jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianbo Han
Source: Academic Journal of Materials & Chemistry (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

A study of the composition of glass products based on multiple linear regression Ancient glass is highly susceptible to weathering by the burial environment, and during the weathering process, its composition ratios change. In order to grasp the pattern of compositional changes in the weathering of ancient glass products, this paper will analyse data on the characteristics of different glass products and the proportions of their chemical composition, discuss the relationships between the factors and promote the development of China's glass manufacturing industry. In this study, for the question of the relationship between weathering and type, decoration and color on the surface of glass, a chi-square test was used to calculate significant p-values of 0.0201, 0.0565 and 0.5066 in that order, and the following conclusions were obtained: there is a significant difference between weathering and type on the surface of glass products, and there is no significant difference with decoration and color; for the statistical pattern of the content of chemical components on the surface of cultural relic samples with or without weathering For the problem of predicting the content of chemical components before weathering, the data were divided into four categories based on the presence or absence of weathering and the type of glass, with silica content as the dependent variable and the rest of the chemical components as independent variables, and a multiple linear regression model was used to find the statistical law. The difference between the mean values of high potassium glass and lead-barium glass before and after weathering was used to predict the pre-weathering chemical content of the weathering points. Weathering ; Chemical composition ; Linear regression ; Regression analysis ; Mineralogy ; Materials science ; Mathematics ; Environmental science ; Chemistry ; Geology ; Geochemistry ; Statistics Pigment Synthesis and Properties
Authors: Jean‐Philippe Julien | Iga Kucharska | Danton Ivanochko | Sophia Hailemariam | Maartje R. Inklaar | Hee Ryung Kim | Karina Teelen | Rianne Stoter | Marga van de Vegte‐Bolmer | Geert‐Jan van Gemert | Anthony Semesi | Brandon McLeod | Ahyoung Ki | Won‐Kyu Lee | John L. Rubinstein | Matthijs M. Jore
Source: Research Square (preprint, 2023-11-30)
Field: Materials Science — Materials Chemistry

Structural elucidation of full-length Pfs48/45 in complex with potent mAbs isolated from a naturally exposed individual Biology ; Computational biology ; Stereochemistry ; Genetics ; Chemistry Enzyme Structure and Function ; Molecular Biology Techniques and Applications ; Bacterial Genetics and Biotechnology
Authors: Austin A. Peach | Carl H. Fleischer | Kirill Levin | Jazmine E. Sanchez | Sean T. Holmes | Robert W. Schurko
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-07-07)
Field: Chemistry — Spectroscopy

Crystal structure prediction with quadrupolar NMR crystallography (QNMRX-CSP) Crystal structure prediction (CSP) is a growing set of methodologies for the prediction of crystal structures from first principles.1,2 CSP methods can be assisted by key experimental data; as such, NMR crystallography (NMRX), which uses data from solid-state NMR (ssNMR) spectroscopy, powder X-ray diffraction (pXRD), and quantum chemical calculations of NMR parameters and static lattice energies, is emerging as an important technique for the prediction, refinement, and validation of crystal structures.Most Crystallography ; Materials science ; Crystal structure ; Crystal structure prediction ; Chemistry Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Molecular spectroscopy and chirality
Authors: Halil Ibrahim Kemaneci | Oguz Ozan YOLCAN | Ramazan Köse
Source: Scientia Iranica (article, 2023-05-22)
Field: Materials Science — Polymers and Plastics

Thermal Comfort and Energy Analysis in Ceramic Tile In this study, the thermal touch comfort of ceramic tiles was investigated. For this purpose, energy analysis was made for the heat transfer during touch and the surface temperature during touch was calculated. It is explained how the thermal inertia phenomenon contributes to the calculation of the surface temperature. It has been revealed why ceramic tiles feel cold when touched. In order to examine the tactile comfort, the structure of the human skin and the mechanism of feeling the temperature are shown. In addition, for other coating materials at the same temperature as the ceramic tile, the surface temperatures during touch were calculated and compared. Previous studies to increase thermal comfort in ceramic tiles are discussed. It was calculated how much the thermal comfort increased as a result of coating the ceramic tile with a polymer material. The applicability of the coating option and how close it is to the goal of increasing thermal comfort are discussed. Thus, by calculating how much the coated ceramic tile improves the thermal comfort, a basic study has been put forward to evaluate the thermal comfort improvements to be made on many surfaces used as coating elements in buildings and touched by people. Tile ; Thermal comfort ; Ceramic ; Coating ; Materials science ; Thermal ; Composite material ; Thermal inertia ; Heat transfer ; Mechanics ; Thermodynamics ; Physics Textile materials and evaluations
Source: Angewandte Chemie International Edition (article, 2023-05-16)
Field: Chemistry — Spectroscopy

CeMOS Mannheim Connects High‐Impact Science, Co‐development, and Transfer through Collaborations This invited Team Profile was created by the Applied Mass Spectrometry Team at the Center for Mass Spectrometry and Optical Spectroscopy (CeMOS Mannheim) in Germany. They recently published an article together with Sirius Fine Chemicals SiChem GmbH and Bruker Daltonics. The work presents a novel concept for vacuum-stable-by-design MALDI matrices that enable long MALDI mass spectrometry measurements (e.g., for imaging) of at least 72 hours. Through the use of a photo-removable group, organic synthesis transformed the widely used, but unfortunately very volatile MALDI matrix 2,5-dihydroxyacetophenone (2,5-DHAP) into a vacuum-stable one. The protecting group can be uncaged by the MALDI laser in the ion source and then the matrix performs in the same way as the common matrix 2,5-DHAP. "A Caged In-Source Laser-Cleavable MALDI Matrix with High Vacuum Stability for Extended MALDI-MS Imaging", Q. Zhou, S. Rizzo, J. Oetjen, A. Fülöp, M. Rittner, H. Gillandt, C. Hopf, Angew. Chem. Int. Ed. 2023, e202217047. Mass spectrometry ; Accelerator mass spectrometry ; Matrix (chemical analysis) ; Chemistry ; Analytical Chemistry (journal) ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: R. Kužel
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Twenty fifth General Assembly and Congress of the International Union of Crystallography – anniversary congress in hybrid form with comprehensive statistics General assembly ; Political science ; Library science ; Law ; Computer science X-ray Diffraction in Crystallography ; History and advancements in chemistry ; Radioactive element chemistry and processing
Authors: Hidefumi Wakamatsu | Hikaru Nara | Yoshiharu Iwata
Source: Transactions of the Institute of Systems Control and Information Engineers (article, 2023-03-15)
Field: Materials Science — Polymers and Plastics

Shape Prediction of Knitted Stitches Using Machine Learning Toward Wearing Simulation of Knitted Clothes Recently, many wearing simulation systems, which do not need samples of clothes or actual trying on them, have been developed to improve the productivity of clothes. However, in the case of knitted clothes, conventional systems only offer a looking not based on mechanical consideration at the stitch level because such consideration leads to a significant increase in computational cost. In this paper, we propose a shape prediction method for knitted stitches using machine learning. First, a yarn is modeled as a structure with straight springs, rotating springs, and torsion springs. By minimizing the potential energy of yarns, the stable shape of a stitch can be derived. Next, using such shapes as training data, machine learning was performed with nonlinear neural networks. Then, various shapes of the stitch can be predicted without time-consuming optimization. Our proposed method will be useful for precise wearing simulation of knitted clothes. Yarn ; Clothing ; Nonlinear system ; Torsion (gastropod) ; Artificial neural network ; Engineering ; Computer science ; Artificial intelligence ; Structural engineering ; Engineering drawing ; Mechanical engineering Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Computer Graphics and Visualization Techniques
Source: NanoWorld Journal (article, 2023-11-03)
Field: Materials Science — Polymers and Plastics

