jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S B Abrish Aaditya | P. B. Karandikar | Mahima Jain | Jaywant Suryawanshi | Aditi Bellam | Datta. S. Chavan | Rajesh M. Holmukhe | Parag Chaudhari | Abhishek Gandhar
Source: Journal of Information and Optimization Sciences (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal profiling of ultracapacitor modules : A layered analysis This study explores heat dissipation in supercapacitor modules tailored for energy-intensive applications. The research investigates the influence of different electrode material combinations on key performance parameters. Employing MATLAB and COMSOL simulations, the thermal conductivity of various layers within the supercapacitor module is analyzed. Findings reveal that polythene separators, responsible for encapsulating and segregating the module’s electrodes, represent critical points for controlled heat transfer. Significantly, the use of metal oxide in electrode construction contributes positively to regulated heat dissipation. Substituting polythene separators with synthesized materials is proposed to optimize heat dissipation. This research pioneers a data-driven analysis and software simulations to augment the study’s outcomes. Supercapacitor ; Dissipation ; Materials science ; Thermal management of electronic devices and systems ; Electrode ; MATLAB ; Thermal conductivity ; Heat transfer ; Thermal ; Software ; Oxide ; Heat energy ; Computer science ; Nuclear engineering ; Mechanical engineering ; Composite material ; Mechanics ; Electrochemistry ; Thermodynamics ; Engineering ; Metallurgy ; Chemistry ; Physics Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electric and Hybrid Vehicle Technologies
Authors: Min Young Cho | Kyunglim Pyo | Byung Do Lee | Hee-jeong Kim | Jiyoon Shin | Jung Yong Seo | Woon Bae Park | Kee‐Sun Sohn
Source: Small (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Discovering Multi‐Compositional Li‐Argyrodite Solid‐State Electrolytes via Experimental Active Learning Significant research has focused on doping third-party elements into representative Li-Argyrodites, which typically consist of a metal cation, a sulfide anion, and a halide. These efforts have generally been limited to doping or substituting a single element at each atomic site in the Argyrodite structure, resulting in, at most, binary combinations at each site. Multi-elemental doping or substitution poses a challenge due to the so-called combinatorial explosion issue. Here, the study reports quaternary and ternary combinations at either the cation or anion sites, optimizing the composition for ambient-temperature ionic conductivity. Managing such a complex multi-compositional system requires artificial intelligence that surpasses human intuition. A particle swarm optimization (PSO) algorithm is employed within an active learning framework to tackle this multi-dimensional optimization problem. Unlike typical active learning approaches that rely on theoretical computational data, the process is driven by experimental data from the synthesis and characterization of a few hundred multi-compositional Argyrodite samples This experimental active learning approach ultimately enables identifying a novel multi-compositional Li-Argyrodite, exhibiting ambient-temperature ionic conductivity of 13.02 mS cm⁻¹ and enhanced cell performance, with the composition Li<sub>6.425</sub>Ge<sub>0.25</sub>Si<sub>0.375</sub>Sb<sub>0.375</sub>S<sub>4.8</sub>I<sub>1.2</sub>. Materials science ; Ternary operation ; Doping ; Ionic conductivity ; Electrolyte ; Fast ion conductor ; Ionic bonding ; Inorganic chemistry ; Ion ; Computer science ; Chemistry ; Physical chemistry ; Optoelectronics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Ketankumar Gayakvad | Ketaki K. Patankar | Sunil Swaminath Swami | Jyoti V. Devkar | V. L. Mathe
Source: Suranaree Journal of Science and Technology (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

