jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Changfa Huang | Xiuru Xu | Yuxuan Chen | Zhengchun Peng
Source: 2022 IEEE 5th International Conference on Electronics Technology (ICET) (article, 2022-05-13)
Field: Materials Science — Biomaterials

Electrospun Titanium Dioxide Nanofibers Reinforced Anti-freezing, Adhesive and Conductive Hydrogels In recent years, multifunctional hydrogels with high electrical conductivity and strong mechanical properties have shown great application prospects in wearable devices, bioelectronics, soft robotics and other fields. It is desirable to achieve multifunctional hydrogels with high electrical conductivity, high mechanical strength, low temperature resistance, and reusability and so on. In this work, we successfully achieved an electrospun titanium dioxide nanofibers reinforced robust, conductive and highly elastic hydrogel via the in-situ polymerization technique. The hydrogen bonds formed between Titanium dioxide nanofibers and the polyvinyl alcohol chains served as sacrificial bonds under external forces, which significantly improved their mechanical and electrical properties. Excellent fracture stress of 2.72 MPa, large fracture strain of 900%, high Young’s modulus of 302.2 kPa, and toughness of 13.37 MJ/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> were demonstrated. Moreover, it also showed good temperature tolerance with working range of -20°C to 20°C, strong self-adhesive strength to various substrates and reusable abilities This approach brings about a new perspective on the exploration of robust, conductive elastomers for artificial intelligence and smart electronic skin applications. Adhesive ; Nanofiber ; Materials science ; Titanium dioxide ; Self-healing hydrogels ; Composite material ; Self adhesive ; Electrical conductor ; Electrospinning ; Polymer chemistry ; Polymer ; Layer (electronics) Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Peddathimula Puneetha | Siva Pratap Reddy Mallem | Ki-Sik Im | Herie Park | Kwi‐Il Park | Jaesool Shim
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Strain-Engineered Piezotronic Effects in Flexible Monolayer Mos2 Continuous Thin Films Monolayer ; Materials science ; Strain (injury) ; Thin film ; Composite material ; Optoelectronics ; Nanotechnology ; Medicine 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Sol Lee | Yangjin Lee | Liping Ding | Kihyun Lee | Feng Ding | Kwanpyo Kim
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

In-situ TEM imaging of Novel Edge Reconstruction in Bilayer Phosphorene Journal Article In-situ TEM imaging of Novel Edge Reconstruction in Bilayer Phosphorene Get access Sol Lee, Sol Lee Department of Physics, Yonsei University, Seoul, KoreaCenter for Nanomedicine, Institute for Basic Science (IBS), Seoul, Korea Search for other works by this author on: Oxford Academic Google Scholar Yangjin Lee, Yangjin Lee Department of Physics, Yonsei University, Seoul, KoreaCenter for Nanomedicine, Institute for Basic Science (IBS), Seoul, Korea Search for other works by this author on: Oxford Academic Google Scholar Li Ping Ding, Li Ping Ding Department of Optoelectronic Science & Technology, School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi'an, ChinaCenter for Multidimensional Carbon Materials, Institute for Basic Science (IBS), Ulsan, Korea Search for other works by this author on: Oxford Academic Google Scholar Kihyun Lee, Kihyun Lee Department of Physics, Yonsei University, Seoul, Korea Search for other works by this author on: Oxford Academic Google Scholar Feng Ding, Feng Ding Center for Multidimensional Carbon Materials, Institute for Basic Science (IBS), Ulsan, KoreaDepartment of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea Search for other works by this author on: Oxford Academic Google Scholar Kwanpyo Kim Kwanpyo Kim Department of Physics, Yonsei University, Seoul, KoreaCenter for Nanomedicine, Institute for Basic Science (IBS), Seoul, Korea Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 2310–2311, https://doi.org/10.1017/S143192762200887X Published: 01 August 2022 Nanomedicine ; Library science ; Phosphorene ; Chinese academy of sciences ; Engineering physics ; Medicine ; Political science ; Nanotechnology ; Engineering ; Computer science ; Materials science ; China ; Nanoparticle 2D Materials and Applications ; Graphene research and applications ; Ga2O3 and related materials
Authors: S. Hirosawa
Source: Journal of the Japan Society of Powder and Powder Metallurgy (article, 2022-04-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Laying out Fundamentals for Production of Nd－Fe－B Permanent Magnet Materials The author received 2019 JSPM Award for Distinguished Achievements in “Development for fundamental research for production of Nd-Fe-B permanent magnet materials.” This paper summarizes the recipient’s works that led to this honor. In the development of isotropic nanocomposite magnets in the Nd－Fe－B－Ti－C system, he developed the concept of the kinetic control of the phases in the rapid solidification process. In addition, the author describes some of the problems that was unsolvable in the early research on the coercive force mechanism of the Nd－Fe－B permanent magnets, and how these problems have been solved as a result of the deepening of the basic science of permanent magnets by many researchers in the “Elements Strategy Research Center for Magnetic Materials,” a project of the Ministry of Education, Culture, Sports, Science and Technology of Japan, of which he has been the directing investigator. These theoretical results are expected to contribute as a basis for future data-driven research to future industry-university collaborative activities. Magnet ; Christian ministry ; Engineering physics ; Mechanical engineering ; Honor ; Isotropy ; Production (economics) ; Process (computing) ; Nanotechnology ; Engineering ; Engineering ethics ; Manufacturing engineering ; Materials science ; Computer science ; Political science ; Physics ; Law ; Economics Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Benedikt Hoock | Santiago Rigamonti | Claudia Draxl
Source: New Journal of Physics (preprint, 2022-11-01)
Field: Materials Science — Materials Chemistry

