jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Longbin Li | Jianchao Jia | Chaosheng Shi | Wei Zeng
Source: Molecules (article, 2022-11-24)
Field: Materials Science — Materials Chemistry

Fine-Tuning Bi2Te3-Copper Selenide Alloys Enables an Efficient n-Type Thermoelectric Conversion Bismuth tellurides is one of the most promising thermoelectric (TE) material candidates in low-temperature application circumstances, but the n-type thermoelectric property is relatively low compared to the p-type counterpart and still needs to be improved. Herein, we incorporated different copper selenides (CuSe, Cu<sub>3</sub>Se<sub>2</sub> and Cu<sub>2-x</sub>Se) into a Bi<sub>2</sub>Te<sub>3</sub> matrix to create the alloy by grinding and successive sintering to enable higher thermoelectric performance. The results demonstrated that all alloys achieved n-type TE characteristics and Bi<sub>2</sub>Te<sub>3</sub>-CuSe exhibited the best Seebeck coefficient and power factor among them. Along with the low thermal conductivity, the maximum dimensionless TE figure of merit (ZT) value of 1.64 at 573 K was delivered for Bi<sub>2</sub>Te<sub>3</sub>-CuSe alloy, which is among the best reported results in the n-type Bi<sub>2</sub>Te<sub>3</sub>-based TE materials to the best of our knowledge. The improved TE properties should be related to the co-doping process of Se and Cu. Our investigation shows a new method to enhance the performance of n-type TE materials by appropriate co-doping or alloying. Materials science ; Thermoelectric effect ; Selenide ; Thermoelectric materials ; Bismuth telluride ; Alloy ; Seebeck coefficient ; Copper ; Doping ; Sintering ; Figure of merit ; Thermoelectric generator ; Metallurgy ; Thermal conductivity ; Bismuth ; Electrical resistivity and conductivity ; Optoelectronics ; Composite material ; Thermodynamics ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Chalcogenide Semiconductor Thin Films
Authors: Haoyu Zhang | Jie Sun | Ge Zhou | Xiaoling Yu | Chuan Wang | Jian Gao
Source: Crystals (article, 2022-12-09)
Field: Materials Science — Metals and Alloys

Influence of Phase Composition on Stress-Corrosion Cracking of Ti-6Mo-5V-3Al-2Fe-2Zr Alloy in 3.5% NaCl Solution The metastable β titanium alloys used in marine engineering applications suffered from stress-corrosion cracking in seawater. The different phase composition leads to the distinct stress-corrosion cracking behaviors of the alloy. In this work, the influence of the phase composition on the stress-corrosion cracking of a novel metastable β titanium alloy Ti-6Mo-5V-3Al-2Fe-2Zr was investigated. The alloys with different phase compositions were prepared by three types of thermal-mechanical processing, i.e., the single β phase (assigned as M(β)), the β phase plus fine α phase (assigned as M(β+fα)), and the β phase plus coarsened α phase (assigned as M(β+cα)). The electrochemical tests and constant-stress loading tests were performed, and the phase composition and microstructure were analyzed by XRD and SEM. The M(β) alloy exhibits the best corrosion resistance as well as the compact properties of oxide films, followed by the M(β+fα) alloy and the M(β+cα) alloy. Tear ridges and a flat facet with an undulating surface were observed on the stress-corrosion cracking fracture surface, which indicated the occurrence of high-degree dislocations movement and localized plastic deformation. Absorption-induced dislocation emission (AIDE) and hydrogen-enhanced localized plasticity (HELP) are the primary mechanisms for the stress-corrosion cracking of the alloy. The increased amount of β phase has a beneficial effect on stress-corrosion cracking resistance. For the alloy with β and α phases, the α phase with wider spacing has an adverse effect on stress corrosion performance. Materials science ; Corrosion ; Alloy ; Stress corrosion cracking ; Metallurgy ; Phase (matter) ; Stress (linguistics) ; Microstructure ; Titanium alloy ; Oxide ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Corrosion Behavior and Inhibition
Authors: Xuan Xu | Sina Khoshsima | Milana Karajic | Jan Balderman | Katarina Marković | Janez Ščančar | Zoran Samardžija | Sašo Šturm | Kristina Žužek Rožman
Source: Journal of Applied Electrochemistry (article, 2022-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrochemical routes for environmentally friendly recycling of rare-earth-based (Sm–Co) permanent magnets Abstract The consumption of critical raw materials, especially those in permanent magnets of Nd–Fe–B and Sm–Co-type, has significantly grown in the past decade. With predictions on further electrification growing exponentially the demand for these materials will even increase. This implies that efforts in assuring sustainability must involve recycling from secondary resources. In recent years the electrochemical approaches in recycling have been extensively investigated and applied owing to their advantages of high efficiency and selectivity, easy operation, low energy consumption, and environmental friendliness. In this paper, we investigate the Sm 2 (Co,Fe,Cu,Zr) 17 permanent magnet leaching process using the anodic oxidation to be paired with the metal deposition on the cathode. Linear sweep voltammetry was performed from − 0.15 to 1 V versus Pt quasi reference electrode that indicated current peaks that would correspond to some preferential leaching of the crystal phases contained in the magnet. The latter was confirmed using the SEM/EDXS analysis. The continuous leaching of the Sm 2 (Co,Fe,Cu,Zr) 17 magnet was performed at a direct current density of 2, 4 and 8 mA cm −2 at the time period of 0–240, 240–480 and 480–720 min, respectively. The ICP-MS results confirmed the leaching of all the metals from the original Sm 2 (Co,Fe,Cu,Zr) 17 permanent magnet. The concentration of Sm 3+ , Cu 2+ , Fe 2+ and Zr 2+ increases linearly along with the leaching time. Reversely the concentration of Co 2+ decreases linearly due to its consumption by electrodeposition of Co, Fe and Cu on the cathode. The presented paired electrochemical process could serve as a starting point for the recycling and recovery of critical raw materials without any acid usage and waste generation. Graphical abstract Leaching (pedology) ; Electrochemistry ; Magnet ; Materials science ; Cathode ; Anode ; Linear sweep voltammetry ; Metallurgy ; Analytical Chemistry (journal) ; Electrode ; Chemistry ; Cyclic voltammetry ; Environmental science ; Environmental chemistry ; Physical chemistry Magnetic Properties of Alloys ; Advancements in Battery Materials ; Extraction and Separation Processes
Authors: Qiang Li | Yang Ren | Qinghua Zhang | Lin Gu | Q. Huang | Hui Wu | Jing Sun | Yili Cao | Kun Lin | Xianran Xing
Source: National Science Review (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

