jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Timothy Fasham | Jean‐Pierre Attal | Hélène Gouze | Sarah Abdel‐Gawad | Elisabeth Dursun | Philippe FRANÇOIS
Source: Dental Materials Journal (article, 2024-10-16)
Field: Chemistry — Organic Chemistry

Effects of CAD/CAM restorative materials and thickness on the depth of cure of various light-cured resin composite materials The study assessed the depth of cure (DOC) of different resin composites light-cured through different types of CAD/CAM materials of variable thickness. Three CAD/CAM materials (multilayer zirconia, lithium disilicate glass ceramic, composite) of three thicknesses (2-, 3-, and 4-mm) were prepared and their translucency parameter (TP) were measured. A light-curable and a dual-curable resin cement (Variolink Esthetic LC and DC) and a bulk-fill resin composite with a higher Ivocerin concentration (Tetric PowerFill) were used to mimic luting agents. DOC was assessed via Vickers microhardness testing. Increased thickness of CAD/CAM specimen was associated with decreased DOC. The chemical composition of the resin composites affected their curing performance depending on the light curing mode. Tetric PowerFill presented the greater DOC among the other resin composites. Receiver operating characteristic models of curing status appeared to provide better insight in predicting the DOC of luting agent according to TP than linear regression. Materials science ; Composite number ; Composite material ; CAD ; Resin composite ; Engineering drawing Photopolymerization techniques and applications ; Dental materials and restorations ; 3D Surveying and Cultural Heritage
Authors: Na Deng | Jiuan Zhang
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

HMFM: A Method for Identifying High-Value Patents by Fusing Multiple Features Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitalit... | Find, read and cite all the research you need on Tech Science Press Value (mathematics) ; Computer science ; Artificial intelligence ; Machine learning Metallurgy and Material Science
Authors: Chao Fang | Zheng Xiao-gang | Jue Liu | Han Du | George J. Weng
Source: Journal of Physics D Applied Physics (article, 2024-08-28)
Field: Materials Science — Materials Chemistry

Uncovering the thermal conductivity of graphene nanoplatelet composites with interlayers using a Monte Carlo model Abstract This paper uses a Monte Carlo method to study the thermal conductivity of graphene nanoplatelet (GNP) composites. Firstly, a large number of GNPs are randomly set in a representative volume element. Then, based on a temperature satisfying the Laplace equation in a matrix, a coated surface (CS) is set up on each GNP surface, and the temperature of the CS and GNP can be obtained by the walk-on-spheres (WoS) method. Finally, the WoS method continues to be applied to calculate the heat flux density of the composite materials, further obtaining the thermal conductivity of the composites. We add the influence of interlayers in random walks. We incorporate the influence of interlayers in the WoS process, and the points that walk onto the interlayer surface have a very low probability of reaching the GNP due to the extremely low thermal conductivity of the interlayer. The calculated results are consistent with the experimental data. The model also studies the effects of the size, orientation, and aggregation of GNPs on the thermal conductivity of composite materials. Thermal conductivity ; Materials science ; Composite material ; Graphene ; Monte Carlo method ; Exfoliated graphite nano-platelets ; Conductivity ; Thermal ; Composite number ; Nanotechnology ; Physics ; Thermodynamics ; Mathematics Thermal properties of materials ; Graphene research and applications ; Composite Material Mechanics
Authors: Farinaz Mostajeran | Salah A Faroughi
Source: arXiv (Cornell University) (preprint, 2024-10-12)
Field: Materials Science — Materials Chemistry

