jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Beichen Li | Bolei Deng | Wan Shou | Tae-Hyun Oh | Yuanming Hu | Yiyue Luo | Liang Shi | Wojciech Matusik
Source: arXiv (Cornell University) (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics. Toughness ; Stiffness ; Computer science ; Workflow ; Robotics ; Materials science ; Mechanical engineering ; Artificial intelligence ; Engineering ; Composite material ; Robot Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Probabilistic and Robust Engineering Design
Authors: Mohammad Sadegh Akhondzadeh | Vijay Lingam | Aleksandar Bojchevski
Source: arXiv (Cornell University) (preprint, 2023-03-07)
Field: Materials Science — Materials Chemistry

Probing Graph Representations Today we have a good theoretical understanding of the representational power of Graph Neural Networks (GNNs). For example, their limitations have been characterized in relation to a hierarchy of Weisfeiler-Lehman (WL) isomorphism tests. However, we do not know what is encoded in the learned representations. This is our main question. We answer it using a probing framework to quantify the amount of meaningful information captured in graph representations. Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models. We compare different families of models and show that transformer-based models capture more chemically relevant information compared to models based on message passing. We also study the effect of different design choices such as skip connections and virtual nodes. We advocate for probing as a useful diagnostic tool for evaluating graph-based models. Computer science ; Graph isomorphism ; Theoretical computer science ; Graph ; Expressive power ; Inductive bias ; Artificial intelligence ; Machine learning ; Line graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Gadel F. Baimukhametov | Aydar A. Kayumov | Aleksey V. Dengaev | А. Ф. Максименко | Denis A. Marakov | V.A. Shishulin | Ilya M. Drozdov | Larisa V. Samuylova | Andrey A. Getalov | Firdavs A. Aliev | Аlexey V. Vakhin
Source: Fluids (article, 2023-10-04)
Field: Materials Science — Materials Chemistry

Unveiling the Potential of Cavitation Erosion-Induced Heavy Crude Oil Upgrading The application of ultrasonic waves in the processing of hydrocarbons is a new promising technology that has developed rapidly in recent years. However, the acoustic-induced cavitation erosion phenomenon is poorly studied. In this paper, a comparison study of cavitation erosion was carried out in water and oils with different viscosities produced from Ashal’cha and North Komsomol using an ultrasonic reactor operating at an industrial frequency of 20 kHz. The acoustic spectra obtained from hydrophones during the ultrasonic treatment of fluids can be characterized by using subharmonics of the main frequency and a continuous white noise level. Moreover, the cavitation erosion of aluminum foil under various ultrasound irradiation times and power levels was thoroughly investigated. It has been found that the process of ultrasonic cavitation has a less erosive impact on metal foil in oil due to its high viscosity. In addition, the formation of microflows in the oil phase, which also intensify the erosion process, is hindered. Cavitation erosion in the Ashal’cha oil sample exhibited a higher intensity compared to that in the North Komsomol oil sample. It was found that upon increasing ultrasound intensity in the case of the viscous (Ashal’cha) oil sample, cavitation stability was disrupted. In turn, this led to a reduction in the collapse energy of the cavitation bubbles. The results we obtained enable the assessment of cavitation erosion in crude oil and could be used to improve methodologies for monitoring and optimizing cavitation processes in crude oil. Cavitation ; Erosion corrosion ; Ultrasonic sensor ; Materials science ; Ultrasound ; Viscosity ; Erosion ; FOIL method ; Bubble ; Environmental science ; Acoustics ; Petroleum engineering ; Composite material ; Geology ; Mechanics ; Corrosion Ultrasound and Cavitation Phenomena ; Petroleum Processing and Analysis ; Fluid Dynamics and Mixing
Authors: Asmaa Reda Abd El-Salam | K. E. Rady | Ezzat A. ELFadaly | Mobarak Aly
Source: Journal of Nanotechnology and Nanomaterials (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Enhanced Structural and Morphological Properties of Doped Cobalt Zinc Ferrite In this study, Mn2+ substituted Co0.8−x Mnx Zn0.2 (where x = 0.0, 0.1, 0.2, and 0.3) ferrites are prepared by a coprecipitation method to study the effect of Mn2+ions on the structural and morphological properties. These ferrites are characterized by X-ray powder diffraction (XRD), and Fourier transform infrared. X-ray diffraction patterns of the prepared samples confirm partial substitution of Mn2+ ions that does not change the basic structure of Co0.8 Zn0.2 Fe2O4. It also provides information about the formation of a single-phase spinel structure without any secondary phase. It is concluded that Co0.6 Mn0.2 Zn0.2 Fe2O4 has a spherical shape with an average particle size of 22.51 nm based on TEM, as confirmed by the XRD analysis. FT-IR analysis confirms the formation of vibrational frequency bands associated with the entire spinel structure. The IR spectra of ferrites show two clear and sharp absorption bands in the range of 442.09 and 620.21 cm-1 in the range of 200–1000 cm-1, which confirms the formation of the ferrite composite. Spinel ; Coprecipitation ; Ferrite (magnet) ; Materials science ; Diffraction ; Fourier transform infrared spectroscopy ; Analytical Chemistry (journal) ; Cobalt ; Zinc ferrite ; Infrared ; Infrared spectroscopy ; Ion ; X-ray crystallography ; Doping ; Powder diffraction ; Zinc ; Crystallography ; Chemistry ; Inorganic chemistry ; Metallurgy ; Optics ; Composite material ; Physics Magnetic Properties and Synthesis of Ferrites
Authors: R. C. Die | Fanté Bamba | N. C. M. Ambeu-Loko | Marie‐Aude Hiebel | Aurélie Vallin | Franck Suzenet | Vincent Chagnault
Source: RASAYAN Journal of Chemistry (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