FABRICATION AND STRUCTURAL STUDIES OF SPIN-COATED COBALT FERRITE LAYERS Cobalt ferrite coating of various layers is fabricated on fluorine-doped tin oxide (FTO) substrate using a spin coater. A precursor sol of 0.1 M metal ion concentration is employed to develop layers of cobalt ferrite on FTO substrate. Various layers of cobalt ferrite are deposited on the FTO substrate at a rotation speed of 3000 revolutions per minute for 30 seconds. After deposition of each layer, the sample is kept for thermal baking at 200℃ for 5 min. Three samples of three different coats ranging from single layer to three layers are fabricated and thermally baked. These samples are then annealed at 500℃ for 7 hours in air ambient. Structural studies namely phase formation, chemical element analysis and surface morphology of the sample is carried out respectively using X-ray diffractometer (XRD), Energy-dispersive X-ray analysis (EDX) and Field Emission Scanning Electron Microscope (FESEM). These structural characterizations confirm the phase formation of cobalt ferrite. Spin-coated layers of cobalt ferrite can be widely employed in memristive applications due to its excellent resistive switching characteristics. The typical memristive applications are resistive random-access memory (RRAM), artificial intelligence (A.I.), neuromorphic computing, and hardware security. Such deposited cobalt ferrite layers also find applications in gas sensor fabrication, magneto-optic recording technology, and spintronics devices. Fabrication ; Cobalt ; Materials science ; Ferrite (magnet) ; Cobalt ferrite ; Nanotechnology ; Metallurgy ; Composite material Magnetic Properties and Synthesis of Ferrites
Authors: Fatemeh Etezadi | Shunichi Ito | Kosuke Yasui | Rodi Abdalkader | Itsunari Minami | Motonari Uesugi | Namasivayam Ganesh Pandian | Haruko Nakano | Atsushi Nakano | Daniel M. Packwood
Source: Journal of Chemical Information and Modeling (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Molecular Design for Cardiac Cell Differentiation Using a Small Data Set and Decorated Shape Features The discovery of small organic compounds for inducing stem cell differentiation is a time- and resource-intensive process. While data science could, in principle, streamline the discovery of these compounds, novel approaches are required due to the difficulty of acquiring training data from large numbers of example compounds. In this paper, we present the design of a new compound for inducing cardiomyocyte differentiation using simple regression models trained with a data set containing only 80 examples. We introduce decorated shape descriptors, an information-rich molecular feature representation that integrates both molecular shape and hydrophilicity information. These models demonstrate improved performance compared to ones using standard molecular descriptors based on shape alone. Model overtraining is diagnosed using a new type of sensitivity analysis. Our new compound is designed using a conservative molecular design strategy, and its effectiveness is confirmed through expression profiles of cardiomyocyte-related marker genes using real-time polymerase chain reaction experiments on human iPS cell lines. This work demonstrates a viable data-driven strategy for designing new compounds for stem cell differentiation protocols and will be useful in situations where training data is limited. Computer science ; Set (abstract data type) ; Representation (politics) ; Training set ; Molecular descriptor ; Computational biology ; Feature (linguistics) ; Process (computing) ; Data set ; Simple (philosophy) ; Artificial intelligence ; Biological system ; Machine learning ; Biology ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Molecular Biology Techniques and Applications
Authors: Weidong Wang | Runhu Xiao | Shiwei Zhu | Changsheng Song
Source: Computational Materials Science (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Data driven studies of magnetic ground state and transition temperature in two-dimensional magnets Magnet ; Ground state ; Condensed matter physics ; Materials science ; Physics ; Mechanical engineering ; Atomic physics ; Engineering Machine Learning in Materials Science ; Magnetic properties of thin films ; Magnetic and transport properties of perovskites and related materials
Authors: Akihiro Nakanishi | Yukinori Koyama | Takayuki Nakanishi | Shiro Funahashi | Hisanori Yamane | Kohsei Takahashi | Naoto Hirosaki | Hidekazu Ikeno | Takashi Takeda
Source: Journal of Alloys and Compounds (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Discovery of narrow-band emitting phosphor Na5Al3F14:Eu2+ using local structure similarity Phosphor ; Similarity (geometry) ; Materials science ; Optoelectronics ; Computer science ; Artificial intelligence Luminescence Properties of Advanced Materials ; Inorganic Fluorides and Related Compounds ; Solid-state spectroscopy and crystallography
Authors: Qingyun Hu | Lingyue Zhu | Genmao Zhuang | Hong Wang | Yang Ren | Jian Hui
Source: Materials Genome Engineering Advances (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

High‐throughput study of X‐ray‐induced synthesis of flower‐like Cu<sub>x</sub>O Abstract Cu x O with flower‐like hierarchical structures has attracted significant research interest due to its intriguing morphologies and unique properties. The conventional methods for synthesizing such complex structures are costly and require rigorous experimental conditions. Recently, the X‐ray irradiation has emerged as a promising method for the rapid fabrication of precisely controlled Cu x O shapes in large areas under environmentally friendly conditions. Nevertheless, the morphological regulation of the X‐ray‐induced synthesis of the Cu x O is a multi‐parameter optimization task. Therefore, it is essential to quantitatively reveal the interplay between these parameters and the resulting morphology. In this work, we employed a high‐throughput experimental data‐driven approach to investigate the kinetics of X‐ray‐induced reactions and the impact of key factors, including sputtering power, film thickness, and annealing of precursor Cu thin films on the morphologies of Cu x O. For the first time, the flower‐like Cu x O nanostructures were synthesized using X‐ray radiation at ambient condition. This research proposes an eco‐friendly and cost‐effective strategy for producing Cu x O with customizable morphologies. Furthermore, it enhances comprehension of the underlying mechanisms of X‐ray‐induced morphological modification, which is essential for optimizing the synthesis process and expanding the potential applications of flower‐like structures. Throughput ; X-ray ; Materials science ; Physics ; Computer science ; Optics ; Operating system Copper-based nanomaterials and applications ; ZnO doping and properties ; Catalytic Processes in Materials Science
Authors: Yoshikuni Teramoto | Shinji Ogoe | Takashi Endo
Source: Polymer Composites (article, 2024-09-02)
Field: Materials Science — Polymers and Plastics