Advancing descriptor search in materials science: feature engineering and selection strategies Abstract A main goal of data-driven materials research is to find optimal low-dimensional descriptors, allowing us to predict a physical property, and to interpret them in a human-understandable way. In this work, we advance methods to identify descriptors out of a large pool of candidate features by means of compressed sensing. To this extent, we develop schemes for engineering appropriate candidate features that are based on simple basic properties of building blocks that constitute the materials and that are able to represent a multi-component system by scalar numbers. Cross-validation based feature-selection methods are developed for identifying the most relevant features, thereby focusing on high generalizability. We apply our approaches to an ab initio dataset of ternary group-IV compounds to obtain a set of descriptors for predicting lattice constants and energies of mixing. In particular, we introduce simple complexity measures in terms of involved algebraic operations as well as the amount of utilized basic properties. Computer science ; Generalizability theory ; Feature selection ; Scalar (mathematics) ; Ternary operation ; Data mining ; Simple (philosophy) ; Set (abstract data type) ; Lattice (music) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Laurent White
Source: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (article, 2022-05-01)
Field: Materials Science — Materials Chemistry

HCW 2022 Keynote Speaker: Heterogeneous Computing for Scientific Machine Learning More than ever, the semiconductor industry is asked to answer society's call for more computing capacity and capability, which are driven by rapid digitalization, the widespread adoption of artificial intelligence, and the ever-increasing need for high-fidelity scientific simulations. While facing high demand, the supply of computing capability is being technically challenged by the slowdown of Moore's law and the need for high energy efficiency. This tug-of-war has now pushed the industry towards domain-specific accelerators, perhaps likely past the point of no return. The mix of general-purpose CPUs and high-end GPGPUs, which has pervaded data centers over the past few years, is likely to be expanded to a much richer set of application-specific accelerators, including AI engines, reconfigurable hardware, and even perhaps quantum, annealing, and neuromorphic devices. While acceleration and better efficiency may be enabled by using domain-specific accelerators for selected workloads, a much more holistic (i.e., system-wide) approach will have to be adopted to achieve significant performance gains for complex applications that consist of a variety of workloads where each could benefit from a specific accelerator. As an important example, scientific computing, which increasingly incorporates AI training and inference kernels in a tightly-integrated fashion, provides a rich and exciting laboratory for addressing the challenges of efficiently using highly-heterogeneous systems and for ultimately realizing their promises. Those challenges include co-designing the application, which requires domain experts to collaborate with other experts across the stack for workload mapping and data orchestration, and also adopting a decentralized strategy that embeds processing units where the data need them. Finally, the early experience of those co-design efforts should help the industry devise a longer-term strategy for developing programming models that would relieve application experts from what is often perceived as the burden of hardwareaware development and code optimization. Computer science ; Data science ; Neuromorphic engineering ; Domain (mathematical analysis) ; Efficient energy use ; Big data ; Orchestration ; Workload ; Computer architecture ; Distributed computing ; Artificial intelligence ; Operating system ; Artificial neural network Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Data Storage Technologies
Authors: Tomilola M. Obadiya | Daniel M. Sussman
Source: arXiv (Cornell University) (preprint, 2022-11-01)
Field: Materials Science — Materials Chemistry