SYNTHESIS AND ANTIBACTERIAL ACTIVITIES OF NEW 6- ARYL-4-OXO-1,4-DIHYDROPYRIMIDINE DERIVATIVES Pyrimidine derivatives are an important class of compounds in medicinal chemistry.In this study, a series of 6-aryl-4-oxo-1,4-dihydropyrimidine-5-carbonitrile derivatives were synthesized using a multicomponent reaction, followed by S-alkylation with different halogenated derivatives, and then evaluated for their antibacterial activities.Ciprofloxacin was used as the reference antibiotic.The prepared compounds were characterized by 1 H, 13 C NMR, FT-IR spectroscopy, and LC-and HR-MS spectrometry.All compounds were screened in vitro against Pseudomonas aeruginosa and Escherichia coli (Gram-negative bacteria), as well as Staphylococcus aureus and Enterococcus faecalis (Gram-positive bacteria).The results showed that S-alkylation is beneficial in improving the antibacterial activity of the thiouracil derivatives offering a few compounds with antibacterial activity against Gram-positive bacteria. Aryl ; Combinatorial chemistry ; Chemistry ; Antibacterial activity ; Organic chemistry ; Bacteria ; Biology ; Genetics Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Multicomponent Synthesis of Heterocycles
Authors: Akbar Eshaghi | Hossein Salehi | Faezeh Saeedi | Abolfazl Bayati
Source: Preprints.org (preprint, 2023-10-18)
Field: Materials Science — Surfaces, Coatings and Films

Anti-Icing Epoxy-Modified Silica Nano-Composite Coating on Conductors Used in Overhead Power Lines In the present study, an epoxy-modified silica nano-composite coating was deposited on an aluminum substrate and ACSR conductor. For this purpose, super-hydrophobic modified silica nanoparticles based on the TEOS and MTES precursors were prepared. Then, the modified silica nanoparticles were added to the epoxy resin solution. The coating deposition on an aluminum substrate and ACSR conductors was performed by a spraying method. The structure, morphology, and chemical analysis of the nanoparticle’s surface were studied with X-ray diffractometer (XRD), Transmission electron microscopy (TEM), and Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) methods. The water contact angles on the prepared samples were measured by a water contact angle analyzer method. Icing tests were performed at a constant operating temperature of -14 °C. Also, the self-cleaning behavior of the samples was evaluated with an iron oxide powder. The results indicated that the epoxy-modified silica nano-composite coating showed hydrophobic (CA, 141°), anti-icing, and self-cleaning properties. Materials science ; Epoxy ; Contact angle ; Coating ; Diffractometer ; Composite material ; Composite number ; Fourier transform infrared spectroscopy ; Nanoparticle ; Substrate (aquarium) ; Scanning electron microscope ; Nano- ; Superhydrophobic coating ; Hydrophobic silica ; Chemical engineering ; Nanotechnology Surface Modification and Superhydrophobicity ; Icing and De-icing Technologies ; High voltage insulation and dielectric phenomena
Authors: Esther Heid | Daniel Probst | William H. Green | Georg K. H. Madsen
Source: ChemRxiv (preprint, 2023-04-20)
Field: Materials Science — Materials Chemistry

EnzymeMap: Curation, validation and data-driven prediction of enzymatic reactions Enzymatic reactions are an ecofriendly, selective and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online. Retrosynthetic analysis ; Computer science ; Regioselectivity ; Artificial intelligence ; Margin (machine learning) ; Machine learning ; Training set ; Set (abstract data type) ; Feature (linguistics) ; Biochemical engineering ; Chemistry ; Engineering ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Bioinformatics and Genomic Networks
Authors: Inzamam Ul Haq | Feipeng Wang
Source: IET Nanodielectrics (article, 2023-07-04)
Field: Materials Science — Materials Chemistry

Multi‐modified epoxy insulation with improved flashover threshold for HVDC applications Abstract The flashover threshold ( V flsh ) of polymer insulations such as epoxy (EP) in HVDC systems can be augmented by modifying their surface with an appropriate treatment technology. For such purpose, several surface treatment methods such as ion beam, sandpaper and a combination of the two is introduced. Firstly, insulation samples with pure epoxy ( EP pure ), different ion beam treatment periods ( EP ion ;10, 15, 20 min) and different roughness ( EP sand ; R 1 = 4.23 μm, R 2 = 6.34 μm, R 3 = 9.21 μm) are prepared on the laboratory scale. Afterward, based on several characterisations, such as surface conductivity, mean surface roughness and potential distribution of these insulations, multi‐modified insulation ( EP multi = EP ion‐20 + EP sand‐R3 ) is prepared. In the end, the V flsh of each insulation group is measured and compared to examine the effectiveness of the proposed modifications. It is obtained that the V flsh of the modified insulations augmented dramatically irrespective of the treatment method. The V flsh of the insulation group EP multi augmented by 52.66 % which is the highest improvement among all insulation groups. In short, the proposed surface modifications are effective and could be used as references to enhance the insulation strength of polymer dielectrics in HVDC systems. Epoxy ; Sandpaper ; Arc flash ; Materials science ; Composite material ; Polymer ; Surface roughness ; Dielectric strength ; Conductivity ; Dielectric ; Chemistry ; Optoelectronics ; Insulator (electricity) High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Power Transformer Diagnostics and Insulation
Authors: Megha Tonk | Shabnam Ain | Babita Kumar | Qurratul Ain
Source: Annals of Phytomedicine An International Journal (article, 2023-06-01)
Field: Chemistry — Analytical Chemistry

Study the effect of polymers on the release rate of propranolol hydrochloride from controlled release matrix tablets The aim of the present study was to see how polymers effect on the rate of propranolol hydrochloride (propranolol HCl) release from a controlled release matrix tablet.Hydroxy propyl methyl cellulose (HPKMC K4M and HPMC K15M) and ethyl cellulose (EC) were used to retard the rate of propranolol HCl release.The UV (Ultraviolet) absorption, FTIR (Fourier transform infrared) spectroscopy and DSC (Differential scanning calorimetry) spectra showed that propranolol HCl is compatible with HPMC K4M, HPMC K15M and ethyl cellulose.Controlled release tablets were made using a wet granulation technique.Angle of repose, bulk density, tapped density and compressibility index of propranolol HCl were all assessed prior to compression.Prepared tablets were evaluated for thickness, hardness, content uniformity, % friability and disintegration time and % drug release.All the parameters of pre-compression and post compression comply with standard specification.The in vitro drug release study disclosed that a formulation containing HPMC K15M and ethyl cellulose retarded the drug release rate for maximum time.The results of this investigation manifested that a blend of hydrophilic and hydrophobic polymers might be used to successfully enhance delayed rate of propranolol hydrochloride release from its controlled release matrix tablet. Propranolol Hydrochloride ; Controlled release ; Polymer ; Matrix (chemical analysis) ; Immediate release ; Propranolol ; Materials science ; Chemistry ; Chromatography ; Composite material ; Nanotechnology ; Medicine ; Anesthesia Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems
Authors: М. И. Бичурин | Oleg Sokolov | С. Н. Иванов | E. Е. Ivasheva | Viktor Leontiev | Vyacheslav Lobekin | Gennady Semenov
Source: Materials (article, 2023-08-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling the Magnetoelectric Composites in a Wide Frequency Range This article presents a general theory of the ME effect in composites in the low- and high-frequency ranges. Besides the quasi-static region, the area of electromechanical resonance, including longitudinal, bending, longitudinal shear, and torsional modes, is considered in more detail. To demonstrate the theory, expressions of ME voltage coefficients are obtained for symmetric and asymmetric layered structures. A comparison is made with the experimental results for the GaAs/Metglas and LiNbO<sub>3</sub>/Metglas structures. The main microwave ME effect, consisting of the FMR line shift in an electric field, for the ferromagnetic metals, their alloys, and YIG ferrite using various piezoelectrics is discussed. In addition to analytical calculations, in the article, finite element modeling is considered. The calculation methods and experimental results are compared for some composites. Metglas ; Materials science ; Finite element method ; Composite material ; Piezoelectricity ; Ferrite (magnet) ; Microwave ; Shear (geology) ; Structural engineering ; Amorphous metal ; Alloy ; Physics ; Engineering Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Ultrasonics and Acoustic Wave Propagation
Authors: Mahmut Di̇ri̇k
Source: International Conference on Contemporary Academic Research (article, 2023-05-24)
Field: Chemistry — Analytical Chemistry

Improving Raisin Grains Classification with a Hybrid PSO-NN Approach – Raisin grain classification is crucial in the food industry for maintaining product quality.Traditional classification techniques can be labor-intensive and time-consuming, presenting significantchallenges. To address these issues, this study proposes a hybrid approach for raisin classification thatcombines Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN). The PSO algorithmis utilized to optimize ANN models with the aim of enhancing the accuracy of raisin grain classification.Our research, conducted on a dataset consisting of 900 raisin grains from two distinct categories, evaluatesthe performance of the proposed hybrid PSO-ANN method in comparison to k-Nearest Neighbor (KNN)and Random Tree (RT). The hybrid PSO-NN approach achieved a remarkable classification performance,demonstrating an accuracy rate of 100%, outperforming other methods under evaluation. The respectiveaccuracies of KNN and RT were 87.39% and 94.91%. This outstanding performance underscores theefficacy of integrating PSO optimization with ANN in the field of raisin grain classification. The resultssuggest that the hybrid PSO-ANN approach surpasses other methods in classification accuracy, indicatingits potential to advance raisin grain classification within the food industry. Particle swarm optimization ; Artificial neural network ; Artificial intelligence ; Computer science ; Field (mathematics) ; Pattern recognition (psychology) ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Qi An | Panshuo Yang | Kepeng Wang | Xiaohui Han | Guoqing An | Zhenbin Du | Ping He
Source: Research Square (preprint, 2023-08-31)
Field: Materials Science — Materials Chemistry

Partial discharge fault identification of transformer based on CNN-SVM and multi-model fusion Abstract Partial discharge pattern recognition has been established as a standard diagnostic tool for monitoring the operation of electrical equipment. Intelligent state recognition is the development trend of transformer state recognition, but the existing intelligent state recognition has the disadvantages of single model and low recognition accuracy. In order to overcome this shortcoming, a multi-dimensional information source transformer partial discharge fault identification method based on D-S evidence theory is proposed. Firstly, wavelet packet decomposition is used to extract the energy features of high frequency partial discharge signal and ultrasonic signal. Then, according to the selected feature set, the convolutional neural network (CNN) model and CNN-SVM model are established respectively. Finally, the Dempster-Shafer(D-S) evidence theory is used to combine the output results of the two signal recognition models. The results show that using the proposed wavelet packet decomposition energy feature set as input vector, the recognition rates of the two signals CNN-SVM models reach 95% and 81.67%, which are 3.33% and 8.34% higher than CNN respectively. The overall performance of D-S evidence theory fusion method is better than that of CNN and CNN-SVM, and the accuracy and consistency are improved by 3.33% and 16.66% respectively. The effectiveness and feasibility of this method are verified. Pattern recognition (psychology) ; Wavelet packet decomposition ; Artificial intelligence ; Computer science ; Support vector machine ; Partial discharge ; Convolutional neural network ; Transformer ; Wavelet ; Network packet ; Wavelet transform ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Jun Fu | Chao Chen | Rongqiang Zhao | Zhi Chen | Dan Li | Yongliang Qiao
Source: Frontiers in Plant Science (article, 2023-03-14)
Field: Chemistry — Analytical Chemistry

Frame vibration states identification for corn harvester based on joint improved empirical mode decomposition - Support vector machine method The frame of corn harvester is prone to vibration bending and torsional deformation due to the vibration caused by field road bumps and fluctuations. It poses a serious challenge to the reliability of machinery. Therefore it is critical to explore the vibration mechanism, and to identify the vibration states under different working conditions. To address the above problem, a vibration state identification method is proposed in this paper. An improved empirical mode decomposition (EMD) algorithm was used to decrease noise for signals of high noise and non-stationary vibration in the field. The support vector machine (SVM) model was used for identification of frame vibration states under different working conditions. The results showed that: (1) an improved EMD algorithm could effectively reduce noise interference and restore the effective information of the original signal. (2) based on improved EMD - SVM method identify the vibration states of the frame with the accuracy of 99.21%. (3) The corn ears in grain tank were not sensitive to low order vibration, but had an absorption effect on high order vibration. The proposed method has the potential to be applied for accurately identifying vibration state and improving frame safety. Frame (networking) ; Vibration ; Joint (building) ; Support vector machine ; Mode (computer interface) ; Hilbert–Huang transform ; Identification (biology) ; Decomposition ; Computer science ; Structural engineering ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Control theory (sociology) ; Engineering ; Acoustics ; Biology ; Energy (signal processing) ; Botany ; Statistics ; Physics ; Telecommunications Spectroscopy and Chemometric Analyses ; Advanced Algorithms and Applications ; Remote Sensing and Land Use