Chemical order-disorder nanodomains in Fe3Pt bulk alloy Chemical ordering is a common phenomenon and highly correlated with the properties of solid materials. By means of the redistribution of atoms and chemical bonds, it invokes an effective lattice adjustment and tailors corresponding physical properties. To date, however, directly probing the 3D interfacial interactions of chemical ordering remains a big challenge. In this work, we deciphered the interlaced distribution of nanosized domains with chemical order/disorder in Fe<sub>3</sub>Pt bulk alloy. HAADF-STEM images evidence the existence of such nanodomains. The reverse Monte Carlo method with the X-ray pair distribution function data reveal the 3D distribution of local structures and the tensile effect in the disordered domains at the single-atomic level. The chemical bonding around the domain boundary changes the bonding feature in the disordered side and reduces the local magnetic moment of Fe atoms. This results in a suppressed negative thermal expansion and extended temperature range in Fe<sub>3</sub>Pt bulk alloy with nanodomains. Our study demonstrates a local revelation for the chemical order/disorder nanodomains in bulk alloy. The understanding gained from atomic short-range interactions within the domain boundaries provides useful insights with regard to designing new functional compounds. Materials science ; Alloy ; Chemical bond ; Chemical physics ; Atomic units ; Condensed matter physics ; Redistribution (election) ; Chemistry ; Physics ; Metallurgy X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Driss Kenfaui | Sophie Guillemet‐Fritsch | Zarel Valdez‐Nava | Lionel Laudebat | Christophe Tenailleau | Pascal Dufour | Marie‐Laure Locatelli
Source: Journal of the European Ceramic Society (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Tuning electrical conductivity in AlN-based ceramics by incorporating graphene Materials science ; Graphene ; Ceramic ; Electrical resistivity and conductivity ; Conductivity ; Composite material ; Nanotechnology ; Electrical engineering ; Physical chemistry Graphene research and applications ; Thermal properties of materials ; Advanced ceramic materials synthesis
Authors: Subham Kumar Saha | Pritha Mondal | Navyashree Vasudeva | Rekha Mahadevu | Dev Kumar Thapa | Biswajit Bhattacharyya | Anand Sharma | Saurav Islam | Phanibhusan S. Mahapatra | T. Phanindra Sai | Samartha Channagiri | Pavithra Bellare | Awadhesh Narayan | N. Ravishankar | Satish Patil | Arindam Ghosh | Anshu Pandey
Source: Superconductor Science and Technology (article, 2022-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unconventional properties of engineered Au–Ag nanostructures Abstract Au–Ag nanostructures comprising of ∼1 nm Ag nanoparticles embedded into an Au matrix show several unconventional optical, electric and magnetic properties. Here, we review progress made towards the preparation of these materials as well as analysis of their structure. Further, electrical and magnetic transitions in these materials are discussed. Finally, we review the properties of these materials as revealed from optical and electron microscopic probes. Materials science ; Nanostructure ; Nanotechnology ; Nanoparticle ; Matrix (chemical analysis) ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Surface and Thin Film Phenomena ; Copper-based nanomaterials and applications
Authors: Umid Jamolov | Giovanni Maizza
Source: Energies (article, 2022-02-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integral Methodology for the Multiphysics Design of an Automotive Eddy Current Damper The present work reports an integrated (experimental and numerical) methodology that combines the development of a finite element multiphysics model with an experimental strategy to optimally design an eddy current damper for automotive suspensions. The multiphysics model couples the whole set of time-dependent electromagnetic, thermal, mechanical, and fluid–wall interaction (CFD) partial differential equations. The developed FE model was validated against both literature model predictions and in-house experimental data. The electromagnetic model takes into account the magnetic material characteristics of the ferromagnetic material and iron poles. Loss separation and the Jiles–Atherton hysteresis models were invoked to determine the heat generated in the soft iron parts. The computation of the fluid–wall interaction phenomena in the air gap allowed for the prediction of the temperature field across the solid materials, including the magnets. The design of the EC damper addresses the effects of the geometries of the stator and rotor, as they are the most critical geometries for maximizing the functions of an eddy current damper. The magneto-thermal simulations suggested that the heating of the permanent magnets remains within a safe region over the investigated operational frequency range of the eddy current damper. Multiphysics ; Eddy current ; Damper ; Finite element method ; Magnet ; Mechanical engineering ; Mechanics ; Stator ; Rotor (electric) ; Engineering ; Structural engineering ; Physics ; Electrical engineering Magnetic Properties and Applications ; Magnetic Bearings and Levitation Dynamics ; Vibration Control and Rheological Fluids
Authors: Sergei Kurennov | Kostiantyn Barakhov | Oleksii Vambol
Source: RADIOELECTRONIC AND COMPUTER SYSTEMS (article, 2022-10-04)
Field: Materials Science — General Materials Science