Accelerating development in wood‐plastic composites: Presenting key material properties and demonstrating statistical methods Abstract In a time when global initiatives to lower CO 2 emissions are accelerating the shift towards bio‐based products, various efforts are being made to develop wood plastic composites (WPCs) and similar bio‐composites using a variety of raw materials. This paper first showcases benchmark material properties serving as standards and models the composition, for polypropylene‐based WPCs integrating wood flour and maleic anhydride‐modified polypropylene. Subsequently, by applying statistical modeling techniques, we demonstrate an adaptive experimental design that efficiently and rapidly adjusts formulations. Beginning with an empirical dataset (24 samples), we established high‐performing models that articulate the individual contributions of the constituents on ten vital properties of WPCs. Our adaptive experimental design, leveraging nonlinear partial least squares regression and the Bayesian optimization, successfully refined formulations to enhance bending and impact strengths. Notably, we proposed optimal conditions starting from just eight samples with minimal iterations. This study shows that statistical techniques can quickly optimize WPC formulations, making the overall development process faster. By addressing the multiple conflicting properties, these techniques can greatly reduce the time and effort needed for development. Highlights Applied several regression methods to analyze ten properties of PP‐based WPCs. Showcased distinct impacts of the formulations of WF and MAPP on WPCs. Implemented the adaptive experimental design for formulation optimization. Enhanced bending and impact strengths in the formulations of WPCs. Advanced sustainable material development with data‐driven approach. Materials science ; Composite material ; Key (lock) ; Computer science Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Aggelos Ferikoglou | Andreas Kosmas Kakolyris | Dimosthenis Masouros | Dimitrios Soudris | Sotirios Xydis
Source: ACM Transactions on Reconfigurable Technology and Systems (article, 2024-10-26)
Field: Materials Science — Materials Chemistry

CollectiveHLS: A Collaborative Approach to High-Level Synthesis Design Optimization High-Level Synthesis (HLS) has played a pivotal role in making FPGAs accessible to a broader audience by facilitating high-level device programming and rapid microarchitecture customization through the use of directives. However, manually selecting the right directives can be a formidable challenge for programmers lacking a hardware background. This article presents CollectiveHLS, an ultra-fast, knowledge-driven approach to optimizing HLS designs. It automates the identification and application of optimal directive configurations from the original source code, focusing on minimizing design latency and ensuring synthesizability. This optimization approach is entirely data-driven, offering a generalized HLS tuning solution without reliance on Quality of Result (QoR) models or meta-heuristics. CollectiveHLS is designed, implemented, and evaluated using around 60 applications sourced from well-established benchmark suites and GitHub repositories, all running on a Xilinx UltraScale + MPSoC ZCU104. It achieves an average geometric mean speedup of up to \(23.1\times\) compared to the official source code without directives, while maintaining synthesizability and feasibility rates of 100% and 96.6%, respectively, matching those of Vitis, the industry-standard framework for FPGA acceleration. Comparisons with resource over-provisioning, traditional genetic algorithm-based Design Space Exploration (DSE), and State-of-the-Art (SotA) approaches demonstrate that CollectiveHLS produces designs of comparable quality \(14.6\times\) faster on average. These results underscore the potential of our approach as an ultra-fast and automated solution for HLS optimization. Computer science ; High-level synthesis ; Computer architecture ; Embedded system Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Systems Engineering Methodologies and Applications
Authors: Xingze Zhao | Jijun Liu | Sihang Chen | Hang Su | Guang Zhao | Yining Wu | Wenyue Guo | Hao Ren
Source: Journal of Molecular Liquids (article, 2024-05-12)
Field: Materials Science — Materials Chemistry

Interfacial characteristics and foam stability: A microscopic perspective from molecular dynamics simulation Molecular dynamics ; Perspective (graphical) ; Stability (learning theory) ; Materials science ; Dynamics (music) ; Chemical physics ; Nanotechnology ; Statistical physics ; Chemistry ; Physics ; Computer science ; Computational chemistry ; Mathematics ; Geometry Pickering emulsions and particle stabilization ; Phase Equilibria and Thermodynamics ; Surfactants and Colloidal Systems
Authors: Madaniyyu Sulaiman Yahaya | Kapil Nahar | Dinesh Kumar | Habib Muhammad Usman | Abdulhaq Saleh Gambo | Tijjani Aminu Umar | Mustapha Sulaiman
Source: Buletin Ilmiah Sarjana Teknik Elektro (article, 2024-10-08)
Field: Materials Science — Metals and Alloys