Does fluid structure encode predictions of glassy dynamics? Data-driven approaches to inferring the local structures responsible for plasticity in amorphous materials have made substantial contributions to our understanding of the failure, flow, and rearrangement dynamics of supercooled fluids. Some of these methods, such as the ``softness'' approach based on linear support vector machines, have identified combinations of local structural features of a supercooled particle's environment that predict energy barriers associated with particle rearrangements. This approach also predicts the onset temperature, often characterized as the temperature below which the system's dynamics becomes non-Arrhenius and above which local structures are no longer predictive of dynamical activity. We implement a transfer-learning approach in which we first show that classifiers can be trained to predict dynamical activity even far above the onset temperature. We then show that applying these classifiers to data from the supercooled phase recovers essentially the same physical information about the relationship between local structures and energy barriers that softness does. Supercooling ; Statistical physics ; ENCODE ; Particle (ecology) ; Dynamics (music) ; Computer science ; Amorphous solid ; Biological system ; Physics ; Thermodynamics ; Chemistry ; Crystallography ; Geology Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Andy Bridger | William I. F. David | Thomas J. Wood | Mohsen Danaie | Keith T. Butler
Source: arXiv (Cornell University) (preprint, 2022-07-27)
Field: Materials Science — Materials Chemistry

Versatile Domain Mapping Of Scanning Electron Nanobeam Diffraction Datasets Utilising Variational AutoEncoders and Decoder-Assisted Latent-Space Clustering Advancements in fast electron detectors have enabled the statistically significant sampling of crystal structures on the nanometre scale by means of Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural similarity across this length scale is key to bridging the gap between local atomic structure (using atomic resolution techniques such as High Resolution Scanning Transmission Electron Microscopy (HR-STEM)) and the macro-scale (using bulk techniques such as powder X-ray and neutron diffraction). The use of SEND technique allows for structural investigation of a broad range of samples, due to the techniques ability to operate with low electron dosage and its tolerance for sample thickness, relative to HR-STEM. This, coupled with the capacity for data collection over a wide areas and the automation of this collection, allows for statistically representative sampling of the microstructure. Also due to these factors, SEND generates large datasets and as a result automated/ semi-automated data processing workflows are required to aid in maximal extraction of useful information. As such, this paper outlines a versatile, data-driven approach for producing domain maps, as well as a statistical approach for assessing their applicability. The production of such domain maps for a dataset can help highlight nuance in the microstructure, as well as improve the manageability of that dataset for further investigation. The workflow outlined utilises a Variational AutoEncoder to identify and learn the sources of variance in the diffraction signal and this, in combination with clustering techniques, is used to produce domain maps for a set of varied example cases. This approach: is agnostic to domain crystallinity; requires no prior knowledge of crystal structure; and does not require the, potentially prohibitive, simulation of a library of appropriate diffraction patterns. Cluster analysis ; Computer science ; Autoencoder ; Materials science ; Data mining ; Pattern recognition (psychology) ; Artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Dexu Zou | Jiaqi Sun | Shan Wang | Qingjun Peng | Weiju Dai | Zhihu Hong
