples. In addition, a wide variety of grains with different densities, sizes, and shapes were investigated for their terminal settling velocity and behaviour. The derived drag coefficient was compared with well-known models such as the one of Brown and Lawler (2003).</p> <p>A sensitivity analysis showed that the spread is caused, to a lesser extent, by variations in fluid properties, measurement errors, and wall effects. Natural variations in specific particle density, path trajectory instabilities, and distinctive multi-particle settling behaviour caused a slightly larger degree of the spread. In contrast, a greater spread is caused by variations in particle size, shape, and orientation.</p> <p>In terms of robust process designs and adequate process optimisation for fluidisation and sedimentation of natural granules, it is therefore crucial to take into consideration the influence of the natural variations in the settling velocity when using predictive models of round spheres.</p>