Topological optimization of a symmetrical adhesive joint. Island model of genetic algorithm Modern additive technologies make it possible to create structures of variable thickness and of any shape. Thus, designers face problems of optimal design of a new type, and these are problems of topological optimization. Such problems are to determine the optimal form of the structure or the optimal distribution of material over the structure. As a rule, the criterion of optimality is the mass of the structure. However, the structure must retain its bearing capacity under a certain load. The symmetric two-shear adhesive joint of the main plate with two overlays of the same shape on both sides is the object of study in this article. The main goal of this study was to determine the optimal form of overlays with variable thicknesses under certain restrictions. The main restriction is the strength of the structure. Furthermore, additional restrictions are imposed on the minimum and maximum thickness of the overlay. Therefore, the solution to the problem is presented in the form of a set of the following tasks: building a mathematical model of the adhesive joint, building a numerical solution to the primal problem using the finite difference method, and building a genetic optimization algorithm. In the presented article, to improve the convergence of the genetic algorithm is proposed to use an island model that consists of several populations. The main feature of the proposed model of the genetic algorithm lies in the fact that on one of the "islands" mutations occur more frequently and with higher dispersion than on the other two "islands". On the one hand, this decision ensures a high rate of evolutionary selection, and on the other hand, the stability of the results is achieved. Several modeling problems are solved in this article. The main results of this research include the following: nonlinear dependence of the overlay length on the applied load was determined; restrictions on the minimum thickness of the overlay, which cause the appearance of a certain “plateau” at the edge of the overlay, the thickness of which is equal to the minimum allowable were defined. Convergence (economics) ; Overlay ; Genetic algorithm ; Mathematical optimization ; Feature (linguistics) ; Algorithm ; Computer science ; Optimization problem ; Mathematics ; Topology (electrical circuits) ; Combinatorics Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Engineering Technology and Methodologies
Authors: Haixia Sun | Shujuan Zhang | Rui Ren | Jianxin Xue | Huamin Zhao
Source: Foods (article, 2022-08-20)
Field: Chemistry — Analytical Chemistry

