jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fei Xu | Lu Cai | Daniele Salvato | Fidelma Dilemma | Luca Capriotti | Tiankai Yao
Source: Research Square (preprint, 2022-10-18)
Field: Materials Science — Materials Chemistry

Advanced Characterization-Informed Framework and Quantitative Insight to Irradiated Annular U-10Zr Metallic Fuels Abstract U-10Zr-based metallic nuclear fuel is a promising fuel candidate for next-generation sodium-cooled fast reactors. Idaho National Laboratory’s research experience for this type of fuel dates back to the 1960s. Idaho National Laboratory researchers have accumulated a considerable amount of experience and knowledge regarding fuel performance at the engineering scale. The limitation of advanced characterization and lack of proper data analysis tools prevented a mechanistic understanding of fuel microstructure evolution and properties degradation during irradiation. This paper proposed a new workflow, coupled with domain knowledge obtained by advanced post-irradiation examination methods, to provide unprecedented and quantified insights into the fission gas bubbles and pores, and lanthanide distribution in an annular fuel irradiated in the Advanced Test Reactor. In the study, researchers identify and confirm that the Zr-bearing secondary phases exist and generate the quantitative ratios of seven microstructures along the thermal gradient. Moreover, the distributions of fission gas bubbles on two samples of U-10Zr advanced fuels were quantitatively compared. Conclusive findings were obtained and allowed for evaluation of the lanthanide transportation through connected bubbles based on approximately 67,000 fission gas bubbles of the two advanced samples. Nuclear engineering ; Fission ; Characterization (materials science) ; Workflow ; Irradiation ; Research reactor ; Materials science ; Sodium-cooled fast reactor ; Microstructure ; Nuclear fuel ; Domain (mathematical analysis) ; Nuclear physics ; Computer science ; Nanotechnology ; Engineering ; Physics ; Composite material ; Database Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Guang Yang | Haitao Yang | Meng Wang | Yangjie Sun | Miao Wang
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Biomaterials

Application of Nanofiber Material Based on Electrospinning Technology in Sports Rehabilitation of Basketball Player’s Wrist Joint Wrist joint plays an indispensable role in our daily life. The injury of wrist joint has a great influence on the fine movement of the hand and upper limb. For example, you cannot do joint movements, you cannot carry things, and life is very inconvenient. It is necessary to distinguish the types of wrist disorders and take effective rehabilitation methods in time. Wrist joint has a great impact on people’s life and work, and the rehabilitation of wrist joint has become a research hotspot in rehabilitation medicine and medical engineering. Carpal tunnel syndrome is the most common peripheral nerve entrapment disease in clinical practice, with a very high incidence. According to data, the incidence of carpal tunnel syndrome in the general population abroad is 1%‐5%, while that in special populations is as high as 14.5% above. In order to solve the problem of rehabilitation of wrist joint, using electrospinning technology, the polymer solution is prepared into nanofiber materials with strong adsorption and good filterability under the action of high‐voltage electric field. And using some small‐sized nanofibers that are similar to organs in shape and structure, as well as their excellent degradability and biocompatibility, this material is used in the research of wrist joint movement rehabilitation. In this paper, sports rehabilitation training is carried out on the wrist joint of basketball players, and the nanofiber material is effectively combined with sports rehabilitation training. By comparing the rehabilitation of athletes who did not use nanofiber materials, the role of nanofiber materials in sports rehabilitation was studied. This article analyzes the three parts of the radial joint, the wrist joint, and the ulnar joint before and after the sports rehabilitation training for basketball players with functional impairments and compares the sports rehabilitation of the basketball players without nanofiber materials. This article analyzes the three parts of the radial joint, the wrist joint, and the ulnar joint before and after the sports rehabilitation training for basketball players with functional impairments and compares the sports rehabilitation of the basketball players without nanofiber materials. Wrist ; Rehabilitation ; Carpal tunnel syndrome ; Physical medicine and rehabilitation ; Medicine ; Basketball ; Physical therapy ; Population ; Surgery Electrospun Nanofibers in Biomedical Applications ; Stroke Rehabilitation and Recovery
Authors: Jeremy G. Frey | Bonnie Lawlor | Leah McEwen | Christopher K. Ober | Antony Williams
Source: Chemistry International (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