EPi-cKANs: Elasto-Plasticity Informed Kolmogorov-Arnold Networks Using Chebyshev Polynomials Multilayer perceptron (MLP) networks are predominantly used to develop data-driven constitutive models for granular materials. They offer a compelling alternative to traditional physics-based constitutive models in predicting nonlinear responses of these materials, e.g., elasto-plasticity, under various loading conditions. To attain the necessary accuracy, MLPs often need to be sufficiently deep or wide, owing to the curse of dimensionality inherent in these problems. To overcome this limitation, we present an elasto-plasticity informed Chebyshev-based Kolmogorov-Arnold network (EPi-cKAN) in this study. This architecture leverages the benefits of KANs and augmented Chebyshev polynomials, as well as integrates physical principles within both the network structure and the loss function. The primary objective of EPi-cKAN is to provide an accurate and generalizable function approximation for non-linear stress-strain relationships, using fewer parameters compared to standard MLPs. To evaluate the efficiency, accuracy, and generalization capabilities of EPi-cKAN in modeling complex elasto-plastic behavior, we initially compare its performance with other cKAN-based models, which include purely data-driven parallel and serial architectures. Furthermore, to differentiate EPi-cKAN's distinct performance, we also compare it against purely data-driven and physics-informed MLP-based methods. Lastly, we test EPi-cKAN's ability to predict blind strain-controlled paths that extend beyond the training data distribution to gauge its generalization and predictive capabilities. Our findings indicate that, even with limited data and fewer parameters compared to other approaches, EPi-cKAN provides superior accuracy in predicting stress components and demonstrates better generalization when used to predict sand elasto-plastic behavior under blind triaxial axisymmetric strain-controlled loading paths. Chebyshev polynomials ; Mathematics ; Pure mathematics ; Applied mathematics ; Discrete mathematics ; Algebra over a field ; Mathematical analysis Machine Learning in Materials Science
Authors: Ali J. Ramadhan | Tufleuddin Biswas | Soumik Ray | Shobharam Anjanawe | Deepa Rawat | Binita Kumari | Shikha Yadav | Pradeep Mishra | Mostafa Abotaleb | Hussein Alkattan | Zainalabideen Albadran
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Modeling and Forecasting of Coconut Area, Production, and Productivity Using a Time Series Model The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections. Productivity ; Series (stratigraphy) ; Production (economics) ; Time series ; Productivity model ; Econometrics ; Computer science ; Agricultural economics ; Mathematics ; Economics ; Machine learning ; Microeconomics ; Total factor productivity ; Macroeconomics Coconut Research and Applications ; Natural Products and Biological Research ; Spectroscopy and Chemometric Analyses
Authors: Stephanie A. Wankowicz
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-15)
Field: Materials Science — Materials Chemistry

Modeling Bias Toward Binding Sites in PDB Structural Models Abstract The protein data bank (PDB) is one of the richest databases in biology. The structural models deposited have provided insights into protein folds, relationships to evolution, energy functions of structures, and most recently, protein structure prediction, connecting sequence to structure. However, the X-ray crystallography (and cryo-EM) models deposited in the PDB are determined by a combination of refinement algorithms and manual modeling. The intervention of human modeling leads to the possibility that within a single structure, there can be differences in how well parts of a structure are modeled and/or fit the underlying experimental data. We identified that small molecule binding sites are more carefully modeled and better match the underlying experimental data than the rest of the protein structural model. This trend persisted irrespective of the structure’s resolution or its overall agreement with the experimental data. The variation of modeling has implications for how we interpret protein structural models and use structural models in explaining mechanisms, structural bioinformatics, simulations, docking, and structure prediction, especially when drawing conclusions about binding sites compared to the rest of the protein. Protein Data Bank (RCSB PDB) ; Computer science ; Computational biology ; Biology ; Chemistry ; Stereochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Smaragda-Maria Argyri | Maëva Almeida | Fabrice Cousin | Lars Evenäs | Anne‐Laure Fameau | Clémence Le Cœur | Romain Bordes
Source: Journal of Colloid and Interface Science (article, 2024-09-14)
Field: Materials Science — Materials Chemistry

CO2 induced phase transition on a self-standing droplet studied by X-ray scattering and magnetic resonance The introduction of CO<sub>2</sub> gas induced a phase transition from micelles to multi-lamellar tubes, resulting in a gel-like behavior both in the bulk and at the interface. The high stability of the acoustic levitator allowed the investigation of this dynamic phenomenon, in real-time, in a contact-free environment. This study showcases the suitability of acoustic levitation as a tool to investigate complex chemical processes at interfaces. Scattering ; Phase transition ; Resonance (particle physics) ; Phase (matter) ; X-ray ; Materials science ; Nuclear magnetic resonance ; Condensed matter physics ; Physics ; Atomic physics ; Optics ; Quantum mechanics Pickering emulsions and particle stabilization ; Fluid Dynamics and Heat Transfer ; Electrohydrodynamics and Fluid Dynamics
Authors: Dr Trilokchandran B
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2024-02-16)
Field: Chemistry — Analytical Chemistry

A Review: Microwave Assisted Extraction Microwave-assisted extraction (MAE) technique involves heating solvents in contact with a sample using microwave energy in order to separate analytes from the sample matrix into the solvent. The primary benefit of MAE is its intrinsic capacity to quickly heat the sample solvent mixture. The ability of microwave radiation to penetrate and mix with a substrate allows for accurate and regulated heating. As a result, the electromagnetic energy can be specifically engineered to be transported by the microwave process to the desired location of the compounds in the substrate. The utilisation of short processing periods and energy- saving variables results in lower manufacturing costs, better product yields and consistency, and higher- quality products as compared to alternative extraction procedures. This review summarizes the basic principle involved in the MAE and its instrumentation. It also gives an overview of some of the applications of the MAE. Keywords: Microwave assisted Extraction (MAE), microwave radiation, closed vessel syste , open focused system Microwave ; Process engineering ; Extraction (chemistry) ; Materials science ; Sample preparation ; Analyte ; Matrix (chemical analysis) ; Process (computing) ; Computer science ; Chromatography ; Chemistry ; Engineering ; Telecommunications ; Composite material Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Microwave-Assisted Synthesis and Applications
Authors: Fernanda I. Saldívar‐González | Diana L. Prado‐Romero | Raziel Cedillo-González | Ana L. Chávez‐Hernández | Juan F. Avellaneda-Tamayo | Alejandro Gómez‐García | Luis Juárez-Rivera | José L. Medina‐Franco
Source: ChemRxiv (preprint, 2024-01-12)
Field: Chemistry — Physical and Theoretical Chemistry