Detection of Soluble Solids Content in Different Cultivated Fresh Jujubes Based on Variable Optimization and Model Update To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R<sup>2</sup>), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R<sup>2</sup>, RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field. Mean squared error ; Offset (computer science) ; Mathematics ; Near-infrared spectroscopy ; Euclidean distance ; Algorithm ; Biological system ; Statistics ; Computer science ; Artificial intelligence ; Physics ; Biology ; Optics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Advanced Chemical Sensor Technologies
Authors: Michael E. Deagen | Dylan J. Walsh | Debra J. Audus | Kenneth Kroenlein | Juan Pablo | Kaoru Aou | Kyle Chard | Klavs F. Jensen | Bradley D. Olsen
Source: Cell Reports Physical Science (article, 2022-10-27)
Field: Materials Science — Materials Chemistry

Networks and interfaces as catalysts for polymer materials innovation Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial intelligence (AI) and machine learning (ML)—play a foundational role in materials research and development. This perspective draws attention to the roles of networks and interfaces—of and between humans and machines—for the purpose of generating knowledge and accelerating innovation. Polymers, a class of materials with massive global impact, present a unique opportunity for the application of informatics and automation to pressing societal challenges. To develop these networks and interfaces in polymer science, the Community Resource for Innovation in Polymer Technology (CRIPT)—a polymer data ecosystem based on novel polymer data model, representation, search, and visualization technologies—is introduced. The ongoing co-design efforts engage stakeholders in industry, academia, and government to uncover rapidly actionable, high-impact opportunities to build networks, bridge interfaces, and catalyze innovation in polymer technology. Computer science ; Data science ; Resource (disambiguation) ; Informatics ; Automation ; Knowledge management ; Nanotechnology ; Engineering ; Materials science ; Mechanical engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Venkateswaran Shekar | Vincent F. Yu | Benjamin J. Garcia | D. Benjamin Gordon | Gemma E. Moran | David M. Blei | Loı̈c M. Roch | Alberto García-Durán | Mansoor Ani Najeeb | Margaret Zeile | Philip W. Nega | Zhi Li | Mina A. Kim | Emory M. Chan | Alexander J. Norquist | Sorelle A. Friedler | Joshua Schrier
Source: ChemRxiv (preprint, 2022-07-19)
Field: Materials Science — Materials Chemistry

Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models Machine learning is a useful tool for accelerating materials discovery, however it is a challenge to develop accurate methods that successfully transfer between domains while also broadening the scope of reaction conditions considered. In this paper, we consider how active- and transfer-learning methods can be used as building blocks for predicting reaction outcomes of metal halide perovskite synthesis. We then introduce a serendipity-based recommendation system that guides these methods to balance novelty and accuracy. The model-agnostic recommendation system is tested across active- and transfer-learning algorithms, using laboratory experiments for training and testing and a time-separated hold out that includes four different chemical systems. The serendipity recommendation system achieves high accuracy while increasing the scope of the synthesis conditions explored. Serendipity ; Novelty ; Scope (computer science) ; Recommender system ; Computer science ; Transfer of learning ; Artificial intelligence ; Plan (archaeology) ; Machine learning ; Knowledge transfer ; Data science ; Biochemical engineering ; Knowledge management ; Engineering Machine Learning in Materials Science ; Expert finding and Q&A systems ; Advanced Photocatalysis Techniques
Authors: A. Gilad Kusne | Austin McDannald | Brian DeCost | Corey Oses | Cormac Toher | Stefano Curtarolo | Apurva Mehta | Ichiro Takeuchi
Source: Frontiers in Physics (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior. Computer science ; Focus (optics) ; Artificial intelligence ; Class (philosophy) ; Phase (matter) ; Physical system ; Physical science ; Machine learning ; Data science ; Physics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ф. Х. Уракаев | Mukhambetkali Burkitbayev | Natalya Khan
Source: International Journal of Biology and Chemistry (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Biological activity of sulfur nanoparticles in the sulfur-dimethyl sulfoxide-water system For the first time a sulfur solution in dimethyl sulfoxide (DMSO) with concentration of 1.592 g/L [1] was used to prepare sulfur nanoparticles (nanosulfur). Synthesis and stabilization of colloidal sulfur particles was carried out at 298K (25°C) by diluting the sulfur solution in DMSO with water 10 to 1000 times. The stable sizes of nanosulfur measured by dynamic light scattering were ~ 100 nm. The results were confirmed both qualitatively and quantitatively using X-ray and electron microscopy methods. The antibacterial activity of the nanosulfur on the phytopathogenic bacterium Erwinia amylovora and fungus Fusarium solani was tested. Sulfur ; Dimethyl sulfoxide ; Erwinia ; Sulfoxide ; Chemistry ; Fusarium solani ; Nanoparticle ; Dynamic light scattering ; Nuclear chemistry ; Organic chemistry ; Materials science ; Microbiology ; Nanotechnology ; Biochemistry ; Biology Structural mechanics and materials