Advancements in Anode Materials for Cathodic Protection: Nanostructured Alloys, Surface Modifications, and Smart Monitoring This review critically examines advancements in anode materials for cathodic protection systems, focusing on overcoming the limitations of traditional materials like magnesium, zinc, aluminum, graphite, lead-silver alloys, and high-silicon cast iron (HSCI). Conventional anode materials, though widely used, face issues such as rapid degradation, high maintenance costs, and environmental harm. Novel materials, including mixed metal oxides (MMO), advanced aluminum-based alloys, nanostructured materials, and conductive polymers, offer superior electrochemical properties, enhanced durability, and improved performance in aggressive environments like seawater. This review also highlights the role of surface modifications and coatings, such as platinum on titanium and ceramic coatings, in boosting corrosion resistance. Moreover, smart monitoring systems, integrated with IoT and SCADA technologies, are explored for their potential to improve the longevity and efficiency of cathodic protection systems. The paper emphasizes the urgent need for sustainable solutions due to the substantial economic and environmental costs of corrosion, particularly in high-risk industries like oil and gas, maritime, and infrastructure. Future research directions, including the development of hybrid systems combining coatings with CP technologies and the application of advanced alloys and nanostructured materials, are proposed to address the long-term performance and ecological impacts of CP systems. Cathodic protection ; Materials science ; Galvanic anode ; Anode ; Nanotechnology ; Metallurgy ; Chemistry ; Electrode Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jiaqi Wu | Bo Peng
Source: arXiv (Cornell University) (preprint, 2024-09-23)
Field: Materials Science — Materials Chemistry

Smallest [5,6]fullerene as building blocks for 2D networks with superior stability and enhanced photocatalytic performance The assembly of molecules to form covalent networks can create varied lattice structures with distinct physical and chemical properties from conventional atomic lattices. Using the smallest stable [5,6]fullerene units as building blocks, various 2D C$_{24}$ networks can be formed with superior stability and strength compared to the recently synthesised monolayer polymeric C$_{60}$. Monolayer C$_{24}$ harnesses the properties of both carbon crystals and fullerene molecules, such as stable chemical bonds, suitable band gaps and large surface area, facilitating photocatalytic water splitting. The electronic band gaps of C$_{24}$ are comparable to TiO$_2$, providing appropriate band edges with sufficient external potential for overall water splitting over the acidic and neutral pH range. Upon photoexcitation, strong solar absorption enabled by strongly bound bright excitons can generate carriers effectively, while the type-II band alignment between C$_{24}$ and other 2D monolayers can separate electrons and holes in individual layers simultaneously. Additionally, the number of surface active sites of C$_{24}$ monolayers are three times more than that of their C$_{60}$ counterparts in a much wider pH range, providing spontaneous reaction pathways for hydrogen evolution reaction. Our work provides insights into materials design using tunable building blocks of fullerene units with tailored functions for energy generation, conversion and storage. Fullerene ; Photocatalysis ; Stability (learning theory) ; Materials science ; Nanotechnology ; Chemical engineering ; Mathematics ; Computer science ; Chemistry ; Engineering ; Machine learning ; Organic chemistry ; Catalysis 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Graphene research and applications
Authors: Michal Horňáček | Miroslava Bérešová | Pavol Hudec
Source: WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT (article, 2024-12-16)
Field: Chemistry — Inorganic Chemistry

An Environmentally Friendly Catalyst for Aromatic Hydrocarbons Alkylations with 1-alkenes Nowadays, the alkylation of aromatic compounds using 1-alkenes is still conducted in industrial applications using Friedel-Crafts alkylations. The most commonly used catalysts are aluminum chloride (AlCl3) and hydrofluoric acid (HF), both of which pose significant environmental concerns. An alternative approach involves the use of solid acid catalysts, specifically zeolites, which may offer a more environmentally acceptable option. In this study, the alkylation of toluene with 1-decene was performed in a batch reactor under autogenous pressure in the liquid phase at a temperature of 100 °C. Zeolite Y samples with varying sodium content (molar ratio of Si/Al approximately equal to 2.27), were employed as the parent catalysts. These zeolites underwent a dealumination process followed by the removal of residual sodium and the cationic form of aluminum via ion exchange with ammonium nitrate. Accessible physical and chemical methods were used for the characterisation of the prepared catalyst. The residues of sodium were found to influence the catalytic activity in the alkylation reaction. The formation of a secondary mesoporous structure enhanced the selectivity towards the production of 2-decyltoluene. Following the dealumination and ion exchange treatment, an increased ratio of Brønsted to Lewis acid sites was observed, resulting in the suppression of dimerization reactions in the alkylation products. Chemistry ; Alkylation ; Catalysis ; Zeolite ; Inorganic chemistry ; Toluene ; Organic chemistry ; Selectivity ; Ion exchange ; Sodium ; Lewis acids and bases ; Ion Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Hydrodesulfurization Studies