The 2021 IUPAC World Chemistry Leadership Meeting Abstract The 2021 IUPAC World Chemistry Leadership Meeting (WCLM) was more than a year in the making and it was quite a roller coaster ride. Originally planned as a traditional in-person meeting, the pandemic required that the entire IUPAC World Chemistry Congress (WCC) be held virtually. Rather than view this as a negative outcome, the WCLM organizers decided to leverage the situation as an unexpected opportunity to do something unique and innovative that would allow the session to reach a much larger portion of the global scientific community than is the norm at an in-person meeting. The meeting format was designed to stage not one plenary session, but four virtual sessions spread over 24h and covering three different time zones (vide infra). The topic, The Future of Chemistry in the World of Artificial Intelligence, was chosen because artificial intelligence (AI) has been receiving a lot of press— not only in the sciences, but across all industries [1]. In addition, AI was selected in 2020 by a panel of experts as one of IUPAC’s top ten emerging technologies in chemistry—one that will have major positive impacts on how research is conducted and the speed with which science is advanced [2]. Chemical nomenclature ; Plenary session ; Chemistry ; Leverage (statistics) ; Session (web analytics) ; Panel discussion ; Library science ; Operations research ; Computer science ; Engineering ; Business ; Artificial intelligence ; World Wide Web ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yujie Qian | Jiang Guo | Zhengkai Tu | Li, Zhening | Connor W. Coley | Regina Barzilay
Source: arXiv (Cornell University) (preprint, 2022-05-28)
Field: Materials Science — Materials Chemistry

MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation Molecular structure recognition is the task of translating a molecular image into its graph structure. Significant variation in drawing styles and conventions exhibited in chemical literature poses a significant challenge for automating this task. In this paper, we propose MolScribe, a novel image-to-graph generation model that explicitly predicts atoms and bonds, along with their geometric layouts, to construct the molecular structure. Our model flexibly incorporates symbolic chemistry constraints to recognize chirality and expand abbreviated structures. We further develop data augmentation strategies to enhance the model robustness against domain shifts. In experiments on both synthetic and realistic molecular images, MolScribe significantly outperforms previous models, achieving 76-93% accuracy on public benchmarks. Chemists can also easily verify MolScribe's prediction, informed by its confidence estimation and atom-level alignment with the input image. MolScribe is publicly available through Python and web interfaces: https://github.com/thomas0809/MolScribe. Artificial intelligence ; Computer science ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Enzyme Structure and Function
Authors: Attila Hunyadi | Orinamhe Godwin Agbadua | Gergely Takács | György Tibor Balogh
Source: ChemRxiv (preprint, 2022-07-10)
Field: Chemistry — Organic Chemistry

Scavengome of an Antioxidant The term ‘scavengome’ refers to the chemical space of all the metabolites that may be formed from an antioxidant upon scavenging reactive oxygen or nitrogen species (ROS/RNS). This chemical space is very rich in structures representing an increased chemical complexity as compared to the parent antioxidant: a wide range of unusual heterocyclic structures, new C-C bonds, etc. may be formed. Further, in a biological environment, this increased chemical complexity is directly translated from the localized conditions of oxidative stress that determines the amounts and types of ROS/RNS present. Biomimetic oxidative chemistry provides an excellent tool to model chemical reactions between antioxidants and ROS/RNS. In this chapter, we provide an overview on the known metabolites obtained by biomimetic oxidation of a few selected natural antioxidants, i.e., a stilbene (resveratrol), a pair of hydroxycinnamates (caffeic acid and methyl caffeate), and a flavonol (quercetin), and discuss the drug discovery perspectives of the related chemical space. Antioxidant ; Chemistry ; Chemical space ; Resveratrol ; Reactive oxygen species ; Oxidative stress ; Quercetin ; Caffeic acid ; Chemical structure ; Biochemistry ; Organic chemistry ; Drug discovery ; Combinatorial chemistry Free Radicals and Antioxidants ; Bioactive Compounds and Antitumor Agents ; Radical Photochemical Reactions
Authors: Anupam Anand Ojha | Saumya Thakur | Surl-Hee Ahn | Rommie E. Amaro
Source: ChemRxiv (preprint, 2022-03-22)
Field: Materials Science — Materials Chemistry

DeepWEST: Deep learning of kinetic models with the Weighted Ensemble Simulation Toolkit for enhanced kinetic and thermodynamic sampling Recent advances in computational power and algorithms have made molecular dynamics (MD) simulations reach greater timescales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this challenge, including the weighted ensemble (WE) method, which samples transition between metastable states using many weighted trajectories to estimate kinetic rate constants. However, initial sampling of the potential energy surface has a significant impact on the performance of WE, i.e., convergence and efficiency. We thereby introduce deep-learned kinetic modeling approaches that extract statistically relevant information from short MD trajectories to provide a well-sampled initial state distribution for WE simulation. This hybrid approach overcomes any statistical bias to the system as it runs short unbiased MD trajectories and identifies meaningful metastable states of the system. It is shown to provide a more refined free energy landscape closer to steady-state that could efficiently sample kinetic properties such as rate constants. Metastability ; Kinetic energy ; Statistical physics ; Energy landscape ; Molecular dynamics ; Sampling (signal processing) ; Computer science ; Convergence (economics) ; Algorithm ; Physics ; Thermodynamics ; Chemistry ; Computational chemistry ; Classical mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Linbo Tian | Jinbao Xia | A.A. Kolomenskii | H. A. Schuessler | Feng Zhu | Yanfeng Li | Jingliang He | Qian Dong | Sasa Zhang
Source: Research Square (preprint, 2022-07-18)
Field: Chemistry — Spectroscopy