Impact Strength Analysis of Novel Kenaf Fiber Epoxy Composite Laminate with and without the Reinforcement of Stainless-steel Wire Mesh Devarala Srinivasulu and Ramya Devi Impact strength analysis of novel Kenaf fiber (KF) epoxy composite laminate with and without the reinforcement of stainless-steel wire mesh. Composite without stainless reinforcement is made using 5 plies of bidirectional composite laminate fabric, and composite laminate with reinforcement made using bidirectional fiber fabric and stainless-steel wire mesh stacked in alternate sequence. Kenaf ; Materials science ; Epoxy ; Composite material ; Reinforcement ; Composite number ; Wire mesh ; Fiber Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; High-Velocity Impact and Material Behavior
Authors: Marcin Hinz | Jannis Pietruschka | Stefan Bracke
Source:  (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Optical Surface Analysis with Support Vector Machines based on Two Different Measurement Techniques The surface topography as well as the optical perception are important features for evaluating the quality of fine grinded knives.Parameters as the surface roughness, gloss or coloring are used for the quantification of these features.The measuring is implemented by the use of traditional methods, which are manual, time-consuming and cost-intensive.On top of that, the application of these methods for the condition monitoring of the ongoing process is rather limited.Therefore, a new, faster and more cost-effective approach is needed to improve the classical measurement methods.A conceivable approach could be based on image analysis.Over the past years, different contactless image analysis based approaches have been developed to simplify the traditional roughness measurement methods.Some studies propose picture pre-processing and feature extraction in combination with machine learning algorithms.The overall goal of the presented research activities is the development of a condition monitoring tool which can be implemented in the ongoing grinding process of the knives.It should be used to ensure the knives quality and to reduce rejects by an immediate detection of deviations of the target values and the possibility to adapt the production process accordingly.For this reason, a data set based on cutlery samples has been generated and analyzed.The extraction of features of the data set is presented for a better understanding of the training process.The features are used to train various machine learning algorithms with and without a combination of logged process parameters to evaluate the surface roughness.Within this study the image of each grinded surface is analyzed regarding its measured arithmetic average roughness value (Ra) by the use of Support Vector Machine (SVM) and Support Vector Regressor (SVR) algorithms. Computer science ; Gloss (optics) ; Surface roughness ; Feature extraction ; Process (computing) ; Artificial intelligence ; Support vector machine ; Machine learning ; Surface finish ; Image processing ; Set (abstract data type) ; Computer vision ; Data mining ; Image (mathematics) ; Mechanical engineering ; Engineering ; Materials science Textile materials and evaluations ; Advanced machining processes and optimization ; Surface Roughness and Optical Measurements
Authors: Huijun Zhang | Feng Liu | Yilong Han
Source: arXiv (Cornell University) (preprint, 2023-05-07)
Field: Materials Science — Materials Chemistry

Anisotropic-Isotropic Transition of Cages at the Glass Transition Characterizing the local structural evolution is an essential step in understanding the nature of glass transition. In this work, we probe the evolution of Voronoi cell geometry in simple glass models, and find that the individual particle cages deform anisotropically in supercooled liquid and isotropically in glass. We introduce an anisotropy parameter $k$ for each Voronoi cell, which mean value exhibits a sharp change at the mode-coupling glass transition $ϕ_\mathrm{c}$. Moreover, a power law of packing fraction $ϕ\propto q_1^{-d}$ is discovered in the liquid regime with $d&gt;D$, in contrast to $d=D$ in the glass regime, where $q_1$ is the first peak position of structure factor, and $D$ is the space dimension. This power law is explained by the change of $k$. The active motions in supercooled liquid are spatially correlated with long axes rather than short axes of Voronoi cells. In addition, the dynamic slowing down approaching the glass transition can be well characterized through a modified free volume model based on $k$. These findings reveal that the nonagnostic structural parameter $k$ determines glassy dynamics and is effective in identifying the structure-dynamics correlations and the glass transition. Supercooling ; Glass transition ; Voronoi diagram ; Anisotropy ; Isotropy ; Power law ; Condensed matter physics ; Work (physics) ; Dimension (graph theory) ; Materials science ; Atomic packing factor ; Coupling (piping) ; Position (finance) ; Physics ; Geometry ; Optics ; Thermodynamics ; Mathematics ; Nuclear magnetic resonance ; Combinatorics Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Theoretical and Computational Physics
Authors: Yang Bing | Di Cui | Meng Tong | Huaming Guo | Guoxi Lian
Source: Research Square (preprint, 2023-06-13)
Field: Chemistry — Inorganic Chemistry

Characteristics and influencing factors of uranium adsorption by sandstones outside an acid in-situ leaching uranium mining area Abstract Uranium (U) contamination in groundwater of the uranium mines exploited using the in-situ leaching (ISL) technology (also referred to as the ISL uranium mines) has attracted wide international attention. Since the adsorption of U in groundwater by aquifer media influences the migration scope of U, understanding the U adsorption by aquifer media is of great significance for accurately predicting the influencing scope of ISL uranium mines on groundwater. This study collected sandstone samples from the ore-bearing aquifer outside a typical mining area in a uranium mine exploited using the acid ISL (AISL) technology (also referred to as an AISL uranium mine) in northern China. Using these sandstone samples, this study conducted batch adsorption experiments and column experiments of U to reveal the characteristics and major influencing factors of U adsorption under different conditions. The results show that the adsorption of U by sandstones in the ore-bearing aquifer outside a mining area satisfies the pseudo-second-order kinetic equation and the linear isothermal adsorption model. In the batch U adsorption experiments, the adsorption efficiency and capacity for U reached maxima of 77.45% and 0.069 mg/g at pH 7, respectively. As shown by column experiments, the sandstones showed a significant increase in the U adsorption and retardation as pH increased. The SO 4 2− concentration has minor effects on U adsorption under experimental conditions. Sandstones from different monitoring wells exhibited different U adsorption characteristics. Sandstone cores with relatively high contents of clay minerals such as illite showed strong U adsorption. Under the same experimental conditions, the U distribution coefficient K d (6.91×10 − 2 L/g) obtained from column experiments is much less than that (0.14 L/g) obtained from the batch U adsorption experiments. These results suggest that it is necessary to take into account the U adsorption and retardation by aquifer media in predicting the impacts of ISL uranium mines on the groundwater environment. Uranium ; Adsorption ; Groundwater ; Aquifer ; Leaching (pedology) ; Uranium ore ; Clay minerals ; Geology ; Groundwater resources ; Environmental chemistry ; Mining engineering ; Environmental science ; Geochemistry ; Chemistry ; Mineralogy ; Soil science ; Materials science ; Geotechnical engineering ; Metallurgy Radioactive element chemistry and processing ; Groundwater flow and contamination studies ; Radioactivity and Radon Measurements
Authors: B. Varona-Uriarte | Carlos Munuera-Javaloy | E. Terradillos | Ban, Y. | Aitor Álvarez-Gila | Estíbaliz Garrote | J. Casanova
Source: arXiv (Cornell University) (preprint, 2023-11-25)
Field: Materials Science — Materials Chemistry

Automatic Detection of Nuclear Spins at Arbitrary Magnetic Fields via Signal-to-Image AI Model Quantum sensors leverage matter's quantum properties to enable measurements with unprecedented spatial and spectral resolution. Among these sensors, those utilizing nitrogen-vacancy (NV) centers in diamond offer the distinct advantage of operating at room temperature. Nevertheless, signals received from NV centers are often complex, making interpretation challenging. This is especially relevant in low magnetic field scenarios, where standard approximations for modeling the system fail. Additionally, NV signals feature a prominent noise component. In this Letter, we present a signal-to-image deep learning model capable of automatically inferring the number of nuclear spins surrounding a NV sensor and the hyperfine couplings between the sensor and the nuclear spins. Our model is trained to operate effectively across various magnetic field scenarios, requires no prior knowledge of the involved nuclei, and is designed to handle noisy signals, leading to fast characterization of nuclear environments in real experimental conditions. With detailed numerical simulations, we test the performance of our model in scenarios involving varying numbers of nuclei, achieving an average error of less than $2\ \rm{kHz}$ in the estimated hyperfine constants. Spins ; Leverage (statistics) ; Hyperfine structure ; Physics ; Computational physics ; Magnetic field ; Computer science ; Artificial intelligence ; Quantum mechanics ; Condensed matter physics Diamond and Carbon-based Materials Research ; Nuclear Physics and Applications ; Atomic and Subatomic Physics Research
Authors: Alaa Zreik
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2023-01-16)
Field: Materials Science — Materials Chemistry