Source: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE) (article, 2022-09-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonlinear Simulation Analysis of Transformer DC Bias Based on Finite Element Method The quasi-DC current and DC current formed in the earth by solar magnetic storm and DC transmission will affect the magnetization characteristics of the transformer core and the normal operation of the power grid. The DC bias phenomenon indicates that the transformer is in an abnormal working state. Due to the nonlinearity of the transformer core material, the DC bias causes the transformer core to enter the saturation region, which in turn produces a series adverse effects such as excitation current distortion, increase in core loss and local overheating. The non-linearity of the iron core material of transformer equipment has an important influence on the transient and steady state electrical quantities under different working conditions. The analysis of magnetization characteristics of the transformer core under the condition of DC bias, the stable operation of the power system, and the design and manufacture of the transformer has important engineering significance. Power equipment design and manufacture and accident tracing are highly dependent on multi-physics simulation. In this paper, finite element technology is used to obtain transient and steady-state electrical quantity waveform data for the nonlinear working condition of transformer DC bias, and build the corresponding nonlinear working condition database. It provides a large, rich and reliable data training and test set for the research of transformer twinning data-driven model, and supports the research of multi-physics fast simulation method of electric power equipment. Transformer ; DC bias ; Energy efficient transformer ; Delta-wye transformer ; Current transformer ; Electrical engineering ; Linear variable differential transformer ; Isolation transformer ; Distribution transformer ; Geomagnetically induced current ; Electronic engineering ; Transformer effect ; Nonlinear system ; Finite element method ; Engineering ; Voltage ; Physics ; Magnetic field ; Geomagnetic storm ; Structural engineering Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Energy Load and Power Forecasting
Authors: Anna Hartmann | Marion Schulz | Timothy A. Su | Toshio Shimizu | Naoki Tanifuji | Hirofumi Yoshikawa | M. V. del Valle | Marta Ximenis | Xabier Lopez de Pariza | Juliana C.N. Chan | Haritz Sardón | Huolin L. Xin | Huan Wang | Weiqi Zhang | Yixi Chen | Q. P. Ji | Guo Zhang | Hui Liu | A. Taylor | Jinping Qu | Eva Illes‐Toth | Oliver J. Hale | Janet Hughes | Nicole Strittmatter | J. Rose | B. E. Clayton | Rebecca Sargeant | S. J. Zhao | Z Wang | Zhihong Lin | Wei Gao | Xiaogang Wen | Shuang Li | Xinyu Yang | Qingmin Zhang | Chao Jing | Yumin Dai | Jinxin Guo | Yinghua He | J Photocatalysis | T Liu | L Hao | M Qian | * Shi | N Wei | M Almenara | X Garralda | J Lopez | * Matxain | M Freixa | ; Huertos | B Zheng | G Ding | C Zhu | * Li | G Lin ; Catalysis | J.-N Fang | L.-C Hu | J.-X Tian | J Liang | * Lin | C Li | * Zhu | Wang | D Biocatalysis | W Gao | J Song | L Wu | C Guo | J Gao | X Liu | L Chen | ; Liu | G Li | H Peng | C Chen | Z Shi | Z Li | ; Jin | Y Smith | Y Chen | M Ma | M Kippax-Jones | W Fan | M Lu | G Frogley | S Cinque | S Day | Y Thompson | L Cheng | A Daemen | M Ramirez-Cuesta | * Schrder | ; Yang | S Hirai | T Takano | T Nakashima | T Iwasa | T Taketsugu
Source: Angewandte Chemie (article, 2022-08-29)
Field: Chemistry — Organic Chemistry

Graphisches Inhaltsverzeichnis: Angew. Chem. 36/2022 Bestimmung von Enantioselektivitten ein. Die Methode basiert auf der Ionenmobilittstrennung von diastereomeren Intermediaten. Citation ; Computer science ; German ; Information retrieval ; Library science ; World Wide Web ; Philosophy ; Linguistics Synthesis and Catalytic Reactions ; Cyclopropane Reaction Mechanisms ; CO2 Reduction Techniques and Catalysts
Authors: Nur Ayu | Naoki Matsui | Guangzhong Jiang | Takashi Hirose | Takashi Itoh | Takeya Mezaki | Yoshitake Toda | Kota Suzuki | Masaaki Hirayama | Takashi Saito | Takashi Kamiyama | Ryoji Kanno
Source: Research Square (preprint, 2022-05-03)
Field: Materials Science — Materials Chemistry