Neural network multi-component gas mixture analysis with broadband dual-frequency comb absorption spectroscopy Broadband ; Spectroscopy ; Component (thermodynamics) ; Absorption (acoustics) ; Dual (grammatical number) ; Absorption spectroscopy ; Materials science ; Artificial neural network ; Telecommunications ; Optics ; Physics ; Computer science ; Artificial intelligence ; Composite material Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Anupam Anand Ojha | Saumya Thakur | Surl-Hee Ahn | Rommie E. Amaro
Source: ChemRxiv (preprint, 2022-07-31)
Field: Materials Science — Materials Chemistry

DeepWEST: Deep learning of kinetic models with the Weighted Ensemble Simulation Toolkit for enhanced sampling Recent advances in computational power and algorithms have made molecular dynamics (MD) simulations reach greater timescales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this challenge, including the weighted ensemble (WE) method, which samples transition between metastable states using many weighted trajectories to estimate kinetic rate constants. However, initial sampling of the potential energy surface has a significant impact on the performance of WE, i.e., convergence and efficiency. We thereby introduce deep-learned kinetic modeling approaches that extract statistically relevant information from short MD trajectories to provide a well-sampled initial state distribution for WE simulation. This hybrid approach overcomes any statistical bias to the system as it runs short unbiased MD trajectories and identifies meaningful metastable states of the system. It is shown to provide a more refined free energy landscape closer to steady-state that could efficiently sample kinetic properties such as rate constants. Metastability ; Kinetic energy ; Energy landscape ; Molecular dynamics ; Statistical physics ; Sampling (signal processing) ; Computer science ; Convergence (economics) ; Algorithm ; Physics ; Chemistry ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Robert Hein | Paul D. Beer
Source: ChemRxiv (preprint, 2022-03-28)
Field: Chemistry — Spectroscopy

Halogen Bonding and Chalcogen Bonding Mediated Sensing Sigma-hole interactions, in particular halogen bonding (XB) and chalcogen bonding (ChB), have become indispensible tools in supramolecular chemistry, with wide-ranging applications in crystal engineering, catalysis and materials chemistry as well as anion recognition, transport and sensing. The latter has very rapidly developed in recent years and is becoming a mature research area in its own right. This can be attributed to the numerous advantages sigma-hole interactions imbue in sensor design, in particular high degrees of selectivity, sensitivity and the capability for sensing in aqueous media. Herein, we provide the first detailed overview of all developments in the field of XB and ChB mediated sensing, in particular the detection of anions but also neutral (gaseous) Lewis bases. This includes a wide range of optical colorimetric and luminescent sensors as well as an array of electrochemical sensors, most notably redox-active host systems. In addition, we discuss a range of other sensor designs, including capacitive sensors and chemiresistors, and provide a detailed overview and outlook for future fundamental developments in the field. Exemplified herein for the XB and ChB mediated sensing of anions, importantly the sensing concepts and methodologies described are generically applicable for the development of supramolecular receptors and sensors in general, including those for cations and neutral molecules employing a wide array of non-covalent interactions. As such we believe this review to be a useful guide to both the supramolecular and general chemistry community with interests in the fields of host-guest recognition and small molecule sensing. Moreover, we also highlight the need for a broader integration of supramolecular chemistry, analytical chemistry, synthetic chemistry and materials science in the development of the next generation of potent sensors. Supramolecular chemistry ; Chalcogen ; Halogen bond ; Nanotechnology ; Host–guest chemistry ; Chemistry ; Molecule ; Crystal engineering ; Covalent bond ; Materials science ; Organic chemistry Molecular Sensors and Ion Detection ; Crystallography and molecular interactions ; Organic and Molecular Conductors Research
Authors: Liang-Yin Huang | Hwa‐Dong Liu | Shiue‐Der Lu | Chih-Ming Hsu
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Novel Graphene Allocating Carbon–Copper Ratio Method for the Rail Vehicle Propulsion System Ground Carbon Brush This study proposed graphene allocating carbon−copper ratio (GACCR) for the grounding carbon brush of rail vehicle propulsion systems. The proposed GACCR is the key component of grounding apparatuses, and the system effectiveness can be enhanced by increasing the electro-conductive efficiency of the grounding carbon brush. The proposed GACCR applies physical vapor deposition, which uses a sputtering system to control the vacuum and temperature, thereby adjusting the copper to carbon ratio, and fabricate optimized graphene. This study employed an energy dispersive spectrometer to observe the graphite flake corrugated stack architecture on the surface of grounding carbon brush, and analyze the composition. A four−point probe was used to measure the impedance characteristic. The characteristics of graphite film were detected using a Raman spectrometer. The G peak of 1580 cm-1 and the 2D peak at 2750 cm-1 formed the two−dimensional structure of graphite film. Finally, this study took the underground rail vehicle as an example. The grounding carbon brush with the proposed GACCR was compared with traditional graphite grounding carbon brush to assess the economic benefit of periodically changing the grounding carbon brush of rail vehicles every three years. The proposed GACCR can save 112000 USD (35% compared to the original replacement grounding carbon brush cost) for Taiwan Metro, proving its significant cost−benefit effect. Brush ; Graphite ; Carbon fibers ; Ground ; Graphene ; Materials science ; Electrical conductor ; Electromagnetic shielding ; Composite material ; Computer science ; Electrical engineering ; Nanotechnology ; Engineering ; Composite number Graphene research and applications ; Advancements in Battery Materials ; Electrical Contact Performance and Analysis
Authors: Andraž Kravos | Tomaž Katrašnik
Source: Catalysts (article, 2022-01-04)
Field: Materials Science — Materials Chemistry

Closed-Form Formulation of the Thermodynamically Consistent Electrochemical Model Considering Electrochemical Co-Oxidation of CO and H2 for Simulating Solid Oxide Fuel Cells Achieving efficient solid oxide fuel cell operation and simultaneous prevention of degradation effects calls for the development of precise on-line monitoring and control tools based on predictive, computationally fast models. The originality of the proposed modelling approach originates from the hypothesis that the innovative derivation procedure enables the development of a thermodynamically consistent multi-species electrochemical model that considers the electrochemical co-oxidation of carbon monoxide and hydrogen in a closed-form. The latter is achieved by coupling the equations for anodic reaction rates with the equation for anodic potential. Furthermore, the newly derived model is capable of accommodating the diffusive transport of gaseous species through the gas diffusion layer, yielding a computationally efficient quasi-one-dimensional model. This resolves a persistent knowledge gap, as the proposed modelling approach enables the modelling of multi-species fuels in a closed form, resulting in very high computational efficiency, and thus enable the model’s real-time capability. Multiple validation steps against polarisation curves with different fuel mixtures confirm the capability of the newly developed model to replicate experimental data. Furthermore, the presented results confirm the capability of the model to accurately simulate outside the calibrated variation space under different operating conditions and reformate mixtures. These functionalities position the proposed model as a beyond state-of-the-art tool for model supported development and control applications. Electrochemistry ; Computer science ; Biological system ; Carbon monoxide ; Oxide ; Materials science ; Chemistry ; Electrode ; Catalysis ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Chemical Looping and Thermochemical Processes
Authors: Sathya Narayanasamy | Patrick Jollivet | Christophe Jégou | Mélanie Moskura | Abdesselam Abdelouas | Thibault Charpentier | Frédéric Angeli
Source: npj Materials Degradation (article, 2022-11-03)
Field: Materials Science — Ceramics and Composites

Borosilicate glass alteration in vapor phase and aqueous medium Abstract A Na-alumino-borosilicate glass and its Ca-doped counterpart were altered in vapor phase (98% relative humidity) and aqueous medium at 90 °C. Both the alteration media were enriched in 17 O. Characterization of the altered samples pointed out some differences between glass alteration in aqueous medium and vapor phase in terms of the alteration kinetics, the effect of Ca-doping on glass chemical durability, the behavior of elements in the gel layer, and the structure of the gel layer. Some of the key results are the recondensation of boron in the gel layer formed in the vapor phase and the utility of Rotation Echo DOuble Resonance Nuclear Magnetic Resonance spectroscopy to qualitatively distinguish between signals from the pristine glass and hydrated gel layer within a sample that was not altered to the core. The results gave rise to inferences about glass alteration mechanisms in both the alteration media and the differences between them. Borosilicate glass ; Phase (matter) ; Materials science ; Layer (electronics) ; Aqueous solution ; Porous glass ; Boron ; Chemical engineering ; Analytical Chemistry (journal) ; Aqueous medium ; Mineralogy ; Chemistry ; Chromatography ; Composite material ; Physical chemistry ; Organic chemistry ; Porosity Glass properties and applications ; Luminescence Properties of Advanced Materials ; Recycling and utilization of industrial and municipal waste in materials production