jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lerato L. Mokoloko | Boitumelo J. Matsoso | Nikolas Antonatos | Vlastimil Mazánek | Beatriz D. Moreno | Roy P. Forbes | Dean H. Barrett | Zdeněk Sofer | Neil J. Coville
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

From 0D to 2D: N-doped carbon nanosheets for detection of alcohol-based chemical vapours The application of N-doped carbon nanosheets, with and without embedded carbon dots, as active materials for the room temperature chemoresistive detection of methanol and/or ethanol is presented. The new carbons were made by converting 0D N-doped carbon dots (NCDs) to 2D nitrogen-doped carbon nanosheets by heat treatment (200-700 °C). The nanosheets exhibited a lateral size of ∼3 μm and a thickness of ∼12 nm at the highest annealing temperature. Both Raman and TEM analyses showed morphological transitions of the dots to the sheets, whilst XPS analysis revealed transformation of the N-bonding states with increasing temperature. PDF analysis confirmed the presence of defective carbon sheets. Room temperature screening of the chemical vapours of two alcohols (methanol and ethanol), revealed that the structure and the type of N-configuration influenced the detection of the chemical vapours. For instance, the lateral size of the nanosheets and the high charge density N-configurations promoted detection of both methanol and ethanol vapours at good sensitivity (-16.8 × 10<sup>-5</sup> ppm<sup>-1</sup> <sub>EtOH</sub> and 1.2 × 10<sup>-5</sup> ppm<sup>-1</sup> <sub>MeOH</sub>) and low LoD (∼44 ppm<sub>EtOH</sub> and ∼30.3 ppm<sub>MeOH</sub>) values. The study showed that the composite nature as well as the large basal area of the carbon nanosheets enabled generation of adequate defective sites that facilitated easy adsorption of the VOC analyte molecules, thereby eliminating the need to use conducting polymers or the formation of porous molecular frameworks for the alcohol detection. Vapours ; Alcohol ; Carbon fibers ; Doping ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Optoelectronics ; Psychology ; Psychiatry ; Composite material Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Gas Sensing Nanomaterials and Sensors
Authors: Jules Stouten | Nick Sijstermans | Joanna Babilotte | Andrij Pich | Lorenzo Moroni | Katrien V. Bernaerts
Source: Polymer Chemistry (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Micellar drug delivery vehicles formed from amphiphilic block copolymers bearing photo-cross-linkable cyclopentenone side groups UV core cross-linkable amphiphilic block copolymers containing cyclopentenone side groups on the hydrophobic backbone were synthesized and drug delivery experiments were done with the cancer therapeutic drug Doxorubicin. Amphiphile ; Copolymer ; Side chain ; Cyclopentenone ; Drug delivery ; Micelle ; Chemistry ; Drug ; Combinatorial chemistry ; Polymer chemistry ; Materials science ; Organic chemistry ; Polymer ; Pharmacology ; Medicine ; Aqueous solution Advanced Polymer Synthesis and Characterization ; Chemical Synthesis and Analysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Özgür Vatan
Source: Toxics (article, 2022-08-24)
Field: Materials Science — Materials Chemistry

Evaluation of In Vitro Cytotoxic, Genotoxic, Apoptotic, and Cell Cycle Arrest Potential of Iron–Nickel Alloy Nanoparticles The use of iron-nickel alloy nanoparticles (Fe-Ni ANPs) is increasing daily in various fields. People are increasingly exposed to these nanoparticles for occupational and environmental reasons. Our study determined some of the effects of Fe-Ni ANP exposure and impacts on human health at the cellular level. The cytotoxic and genotoxic potentials of Fe-Ni ANPs were investigated by XTT, clonogenic, comet, and GammaH2AX analyses using Beas-2B cells. Annexin V, multicaspase, and cell cycle arrest methods were used to understand the apoptotic mechanism of action. The intracellular ROS method was used to determine the primary mechanism that leads to cytotoxic and genotoxic activity. The Fe-Ni ANPs showed cytotoxic activity with the XTT and clonogenic methods: they had genotoxic potential, as demonstrated via genotoxicity methods. It was determined that the cytotoxic effect was realized by the caspase-dependent apoptotic pathway, and the cells were stopped at the G0/G1 stage by Fe-Ni ANPs. Increased intracellular ROS due to Fe-Ni ANPs led to cytotoxic, genotoxic, and apoptotic activity. Potential risks to human health due to Fe-Ni ANPs were then demonstrated at the cellular level. Cytotoxic T cell ; Genotoxicity ; Clonogenic assay ; Apoptosis ; Comet assay ; Chemistry ; Intracellular ; Annexin ; Cell cycle checkpoint ; In vitro ; Cell cycle ; DNA damage ; Biochemistry ; Toxicity ; DNA Nanoparticles: synthesis and applications ; Heavy Metal Exposure and Toxicity ; Environmental remediation with nanomaterials
Authors: Sherif S. Z. Hindi | Uthman M. Dawoud | Khalid A. Asiry
Source: Polymers (article, 2022-12-28)
Field: Materials Science — Biomaterials

Bioplastic Floss of a Novel Microwave-Thermospun Shellac: Synthesis and Bleaching for Some Dental Applications In this paper, crude flakes (CFs) of shellac were converted into purified, nonwoven, thermospun fibers (shellac floss) using two devices, namely, an electric thermospinner (ETS) and a microwave thermospinner (MTS). This conversion was achieved by the action of heating and the centrifugal forces that arose toward the outside of the spinner-head cavity. The dissolved MTS floss was bleached using hydrogen peroxide to produce the bleached MTS floss. The unbleached shellac (CFs, ETS floss, and MTS floss) and the bleached MTS floss were characterized physically and chemically. There was no deterioration in the floss properties due to the heating tools or bleaching process. For the unbleached shellac, although there were no statistical differences in properties among the three shellac types (CFs, ETS floss, and MTS floss), except for insolubility in hot alcohol, acid value, and moisture content, the MTS floss exhibited superior values compared with the other types for nearly all the properties studied. Bleaching the MTS floss produced the greatest color change among other studies, caused a high reduction in insoluble solid matter due to increasing the solubility of some of the solid constituents of shellac, and slightly decreased its Young's modulus (<i>E</i>). The important dental applications were surveyed and it was suggested that the suitability was enhanced by using the bleached MTS floss, based on its superior whiteness, along with the unique properties detected. Shellac ; Materials science ; Composite material ; Chemistry ; Pulp and paper industry Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Material Properties and Processing
Authors: Ahmed M. Abed | Laila F. Seddek | Samia Elattar
Source: Processes (article, 2022-12-14)
Field: Materials Science — Materials Chemistry

Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique Scholars seek to recycle wasted energy to produce electricity by integrating thermoelectric generators (TEGs) with internal combustion engines (ICE), which rely on the electrical conductivity, β, of the thermal conductor strips. The TEG legs are alloyed from iron, aluminum and copper in a strip shape with specific characteristics that guarantee maximum thermo-electric transformation, which has fluctuated between a uniform, Gaussian, and exponential distribution according to the structure of the alloy. The ICE exhaust and intake gates were chosen as the TEG sides. The digital simulator twin model checks the integration efficiency through two sequential stages, beginning with recording the causes of thermal conductivity failure via filming and extracting their data by neural network procedures in the feed of the second stage, which reveal that the cracks are a major obstacle in reducing the TEG-generated power. Therefore, the interest of the second stage is predicting the cracks’ positions, Pi,j, and their intensity, QP, based on the ant colony algorithm which recruits imaging data (STTF-NN-ACO) to install the thermal conductors far away from the cracks’ positions. The proposed metaheuristic (STTF-NN-ACO) verification shows superiority in the prediction over [Mat-ACO] by 8.2% and boosts the TEGs’ efficiency by 32.21%. Moreover, increasing the total generated power by 12.15% and working hours of TEG by 20.39%, reflects reduced fuel consumption by up to 19.63%. Computer science ; Automotive engineering ; Thermoelectric generator ; Artificial neural network ; Fuel efficiency ; Materials science ; Mechanical engineering ; Environmental science ; Simulation ; Thermoelectric effect ; Artificial intelligence ; Engineering Advanced Thermoelectric Materials and Devices ; Advanced Battery Technologies Research ; Sustainable Supply Chain Management
Authors: Shuai Yuan | Lijun Zhou | Tiandong Chen | Dongyang Wang | Lujia Wang
Source: e-Polymers (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Thermally conductive h-BN/EHTPB/epoxy composites with enhanced toughness for on-board traction transformers Abstract The dry-type on-board traction transformer (DOBTT) has the characteristics of huge heat generation and high heat dissipation requirements, so it has higher requirements for heat dissipation performance of epoxy resin (EP) insulation. However, the toughness of the existing high thermal conductivity EP composite after being modified by inorganic particles is greatly reduced, and it is very easy to crack under the occasion of frequent vibration such as electric multiple units or electric locomotives, so it cannot be directly applied to the DOBTT. In this article, the composite using h-BN to improve the thermal conductivity, and epoxidized hydroxy-terminated polybutadiene (EHTPB) liquid rubber to improve the toughness was prepared. After characterization and testing, it was found that when the EHTPB content was between 10 and 15 phr, the elongation at break of the EP/h-BN/EHTPB composite could be increased by 47.9% and the impact strength could be increased by 47.8% compared with that without EHTPB. The thermal and electrical performances were still satisfactory, which has a potential in application of on-board electrical equipment. Materials science ; Composite material ; Epoxy ; Composite number ; Polybutadiene ; Toughness ; Thermal conductivity ; Natural rubber ; Electrical conductor ; Transformer ; Polymer ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Epoxy Resin Curing Processes
Authors: Yi Pan | Juan Mo | Rui Liu | Baozhen Fan | Kui Xiao | Jin Gao | Haifei Zhou
Source: Applied Sciences (article, 2022-02-15)
Field: Materials Science — Materials Chemistry

Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity In this paper, the effects of mixture fillers on the thermal conductivity of a coating are studied, and the optimal addition proportion was obtained. Then, the corrosion behavior of the modified waterborne polyurethane coating with high thermal conductivity is investigated by salt spray experiments, pull-off tests, EIS and SKP measurements. The result shows that the coating with 2 wt % composite fillers exhibits the most excellent thermal conductivity (1.222 W·m−1·K−1). In terms of corrosion resistance, there is an absence of obvious corrosion phenomenon for the modified coating, and the polarization resistance is still as high as 1.31 × 107 Ω·cm2 at the final stage, which indicates that the incorporation of 2 wt % composite fillers also markedly enhances the corrosion resistance property of waterborne polyurethane coating. This is mainly attributed to the more compact structure and more excellent shielding action to the electrolyte. The surface Kelvin potential results further confirm the advantageous effects of mixture fillers on the corrosion protective performance. The corresponding protective mechanism of the composite coating is also proposed. Materials science ; Coating ; Corrosion ; Polyurethane ; Composite material ; Thermal conductivity ; Composite number ; Polarization (electrochemistry) ; Conductivity ; Chemistry Thermal properties of materials ; Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials
Authors: T. Boczar | S. Borucki | Daniel Jancarczyk | Marcin Bernaś | P. Kurtasz
Source: Energies (article, 2022-07-08)
Field: Materials Science — Materials Chemistry

Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established. Support vector machine ; Naive Bayes classifier ; Acoustic emission ; Partial discharge ; Artificial intelligence ; Random forest ; Pattern recognition (psychology) ; Artificial neural network ; Probabilistic neural network ; Extreme learning machine ; Computer science ; Classifier (UML) ; Machine learning ; Identification (biology) ; Statistical classification ; Engineering ; Voltage ; Acoustics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Water Systems and Optimization
Authors: Steven Dajnowicz | Garvit Agarwal | James Stevenson | Leif D. Jacobson | Farhad Ramezanghorbani | Karl Leswing | Richard A. Friesner | Mathew D. Halls | Robert Abel
Source: ChemRxiv (preprint, 2022-05-19)
Field: Materials Science — Materials Chemistry

High-dimensional neural network potential for liquid electrolyte simulations Liquid electrolytes are the most common electrolyte classes used in Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (viscosity, ionic diffusivity) from first principles necessary to sup-port improved designs. In this work, we report a machine learning-based force field for liquid electrolyte simulations which bridges the gap between the accuracy of range-separated hybrid density functional theory and the efficiency of classical force fields. Predictions of material properties made with this force field are quantitatively accurate compared to experimental data. Our model uses the QRNN deep neural network architecture, which includes both long-range interactions and global charge equilibration. The training dataset is composed solely of non-periodic DFT, allowing the practical use of an accurate theory (here, ωB97X-D3BJ/def2-TZVPD) which would be prohibitively expensive for generating large datasets with periodic DFT. In this report we focus on seven common carbonates and LiPF6, but this methodology has very few assumptions and can be readily applied to any liquid electrolyte system. This provides a promising path forward for large scale atomistic modeling of many important battery chemistries. Electrolyte ; Computer science ; Field (mathematics) ; Battery (electricity) ; Work (physics) ; Artificial neural network ; Range (aeronautics) ; Statistical physics ; Density functional theory ; Materials science ; Chemistry ; Artificial intelligence ; Physics ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Mathematics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Lin Shen | Luyang Jia | Diandong Tang | Zhan‐Yun Zhang | Ding Peng
Source: Scientia Sinica Chimica (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Machine learning in computational chemistry <p indent="0mm">Machine learning (ML) is becoming a powerful tool in many domains in science. Its development on computational chemistry is still at an early stage but has dramatically and simultaneously improved the accuracy and efficiency of existing computational approaches in theoretical chemistry. Here, we summarized several typical and recent applications of machine learning in three aspects of computational chemistry: calculation on potential energy surface, molecular dynamics simulation, and new material discovery within the framework of the material genome initiative. Computer science ; Chemistry ; Cognitive science ; Data science ; Artificial intelligence ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: G. Anand | Swarnava Ghosh | Liwei Zhang | Angesh Anupam | Colin L. Freeman | Christoph Ortner | Markus Eisenbach | James R. Kermode
Source: Journal of The Institution of Engineers (India) Series D (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

Exploiting Machine Learning in Multiscale Modelling of Materials Machine learning ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Gaurav Arora | Serveh Kamrava | Pejman Tahmasebi | Dilpuneet S. Aidhy
Source: Materialia (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys Materials science ; Convolutional neural network ; Stacking ; Stacking fault ; Charge (physics) ; Artificial neural network ; Charge density ; Stacking-fault energy ; Energy (signal processing) ; Engineering physics ; Artificial intelligence ; Dislocation ; Computer science ; Composite material ; Nuclear magnetic resonance Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Lingli Kong | Zhengran Ji | Huolin L. Xin
Source: Scientific Reports (article, 2022-12-23)
Field: Materials Science — Materials Chemistry

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal-noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy. Computer science ; Artificial intelligence ; Convolutional neural network ; Artificial neural network ; Enhanced Data Rates for GSM Evolution ; Pattern recognition (psychology) ; Spectral line ; Electron energy loss spectroscopy ; Automation ; Noise (video) ; Biological system ; Materials science ; Physics ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrochemical Analysis and Applications
Authors: Sina Stocker | Johannes Gasteiger | Florian Becker | Stephan Günnemann | Johannes T. Margraf
Source: ChemRxiv (preprint, 2022-04-01)
Field: Materials Science — Materials Chemistry

How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations? Graph neural networks (GNNs) have emerged as a powerful machine learning approach for the prediction of molecular properties. In particular, recently proposed advanced GNN models promise quantum chemical accuracy at a fraction of the computational cost. While the capabilities of such advanced GNNs have been extensively demonstrated on benchmark datasets, there have been few applications in real atomistic simulations. Here, we therefore put the robustness of GNN interatomic potentials to the test, using the recently proposed GemNet architecture as a testbed. Models are trained on the QM7-x database of organic molecules and used to perform extensive MD simulations. We find that low test set errors are not sufficient for obtaining stable dynamics and that severe pathologies sometimes only become apparent after hundreds of ps of dynamics. Nonetheless, highly stable and transferable GemNet potentials can be obtained with sufficiently large training sets. Testbed ; Robustness (evolution) ; Computer science ; Molecular dynamics ; Benchmark (surveying) ; Quantum chemical ; Artificial neural network ; Graph ; Artificial intelligence ; Machine learning ; Training set ; Theoretical computer science ; Molecule ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics