jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiarui Fan | Mingze Yao | Zhiqiang Fan | Peng Chen | Kun He | Dayong Wang
Source: Energy & Fuels (article, 2024-03-26)
Field: Chemistry — Analytical Chemistry

Unveiling the Effect of Complex Components on CO<sub>2</sub>-Oil Minimum Miscibility Pressure: Insights from Deep Convolutional Neural Networks The determination of the minimum miscibility pressure (MMP) between CO2 and oil holds significant importance in the analysis and modeling of CO2 miscible flooding processes. Several components in both CO2 and crude oil could exert complex effects on MMP values. To address this issue in depth, we designed a novel convolutional neural network to investigate the effect of some complex components in CO2 and oil on MMP in the context of CO2 enhanced oil recovery. Our results yield valuable insights: (1) employing a multilayer deep convolutional structure, our neural network (NN) model is capable of deeply understanding the relationships of MMP with the contents of various components, extending the type and number of the considered components. Using temperature and the contents of totally 11 components, which do not show obvious cross-correlation, as inputs, the established NN model is demonstrated to attain high prediction precision; (2) temperature has the highest positive correlation with MMP, but the correlation would diminish beyond a certain temperature value. Among the analyzed components, molecular weight of C7+ and volatile components in oil as well as that of nitrogen, methane, and C6+ in CO2 gas show positive correlation with MMP, while mole percentages of intermediate light and heavy hydrocarbons in oil as well as that of hydrogen sulfide, intermediate light hydrocarbon, and C5 in CO2 gas exhibit negative correlation. Comparatively, MMP demonstrates heightened sensitivity to variations in molecular weight of C7+ in oil, while displaying less susceptibility to changes in mole percentage of methane, C2–C4, and C5 in gas. The elucidated correlations and sensitivities between MMP and these 12 attributes hold significant potential for future advancements in deriving and refining empirical prediction formulas for MMP. Miscibility ; Convolutional neural network ; Artificial neural network ; Chemistry ; Thermodynamics ; Materials science ; Environmental science ; Artificial intelligence ; Computer science ; Physics ; Organic chemistry Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Enhanced Oil Recovery Techniques
Authors: Mingxiu Liu | Lukáš Grajciar | Christopher J. Heard
Source: Microporous and Mesoporous Materials (article, 2024-06-13)
Field: Chemistry — Inorganic Chemistry

The role of defects in high-silica zeolite hydrolysis and framework healing Zeolite ; Hydrolysis ; Chemical engineering ; Chemistry ; Materials science ; Catalysis ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization ; Concrete and Cement Materials Research
Authors: Yu Sun | Heike Helmholz | Regine Willumeit‐Römer
Source: Journal of Magnesium and Alloys (article, 2024-01-29)
Field: Materials Science — Biomaterials

Systemic modulation of skeletal mineralization by magnesium implant promoting fracture healing: Radiological exploration enhanced with PCA-based machine learning in a rat femoral model The clinical application of magnesium (Mg) and its alloys for bone fractures has been well supported by in vitro and in vivo trials. However, there were studies indicating negative effects of high dose Mg intake and sustained local release of Mg ions on bone metabolism or repair, which should not be ignored when developing Mg-based implants. Thus, it remains necessary to assess the biological effects of Mg implants in animal models relevant to clinical treatment modalities. The primary purpose of this study was to validate the beneficial effects of intramedullary Mg implants on the healing outcome of femoral fractures in a modified rat model. In addition, the mineralization parameters at multiple anatomical sites were evaluated, to investigate their association with healing outcome and potential clinical applications. Compared to the control group without Mg implantation, postoperative imaging at week 12 demonstrated better healing outcomes in the Mg group, with more stable unions in 3D analysis and high-mineralized bridging in 2D evaluation. The bone tissue mineral density (TMD) was higher in the Mg group at the non-operated femur and lumbar vertebra, while no differences between groups were identified regarding the bone tissue volume (TV), TMD and bone mineral content (BMC) in humerus. In the surgical femur, the Mg group presented higher TMD, but lower TV and BMC in the distal metaphyseal region, as well as reduced BMC at the osteotomy site. Principal component analysis (PCA)-based machine learning revealed that by selecting clinically relevant parameters, radiological markers could be constructed for differentiation of healing outcomes, with better performance than 2D scoring. The study provides insights and preclinical evidence for the rational investigation of bioactive materials, the identification of potential adverse effects, and the promotion of diagnostic capabilities for fracture healing. Intramedullary rod ; Bone healing ; Femur ; Medicine ; Implant ; Bone mineral ; Osteotomy ; Soft tissue ; Biomechanics ; Osteoporosis ; Urology ; Biomedical engineering ; Surgery ; Internal medicine ; Anatomy Magnesium Alloys: Properties and Applications ; Magnesium in Health and Disease ; Orthopaedic implants and arthroplasty
Authors: Reza Hassannejad | Babak Alizadeh-Hamidi
Source: International Journal of Mechanics and Materials in Design (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Simultaneous effects of material and geometric nonlinearities on nonlinear vibration of nanobeam with surface energy effects Solid mechanics ; Vibration ; Nonlinear system ; Materials science ; Surface (topology) ; Energy (signal processing) ; Surface energy ; Mechanics ; Structural engineering ; Composite material ; Mathematics ; Geometry ; Physics ; Acoustics ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Mechanical and Optical Resonators
Authors: Afsar Husain | Mohd Danish | Sanan H. Khan | Abdel‐Hamid I. Mourad
Source: Heliyon (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Predicting projectile residual velocities using an advanced artificial neural network model In this research, we delve into the fascinating dynamics of projectiles and their interactions with materials, with a keen focus on residual velocity - the speed a projectile retains after striking a target. This parameter is pivotal, especially when considering the design of protective barriers in various environments. Traditional methods of gauging residual velocity have been cumbersome, resource-intensive, and occasionally inconsistent. To address these challenges, we introduce an innovative approach using an Artificial Neural Network (ANN) model through MATLAB R2021a. This computerized tool, trained on a rich dataset from prior research, can predict residual velocities by considering multiple factors, including the initial speed of the projectile, its material and shape, and the thickness of the target. This paper meticulously details the development, training, and validation of the ANN model, highlighting its superior accuracy when compared to traditional methods like the Recht-Ipson model. The developed ANN model demonstrated remarkable performance compared to the Recht-Ipson model. During training, it exhibited a Mean Absolute Percentage Error (MAPE) of 0.0259 and a Root Mean Squared Error (RMSE) of 1.5993. For validation, MAPE was 0.0295, and RMSE was 2.2056. In contrast, the Recht-Ipson model displayed higher errors, with MAPE and RMSE values of 0.2349 and 14.1791, respectively. Furthermore, we discuss the potential of the ANN model in predicting not just residual velocities but also absorbed energy, showcasing its versatility. The practical implications of our findings are vast. From designing safer infrastructures in urban settings to enhancing armour systems in military applications, the ANN model's predictions can be a cornerstone for innovation. Artificial neural network ; Residual ; Projectile ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Algorithm High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Energetic Materials and Combustion
Authors: Fawaz S. Alharbi
Source: Solid State Sciences (article, 2024-02-14)
Field: Materials Science — Materials Chemistry

Modeling thermoelectric figure of merit of BiCuSeO oxyselenide ceramic using extreme learning machine computational method Figure of merit ; Materials science ; Ceramic ; Mean squared error ; Energy conversion efficiency ; Thermoelectric effect ; Composite material ; Mathematics ; Optoelectronics ; Thermodynamics ; Physics ; Statistics Advanced Thermoelectric Materials and Devices ; Machine Learning and ELM ; Perovskite Materials and Applications
Authors: V. N. Senthil Kumaran | M. Venkatesh | Abdulrahman Saad Alqahtani | Azath Mubarakali | P. Parthasarathy
Source: Journal of Electronic Materials (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Optimization of Sensor Morphology and Sensing Performance in a Non-enzymatic Graphene FET Biosensor for Detection of Biomolecules in Complex Analytes Biosensor ; Biomolecule ; Graphene ; Analyte ; Nanotechnology ; Materials science ; Solid-state physics ; Chemistry ; Chromatography ; Physics Graphene research and applications ; Advanced biosensing and bioanalysis techniques ; Graphene and Nanomaterials Applications
Authors: Haoran Cui | Iyyappa Rajan Panneerselvam | Pranay Chakraborty | Qiong Nian | Yiliang Liao | Yan Wang
Source: Carbon (article, 2024-12-15)
Field: Materials Science — Materials Chemistry

Significantly enhanced interfacial thermal transport between single-layer graphene and water through basal-plane oxidation Basal plane ; Graphene ; Materials science ; Layer (electronics) ; Thermal ; Basal (medicine) ; Thermal oxidation ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Crystallography ; Thermodynamics ; Physics Thermal properties of materials ; Graphene research and applications ; Semiconductor materials and devices
Authors: Jiaqing Shi | Zheng Lv | Jian Wang | Wentao Tang | Yufei Liu | Zenglin Yang | Jian Yang | Zhimin Yang | Shuwang Ma
Source: Journal of Nuclear Materials (article, 2024-10-02)
Field: Materials Science — Materials Chemistry