A Spanish Chemoinformatics GitBook for Chemical Data retrieval and Analysis using Python Programming Searching, retrieving, and analyzing chemical information is one of the main tasks faced by students and professionals in chemistry-related scientific disciplines. Currently, freely available modules developed in programming languages, such as Python, allow efficient data management and facilitate obtaining information and knowledge from the data. This manuscript describes an electronic handbook generated on the GitBook platform to introduce the Python programming language and the Analysis, computational representation, and visualization of chemical data. This manual explores the most common molecular representations of low molecular weight organic compounds and their applications in various contexts. It also illustrates the acquisition of chemical information from large public molecular databases such as ChEMBL and PubChem and the Analysis and visualization of chemical information using concepts such as chemical space. The GitBook is freely available and is expected to foster open science and facilitate learning for chemistry students at the undergraduate and graduate levels and professionals interested in chemical data analysis and visualization. Cheminformatics ; Python (programming language) ; Computer science ; PubChem ; Visualization ; chEMBL ; Chemical space ; Data science ; World Wide Web ; Information retrieval ; Chemistry ; Data mining ; Programming language ; Drug discovery Various Chemistry Research Topics ; Chemistry and Chemical Engineering ; History and advancements in chemistry
Authors: Vladimir Gorshkov | Frank Kjeldsen
Source: Analytical Chemistry (article, 2024-01-06)
Field: Chemistry — Spectroscopy

Exploiting Charge State Distribution To Probe Intramolecular Interactions in Gas-Phase Phosphopeptides and Enhance Proteomics Analyses Charging of analytes is a prerequisite for performing mass spectrometry analysis. In proteomics, electrospray ionization is the dominant technique for this process. Although the observation of differences in the peptide charge state distribution (CSD) is well-known among experimentalists, its analytical value remains underexplored. To investigate the utility of this dimension, we analyzed several public data sets, comprising over 250,000 peptide CSD profiles from the human proteome. We found that the dimensions of the CSD demonstrate high reproducibility across multiple laboratories, mass analyzers, and extensive time intervals. The general observation was that the CSD enabled effective partitioning of the peptide property space, resulting in enhanced discrimination between sequence and constitutional peptide isomers. Next, by evaluating the CSD values of phosphorylated peptides, we were able to differentiate between phosphopeptides that indicate the formation of intramolecular structures in the gas phase and those that do not. The reproducibility of the CSD values (mean cosine similarity above 0.97 for most of the experiments) qualified CSD data suitable to train a deep-learning model capable of accurately predicting CSD values (mean cosine similarity - 0.98). When we applied the CSD dimension to MS1- and MS2-based proteomics experiments, we consistently observed around a 5% increase in protein and peptide identification rate. Even though the CSD dimension is not as effective a discriminator as the widely used retention time dimension, it still holds the potential for application in direct infusion proteomics. Chemistry ; Intramolecular force ; Gas phase ; Charge (physics) ; Chemical physics ; State (computer science) ; Proteomics ; Charge density ; Computational chemistry ; Nanotechnology ; Physical chemistry ; Stereochemistry ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: N Shobhana | Prakash Goudanavar | T Mallamma | Butchi Raju Akondi
Source: Journal of Young Pharmacists (article, 2024-08-23)
Field: Materials Science — Biomaterials

Magnetic Nano-Particles: Properties, Synthesis and its Application in Treatment of Breast Cancer-A Critical Review N Shobhana1, Prakash Goudanavar1, T Mallamma1 and Butchi Raju Akondi2 Author informationArticle notesCopyright and License informationCitations 1Department of Pharmaceutics, Sri Adichunchanagiri College of Pharmacy, Adichunchanagiri University, Mandya, INDIA 2Department of Clinical Pharmacy and Pharmacology, Ibn Sina National College for Medical Studies, Jeddah, SAUDI ARABIA Corresponding author. Correspondence: Mrs. Mallamma T Assistant Professor, Department of Pharmaceutics, Breast cancer ; Nano- ; Medicine ; Cancer ; Nanotechnology ; Internal medicine ; Oncology ; Materials science ; Composite material Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment
Authors: Bo Zhou | Changyin Dong | Xiaobo Li | Haobin Bai | Bin Yin | Huaiwen Li | Kaixiang Shen
Source: Energies (article, 2024-07-05)
Field: Materials Science — Materials Chemistry