Semantic trajectory analysis for the prediction of the physical state of the collections at the BnF The mission of the National Library of France (BnF) is to collect, preserve, enrich and make available the national documentary heritage. Its collections comprise nearly forty million documents.One of the BnF's missions is to maintain the documents of its collections in good condition in order to ensure their availability to readers.The definition of a conservation/restoration policy by the experts requires the identification of the documents in poor condition; to this end, the physical state of the documents must be monitored regularly to identify those requiring urgent interventions. But this time-consuming task is impossible in practice due to the large volume of documents.The objective of our work is to provide a support to the experts in the definition of their conservation/restoration policies and to provide a decision support system allowing the characterization of the physical state of documents by the integration and analysis of the data available in the databases of the various departments of the BnF.Considering that each document is described by a conservation/restoration history, which includes all the information likely to have an impact on its physical state, the main questions we are faced with are, on the one hand, the representation of these histories and their comparison taking into account their terminological heterogeneity, and on the other hand, the definition of an analysis process of these histories enabling to characterize the state of the documents and to predict it.Our work aims to propose some contributions towards a decision support system for conservation/restoration experts at the BnF. We have proposed a representation of conservation/restoration histories as semantic trajectories, and we have proposed appropriate similarity measures to resolve the terminological heterogeneity of the data using an external knowledge base developed in collaboration with experts. We also have defined an analysis process based on a clustering algorithm to predict the documents' physical state. Finally, we have proposed a novel concept weighting approach that allows to define the importance of the concepts considering a specific analysis task. Trajectory ; Computer science ; State (computer science) ; Information retrieval ; Data science ; Data mining ; Natural language processing ; Artificial intelligence ; Programming language ; Physics ; Astronomy Machine Learning in Materials Science
Authors: Simanta Lahkar | Raghavan Ranganathan
Source: Research Square (preprint, 2023-08-24)
Field: Materials Science — Materials Chemistry

Competing mechanisms govern the thermal rectification behavior in semi-stochastic polycrystalline graphene with graded grain-density distribution Rectification ; Crystallite ; Graphene ; Materials science ; Thermal ; Distribution (mathematics) ; Composite material ; Nanotechnology ; Mathematics ; Physics ; Mathematical analysis ; Thermodynamics ; Metallurgy Graphene research and applications ; Thermal properties of materials ; Carbon Nanotubes in Composites
Authors: Gözde Katırcıoğlu | Emel Kızılkaya Aydoğan | Esra Akgül | Yılmaz Delice
Source: GAZI UNIVERSITY JOURNAL OF SCIENCE (article, 2023-08-14)
Field: Materials Science — Polymers and Plastics

Developed ABCLASS-Miner Classification Algorithm Based Rule Extraction for Denim Fabrics Obtaining and storing large amounts of data have become easier with the rapidly developing information technologies (IT). However, the data generated and collected, which are irrelevant in and of themselves, become useful only when they are analyzed for a specific reason. Data mining may transform raw data into useful information. In the present study, classification and analysis of denim fabric quality characteristics according to denim fabric production parameters were carried out. The present study proposes a new classification rule inference algorithm. The suggested approach is mostly based on Artificial Bee Colony Optimization (ABC), a swarm intelligence meta-heuristic. In each step of the algorithm, there are two phases called the employed bee phase and the onlooker bee phase. This algorithm has been compared with the classification algorithms in the related literature. This proposed algorithm is a new data mining tool that intelligently combines various metaheuristic and neural networks and can generate classification rules. The results indicate that the proposed data mining algorithms may be highly useful in determining weight and width in denim fabric manufacture. Denim ; Computer science ; Data mining ; Heuristic ; Algorithm ; Raw data ; Swarm intelligence ; Artificial neural network ; Artificial intelligence ; Machine learning ; Particle swarm optimization Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Spectroscopy and Chemometric Analyses