A finite element study on the irradiation-induced mechanical behaviors of aluminum-matrix radiation-shielding composites Electromagnetic shielding ; Irradiation ; Composite material ; Materials science ; Aluminium ; Finite element method ; Matrix (chemical analysis) ; Radiation shielding ; Structural engineering ; Physics ; Nuclear physics ; Engineering High-Velocity Impact and Material Behavior ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Zhengming Wang | Seongun Yang | Stephanie Lawson | V. Vinay K. Doddapaneni | Marc Albert | Benjamin Sutton | Chih‐Hung Chang | Somayeh Pasebani | Donghua Xu
Source: Materials (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Atomic Diffusivities of Yttrium, Titanium and Oxygen Calculated by Ab Initio Molecular Dynamics in Molten 316L Oxide-Dispersion-Strengthened Steel Fabricated via Additive Manufacturing Oxide-dispersion-strengthened (ODS) steels have long been viewed as a prime solution for harsh environments. However, conventional manufacturing of ODS steels limits the final product geometry, is difficult to scale up to large components, and is expensive due to multiple highly involved, solid-state processing steps required. Additive manufacturing (AM) can directly incorporate dispersion elements (e.g., Y, Ti and O) during component fabrication, thus bypassing the need for an ODS steel supply chain, the scale-up challenges of powder processing routes, the buoyancy challenges associated with casting ODS steels, and the joining issues for net-shape component fabrication. In the AM process, the diffusion of the dispersion elements in the molten steel plays a key role in the precipitation of the oxide particles, thereby influencing the microstructure, thermal stability and high-temperature mechanical properties of the resulting ODS steels. In this work, the atomic diffusivities of Y, Ti, and O in molten 316L stainless steel (SS) as functions of temperature are determined by ab initio molecular dynamics simulations. The latest Vienna Ab initio Simulation Package (VASP) package that incorporates an on-the-fly machine learning force field for accelerated computation is used. At a constant temperature, the time-dependent coordinates of the target atoms in the molten 316L SS were analyzed in the form of mean square displacement in order to obtain diffusivity. The values of the diffusivity at multiple temperatures are then fitted to the Arrhenius form to determine the activation energy and the pre-exponential factor. Given the challenges in experimental measurement of atomic diffusivity at such high temperatures and correspondingly the lack of experimental data, this study provides important physical parameters for future modeling of the oxide precipitation kinetics during AM process. Materials science ; Thermal diffusivity ; Oxide ; Dispersion (optics) ; Diffusion ; Microstructure ; Molecular dynamics ; Ab initio ; Metallurgy ; Thermodynamics ; Composite material ; Computational chemistry ; Chemistry Fusion materials and technologies ; Nuclear Materials and Properties ; Additive Manufacturing Materials and Processes
Authors: Anand Babu | B. Moses Abraham | Sudip Kumar Naskar | Spandan Ranpariya | Dipankar Mandal
Source: Small (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Machine Learning‐Enabled Nanoscale Phase Prediction in Engineered Poly(Vinylidene Fluoride) Engineered poly(vinylidene fluoride) (PVDF) with its diverse crystalline phases plays a crucial role in determining the performance of devices in piezo-, pyro-, ferro- and tribo-electric applications, indicating the importance of distinct phase-detection in defining the structure-property relation. However, traditional characterization techniques struggle to effectively distinguish these phases, thereby failing to offer complete information. In this study, multimodal data-driven techniques have been employed for distinguishing different phases with a machine learning (ML) approach. This developed multimode model has been trained from empirical to theoretical data and demonstrates a classification accuracy of >94%, 15% more noise resilience, and 11% more accuracy from unimodality. Thus, from conception to validation, an alternative approach is provided to autonomously distinguish the different PVDF phases and eschew repetitive experiments that saved resources, thus accelerating the process of materials selection in various applications. Nanoscopic scale ; Materials science ; Characterization (materials science) ; Fluoride ; Process (computing) ; Phase (matter) ; Resilience (materials science) ; Nanotechnology ; Computer science ; Artificial intelligence ; Machine learning ; Biological system ; Physics ; Composite material ; Chemistry Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Sensor and Energy Harvesting Materials
Authors: Kerui Wei | Han Liu | Huatang Cao | Zhiquan Kho | Alexander S. Eggeman | João P. Martins | Esma Yilmaz | Jack Donoghue | Zixian Su | Ping Xiao
Source: Journal of the European Ceramic Society (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Interactions between silver-palladium alloy and silicon carbide cladding layer in TRISO fuel: An in-depth analysis High-temperature gas-cooled reactors (HTGRs) have garnered considerable interest due to their superior efficiency and inherent safety. This study systematically investigates the interaction between silver-palladium (Ag-Pd) alloy and silicon carbide (SiC) coating in tri-structural isotropic (TRISO) particles of HTGRs, focusing on the potential release of radioactive Ag-110m through SiC cladding. Operating temperatures ranging from 800-1100°C were examined to understand the mechanisms of Ag-Pd migration in SiC. Advanced techniques like transmission electron microscopy (TEM), focused ion beam (FIB) coupled with 3D visualization and scanning precession electron diffraction (SPED) integrated with machine learning were employed to identify two pathways of Pd-assisted Ag migration: (1) (Pd,Ag)2Si and (Pd,Ag)3Si acting as migration channels, and (2) Pd-rich phases increasing SiC disorder, enhancing Ag diffusion. Significantly, a carbon barrier formation above 1000°C was observed, which inhibits Ag-Pd/SiC interaction. These findings offering new insights for predicting nuclear reactor lifespan and data support to optimize TRISO particles' structural design. Materials science ; Silicon carbide ; Cladding (metalworking) ; Palladium ; Alloy ; Carbide ; Coating ; Silicon ; Chemical engineering ; Metallurgy ; Composite material ; Catalysis Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Sheng Zhou | Yongxin Gai | Guoqiang Lan
Source: Optics Communications (article, 2024-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of tunable terahertz metamaterial absorber based on deep learning method Terahertz radiation ; Metamaterial ; Materials science ; Terahertz metamaterials ; Optoelectronics ; Metamaterial absorber ; Optics ; Computer science ; Tunable metamaterials ; Physics ; Far-infrared laser Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Xinyue Zhao | Dianming Chu | Xijun Zhang | Chenyu Gao | Yan He | Wenjuan Bai
Source: Diamond and Related Materials (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

Growth and characterization of carbon nanotubes and study of modified carbon fiber—A review Carbon nanotube ; Characterization (materials science) ; Nanotechnology ; Materials science ; Carbon fibers ; Carbon nanobud ; Chemical engineering ; Composite material ; Optical properties of carbon nanotubes ; Composite number ; Nanotube ; Engineering Carbon Nanotubes in Composites ; Graphene research and applications ; Supercapacitor Materials and Fabrication
Authors: Beata E. Kowal | Krzysztof M. Graczyk | Artur M. Ankowski | Rwik Dharmapal Banerjee | Hemant Prasad | J. T. Sobczyk
Source: Physical review. C (article, 2024-08-06)
Field: Materials Science — Surfaces, Coatings and Films

Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of deep-inelastic scattering. We consider two different methods of obtaining such model-independent parametrizations and the corresponding uncertainties: based on the bootstrap approach and the Monte Carlo dropout approach. In our analysis, the <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:msup><a:mi>χ</a:mi><a:mn>2</a:mn></a:msup></a:math> defines the loss function, including point-to-point and normalization uncertainties for each independent set of measurements. Our statistical approaches lead to fits of comparable quality and similar uncertainties of the order of 7%. To test these models, we compare their predictions to test datasets excluded from the training process and theoretical predictions obtained within the spectral function approach. The predictions of both models agree with experimental measurements and theoretical calculations. We also perform a comparison to a dataset lying beyond the covered kinematic region, and find that the bootstrap approach shows better interpolation and extrapolation abilities than the one based on the dropout algorithm. Published by the American Physical Society 2024 Extrapolation ; Interpolation (computer graphics) ; Monte Carlo method ; Statistical physics ; Normalization (sociology) ; Computer science ; Mathematics ; Algorithm ; Applied mathematics ; Physics ; Artificial intelligence ; Statistics Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Nuclear Physics and Applications
Authors: Hocine Heraiz | Chouki Farsi | Hocine Makri | Salah Amroune | Ahmed Belaadi | Khalissa Saada | Moussa Zaoui | Mohammed Ismail Beddiar
Source: Journal of Composite Materials (article, 2024-04-10)
Field: Materials Science — Polymers and Plastics

Assessment of mechanical and physicochemical properties of palm fiber composites: Effect of alkaline treatment and volume alterations This study assesses the impact of alkaline treatments and volume fractions on biocomposites composed of a high-density polyethylene (HDPE) matrix reinforced with date palm tree fibers (FPDS). Tensile tests were conducted on both untreated and NaOH-treated biocomposites. Additionally, fiber analysis was performed using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results reveal higher strength and stiffness compared to HDPE, albeit with limited plasticity making the material brittle. The NaOH treatment enhances certain mechanical properties. Further assessments encompassed hardness, density, melt index, and Izod impact tests. Two volume fractions, 20% and 25%, of FPDS were tested. The study establishes a correlation between empirical predictions and artificial neural network (ANN) models. Notably, an ANN architecture consisting of two input factors, 10 hidden nodes, and one output provides the analysis of mechanical properties. This investigation highlights the potential of FPDS-reinforced HDPE biocomposites, emphasizing their mechanical performance under various treatments and fiber levels. Materials science ; Composite material ; Fiber ; Volume (thermodynamics) Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Textile materials and evaluations
Authors: Xiaxi Xu | Qingfu Huang | Bingbin Chen | Bo Niu | Yayun Zhang | Donghui Long
Source: Chemical Engineering Journal (article, 2024-04-03)
Field: Chemistry — Spectroscopy

High-Precision 3D reconstruction and quantitative structure description: Linking microstructure to macroscopic heat transfer of aerogels Nanoporous ; Microstructure ; Materials science ; Porosity ; Heat transfer ; Biological system ; Focused ion beam ; Computer science ; Projection (relational algebra) ; Thermal conductivity ; Artificial neural network ; Artificial intelligence ; Nanotechnology ; Physics ; Algorithm ; Mechanics ; Ion ; Composite material Aerogels and thermal insulation ; Catalytic Processes in Materials Science ; Enhanced Oil Recovery Techniques