CO2 Corrosion of Downhole Sand Control Screen: Experiments, Model, and Application Under simulated conditions typical of a high-temperature, high-pressure (HTHP) oil and gas reservoir in the South China Sea, dynamic corrosion evaluation experiments were performed on a three-layer screen structure and three types of sand retaining media. The results showed significant variations in corrosion morphology and rates among different screen components and materials. Corrosion products on the base pipe accumulated as cubic crystals, while the protective shroud showed surface needle-like corrosion products. Sand retaining media exhibited “coiled wire” corrosion products with cubic accumulations along seam edges. The 316L media showed a high corrosion risk, especially at temperatures between 140–150 °C. As CO2 partial pressure increased, corrosion rates generally rose. A new predictive method was developed to assess and compare the corrosion resistance and life of screens, achieving a compliance rate of over 90%. This method supports evaluating the corrosion life of screens in HTHP environments. For a typical well in the South China Sea gas field with 4% CO2, there is a high risk of screen corrosion. The screen media was identified as a failure site with a minimum corrosion life of about 5 years, while the protective shroud’s life was estimated at 11–12 years. Corrosion ; Petroleum engineering ; Environmental science ; Control (management) ; Geology ; Geotechnical engineering ; Metallurgy ; Materials science ; Computer science ; Artificial intelligence Corrosion Behavior and Inhibition ; Drilling and Well Engineering ; CO2 Sequestration and Geologic Interactions
Authors: Myung Kue Lee | Dongchan Kim | Min Ook Kim
Source: Polymers (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Experimental Study on the Chlorine-Induced Corrosion and Blister Formation of Steel Pipes Coated with Modified Polyethylene Powder In this study, chlorine-induced corrosion and blister formation on steel pipes (SPs) coated with modified polyethylene powder (MPP) were evaluated through various tests, including chlorine exposure, wet immersion, and temperature gradient experiments. The results confirmed that the extent of corrosion and iron leaching varied with the coating type as expected. In batch leaching tests, no corrosion was observed on modified polyethylene-coated steel pipes (MPCSPs) within a chlorine concentration range of 0 mg/L to 10 mg/L; similarly, there were no significant changes in specimen weight or iron levels. In contrast, the control group with uncoated SPs exhibited significant iron leaching and corrosion, a trend consistent in sequential leaching experiments. SEM analysis after a month of chlorine exposure revealed no significant corrosion on MPCSPs, and SEM-EDX confirmed no major changes in the carbon bond structure, indicating resistance to high chlorine concentrations. Comparative analysis of wet immersion and temperature gradient tests between MPCSP and conventional epoxy-coated SP (ECSP) specimens revealed that MPCSPs did not develop blisters even after 100 days of immersion, whereas ECSPs began showing blisters as early as 50 days. In temperature gradient tests, MPCSPs showed no blisters after 100 days, while ECSPs exhibited severe internal coating layer blisters. Blisters ; Corrosion ; Chlorine ; Materials science ; Leaching (pedology) ; Metallurgy ; Coating ; Carbon steel ; Polyethylene ; Penetration (warfare) ; Composite material Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Asphalt Pavement Performance Evaluation
Authors: Marco De Lucia
Source: Advances in geosciences (article, 2024-11-12)
Field: Chemistry — Inorganic Chemistry

DecTree: a physics-based geochemical surrogate for surface complexation of uranium on clay Abstract. Geochemistry is usually the computational bottleneck in coupled reactive transport simulations, which hampers the complexity of the systems and of the processes they can investigate. In recent years, promising speedups have been obtained by substituting the numerical solution of geochemical models with approximated surrogates borrowed from artificial intelligence and machine learning (AI/ML). In the framework of the DONUT/EURAD project a set of benchmarks were defined to assess the performance and the accuracy of different surrogate approaches in settings relevant to the safety assessment of nuclear waste repositories, such as the surface complexation and exchange of U(VI) on clay. In this context, this work introduces am original surrogate modelling approach based on recursive partitioning of parameter space, which exploits prior domain knowledge for the training. The surrogate, which can be represented as a decision tree, hence the DecTree name, performs dimensionality reduction by identifying functional relationships between outputs and input variables using a straightforward non-monotonic extension of the Spearman's rank correlation coefficient. New predictions are then interpolated from the partitioned training data. Applied to a low-dimensional geochemical model, DecTree shows virtually no training time and excellent accuracy, ensuring a throughput of around 500 000 predictions per second on a single CPU core. Uranium ; Geology ; Mineralogy ; Materials science ; Metallurgy Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping ; Groundwater flow and contamination studies