Hydride-ion conductivity and enhanced atmospheric stability of hydride-fluoride SrMgH4–xFx Hydride ; Fluoride ; Conductivity ; Ion ; Electrical resistivity and conductivity ; Materials science ; Inorganic chemistry ; Chemistry ; Metallurgy ; Physical chemistry ; Electrical engineering ; Organic chemistry ; Engineering Hydrogen Storage and Materials ; Inorganic Fluorides and Related Compounds ; High-pressure geophysics and materials
Authors: S. Chetana | Sanjay Upadhyay | Naveen Chandra Joshi | Niraj Kumar | Priyvart Choudhary | Rohit Sharma | Vikas N. Thaku
Source: Research Square (preprint, 2022-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A facile supercritical fluid synthesis of cobalt sulfide integrated with MXene and PANI/PDOT nanocomposites as electrode material for supercapacitor applications Abstract We report the synthesis of ternary CoS/MXene/PANI and CoS/MXene/ PEDOT composites using supercritical fluid (SCF) method for the first time. These fabricated materials along with CoS/MXene was gone through electrochemical investigations using three electrode system. Enhancement in specific capacitance was observed with inclusion of PANI (407 F/g) and further enhanced with PEDOT (630 F/g) at 2 A/g. Therefore, symmetric device using coin cell technique was fabricated by taking CoS/MXene/PEDOT and CoS/MXene/PANI as electrode material. These coin cells were further be examined on the basis of CV, GCD and EIS and it was observed that the specific capacitance was enhance in CoS/MXene/PEDOT (331.1 F/g) over CoS/MXene/PANI (246 F/g) at 2 A/g. For the material, the capacitance retention was calculated to evaluate the charge storage stability and it was observed that the CoS/MXene/PEDOT (97%) is slightly higher stable than CoS/MXene/PANI (96%). PEDOT:PSS ; Supercapacitor ; Materials science ; Cobalt sulfide ; Capacitance ; Nanocomposite ; Electrode ; Supercritical fluid ; Electrochemistry ; Chemical engineering ; Ternary operation ; Nanotechnology ; Chemistry ; Organic chemistry ; Layer (electronics) ; Physical chemistry Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Advanced battery technologies research
Authors: BS Suresh | Dr G VANMATHI
Source:  (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

REVIEW OF THE ORGANIC REDUCTION PROCESSES INVOLVING IRRADIATION PALLADIUM ON CARBON CATALYSTS Palladium-catalyzed hydrogenation is regarded as an essential step in the production of fine chemicals utilized in pharmaceuticals, agricultural as well as in the complete synthesis of challenging natural products. Because of its easy work-up and reaction rate, Pd/C is one of the finest choices for heterogeneous catalysts for process efficiency. Here, we demonstrate that there are large differences in the efficiency of commercial palladium sources on carbon (Pd/C), which results in notable fluctuations in reaction times. The increased dispersion of Pd particles following irradiation has been ascribed to the reduction in response time. The increased dispersion of Pd particles following irradiation has been ascribed to the reduction in response time. Effective hydrogenation catalysts were found to have the following physicochemical properties of homogenous Pd distribution on the carbon substrate; and tiny Pd particle size. There is no appreciable decrease in catalytic activity after five runs of the EB-irradiated catalyst. Nowadays, before spending time and money on pricey synthetic materials, scientists can assess and forecast the efficacy of catalysts. Palladium ; Catalysis ; Reduction (mathematics) ; Carbon fibers ; Irradiation ; Materials science ; Chemistry ; Organic chemistry ; Composite material ; Composite number ; Mathematics ; Physics Asymmetric Hydrogenation and Catalysis ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions