jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ke-Bin Low | Jian Shi | Melissa Clough Mastry | Vasileios Komvokis | Bilge Yilmaz
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Semi-Quantitative Analysis of Iron Nodules in Equilibrium Refining Catalysts by Artificial Intelligence-Augmented Scanning Electron Microscopy An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Refining (metallurgy) ; Materials science ; Scanning electron microscope ; Catalysis ; Microscopy ; Metallurgy ; Chemical engineering ; Nanotechnology ; Chemistry ; Engineering ; Physics ; Optics ; Composite material ; Organic chemistry Catalytic Processes in Materials Science ; Minerals Flotation and Separation Techniques ; Iron oxide chemistry and applications
Authors: Mohammad Abdullah Al Shohel | Vidya A. Chhabria | Sachin S. Sapatnekar
Source: arXiv (Cornell University) (preprint, 2021-05-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A New, Computationally Efficient "Blech Criterion" for Immortality in General Interconnects Traditional methodologies for analyzing electromigration (EM) in VLSI circuits first filter immortal wires using Blech's criterion, and then perform detailed EM analysis on the remaining wires. However, Blech's criterion was designed for two-terminal wires and does not extend to general structures. This paper demonstrates a first-principles-based solution technique for determining the steady-state stress at all the nodes of a general interconnect structure, and develops an immortality test whose complexity is linear in the number of edges of an interconnect structure. The proposed model is applied to a variety of structures. The method is shown to match well with results from numerical solvers, to be scalable to large structures. Electromigration ; Interconnection ; Immortality ; Very-large-scale integration ; Scalability ; Computer science ; Distortion (music) ; Topology (electrical circuits) ; Algorithm ; Electronic engineering ; Engineering ; Electrical engineering Copper Interconnects and Reliability ; Electronic Packaging and Soldering Technologies ; 3D IC and TSV technologies
Authors: محمد تقان ساسانپور | محسن شریف‌زاده | علی طاهری | مجتبی عسکری
Source: Majallah-i ̒ulūm va funūn-i hastah/ī (article, 2021-04-30)
Field: Materials Science — Materials Chemistry

فاصلهسنجی پسپراکنشی صفحههای فولادی با چشمههای Cs137 و Co60 فاصله‌­سنجی به روش پس ­پراکنش کامپتون روشی نوین برای اندازه‌­گیری فاصله بین صفحات فلزی است که می‌­تواند کاربردهای زیادی در صنایع مختلف از جمله صنعت نفت و گاز و پتروشیمی داشته باشد. در این مقاله به ­منظور فاصله‌­سنجی از ورق‌های فولادی با ضخامت‌های 2، 6 و mm 10 به­ عنوان ضخامت‌های کم، متوسط و زیاد استفاده شد. هم‌­چنین با هدف نشان دادن تأثیر انرژی چشمه بر فاصله‌سنجی، دو چشمه Cs137 و Co60 استفاده شده­‌اند. نتایج نشان داد که فاصله‌سنجی به انرژی چشمه گاما وابسته است و برای دقت اندازه‌گیری یکسان، چشمه گاما با انرژی بالاتر قابلیت بیش‌­تری برای اندازه‌گیری فاصله بین دو صفحه فولادی دارد. افزایش انرژی چشمه، دقت اندازه‌گیری را نیز افزایش می‌دهد. علاوه بر انرژی چشمه گاما، فاصله‌سنجی به ضخامت دو صفحه و ترتیب قرارگیری صفحه ضخیم­‌تر نسبت به صفحه نازک‌­تر نیز بستگی دارد. هم‌­چنین با افزایش فاصله بین دو صفحه فلزی، دقت اندازه‌گیری نیز کاهش می‌یابد. به همین دلیل بیشینه فاصله‌سنجی بین دو صفحه، با در نظر گرفتن دقت اندازه‌گیری، به مقدار cm 1 درنظر گرفته شده است. Radiochemistry ; Chemistry Radiation Shielding Materials Analysis ; Radioactivity and Radon Measurements ; Radiation Detection and Scintillator Technologies
Authors: Caleb Cranney | Jesse G. Meyer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-05-13)
Field: Chemistry — Spectroscopy

CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis (DISPA) ABSTRACT New mass spectrometry data collection methods require new computational tools. Direct Infusion Shotgun Proteome Analysis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here we introduce CsoDIAq, a user-friendly software package for the identification and quantification of peptides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graphical user interface, CsoDIAq introduces algorithmic concepts to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity, and a new spectrum-spectrum match score called match count and cosine (MaCC), which improves target discrimination in a target-decoy analysis. We further show that reanalysis after fragment mass tolerance correction increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC-MS DIA, and show that it outperforms other spectrum-spectrum matching software. Workflow ; Shotgun proteomics ; Computer science ; Software ; Shotgun ; Proteome ; Identification (biology) ; Mascot ; Data mining ; Database search engine ; Pooling ; Graphical user interface ; Fragment (logic) ; Interface (matter) ; Mass spectrometry ; Matching (statistics) ; Proteomics ; Artificial intelligence ; Algorithm ; Database ; Bioinformatics ; Operating system ; Search engine ; Information retrieval ; Chemistry ; Mathematics ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Hironobu KAWAGUCHI | Mariko I. Ito | Takeshi Aoyagi | Takaaki Ohnishi
Source: Journal of Computer Chemistry Japan (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Stress-Strain Curve of Block Copolymers Using Transfer Learning of 3D Convolutional Neural Network. The simulation to obtain stress-strain curves of block copolymers requires large computational resources. As an alternative to simulation, a method for high-throughput prediction of stress-strain curves using a three-dimensional convolutional neural network has been reported. In this study, we incorporated shortcut coupling into the neural network and performed pre-training and transfer learning in a step-by-step manner to successfully predict the stress-strain curve with high accuracy while reducing the training cost. Transfer of learning ; Convolutional neural network ; Computer science ; Stress (linguistics) ; Stress–strain curve ; Artificial neural network ; Block (permutation group theory) ; Throughput ; Transfer (computing) ; Artificial intelligence ; Copolymer ; Coupling (piping) ; Strain (injury) ; Algorithm ; Materials science ; Mathematics ; Structural engineering ; Finite element method ; Engineering ; Parallel computing ; Composite material ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Polymer Nanocomposites and Properties
Authors: Qinjuan Luo | Wang Lian-ming | Zhao Xiaoqing | Hua Qian | Yan Lei
Source: International Journal of Circuits Systems and Signal Processing (article, 2021-08-26)
Field: Chemistry — Analytical Chemistry

Research on Detection Method of Pea Seed Vigor based on Hyperspectral Imaging Technology Rapid and noninvasive detection methods of seed vigor, an important index to evaluate seed quality, have been the research focus in recent years. In this paper, the detection method of pea seed vigor based on hyperspectral imaging technology was researched. First, the spectral images of different vigor grade samples with artificial aging were captured, and the original spectrum was pretreated with multiple scattering correction. Secondly, SPA and PCA were used to select respective bands. Finally, PLS-DA and LS-SVM model were established to identify the seed vigor of the pea seed, based on the whole band spectrum, the characteristic bands extracted by SPA and PCA respectively. The results showed that PLS-DA and LS-SVM models are effective, but LS-SVM had better performance. Through comparison, the method using full band spectrum was more accurate, the efficiency of method using 5 characteristic bands extracted by PCA was the highest while the way of extracting the representative band by SPA was the most meaningful to this study which achieved similar accuracy to the full band with only 20 bands. The SPA-LS-SVM method afforded the recognition accuracy (100%) for modeling set and validation set used to determine the vigor of pea seeds. The overall results suggest that hyperspectral imaging technology is useful for classification of different vitality pea seeds with non-destructive manner, which can provide a basis for further development of online scoring devices Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Set (abstract data type) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Seed and Plant Biochemistry
Authors: Soroor Hediyeh-zadeh | Jarryd Martin | Melissa J. Davis | Andrew I. Webb
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-07)
Field: Chemistry — Spectroscopy

PIPP: Improving peptide identity propagation using neural networks Abstract Peptide identity propagation (PIP) can substantially reduce missing values in label-free mass spectrometry quantification by transferring peptides identified by tandem mass (MS/MS) spectra in one run to experimentally related runs where the peptides are not identified by MS/MS. The existing frameworks for matching identifications between runs perform peak tracing and propagation based on similarity of precursor features using only a limited number of dimensions available in MS1 data. These approaches do not produce accompanying confidence estimates and hence cannot filter probable false positive identity transfers. We introduce an embedding based PIP that uses a higher dimensional representation of MS1 measurements that is optimized to capture peptide identities using deep neural networks. We developed a propagation framework that works entirely on MaxQuant results. Current PIP workflows typically perform propagation mainly using two feature dimensions, and rely on deterministic tolerances for identification transfer. Our framework overcomes both these limitations while additionally assigning probabilities to each transferred identity. The proposed embedding approach enables quantification of the empirical false discovery rate (FDR) for peptide identification, while also increasing depth of coverage through coembedding the runs from the experiment with experimental libraries. In published datasets with technical and biological variability, we demonstrate that our method reduces missing values in MaxQuant results, maintains high quantification precision and accuracy, and low false transfer rate. Computer science ; Identity (music) ; Identification (biology) ; Matching (statistics) ; Embedding ; Filter (signal processing) ; Representation (politics) ; Feature (linguistics) ; Workflow ; Similarity (geometry) ; Artificial neural network ; Data mining ; False discovery rate ; Tracing ; Pattern recognition (psychology) ; Artificial intelligence ; Algorithm ; Chemistry ; Mathematics ; Biology ; Physics ; Statistics ; Image (mathematics) ; Computer vision Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yisheng Chai | Dashan Shang | SaeHwan Chun | Young Sun | KeeHoon Kim
Source: Physical Review Applied (preprint, 2021-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Charge-Driven Transtive Devices via Electric Field Control of Magnetism in a Helimagnet The transtor and memtranstor are the fourth basic linear and memory elements, respectively, which allow direct coupling of charge (q) to magnetic flux (\ensuremath{\varphi}) via linear and nonlinear magnetoelectric (ME) effects, respectively. It is found here that a large variation of magnetization by an electric field is realized in both linear and nonlinear hysteretic styles in a magnetoelectric Y-type hexaferrite ${\mathrm{Ba}}_{0.5}{\mathrm{Sr}}_{1.5}{\mathrm{Zn}}_{2}({\mathrm{Fe}}_{0.92}{\mathrm{Al}}_{0.08}{)}_{12}{\mathrm{O}}_{22}$ single crystal. Moreover, based on the spin-current model, the underlying microscopic mechanisms for generating the two types of linear and nonlinear M versus E curves are understood to be E-induced changes to the cone angle and sign of P, respectively, establishing the charge-driven transtor and memtranstor in the Y-type hexaferrite system. This work points to a promising pathway to develop alternative circuit functionalities using magnetoelectric materials. Magnetism ; Magnetization ; Charge (physics) ; Nonlinear system ; Condensed matter physics ; Electric field ; Coupling (piping) ; Magnetic field ; Magnetic flux ; Work (physics) ; Physics ; Field (mathematics) ; Sign (mathematics) ; Materials science ; Mathematics ; Quantum mechanics ; Mathematical analysis Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Advanced Condensed Matter Physics
Authors: Ömer Nedim Kenger | Eren Özceylan
Source: Journal of Computer Science (article, 2021-04-01)
Field: Chemistry — Analytical Chemistry

Application of Data Mining Classifiers on Sunflower Edible Oil Bleaching Process: A Comprehensive Comparative Analysis Sunflower oil is widely used as edible oil. It is commonly extracted by solvent extraction method from the sunflower seed. After extraction, crude sunflower oil is obtained. Crude sunflower oil has some undesirable impurities and dark colors. These impurities and dark colors require removal. The bleaching process is applied to remove the color. The bleaching earth is used in the refining and removes color. The specifications of crude sunflower oil such as impurity, free fatty acid ratio, wax, color index and the temperature of the process, the vacuum of the process, the amount of bleaching earth used affect the bleaching output color value. In this study, machine learning algorithms are used to predict the bleaching output color. In order to predict, Waikato Environment for Knowledge Analysis (WEKA), an open-source Data Mining workbench is run. 15 well-known machine learning classifier algorithms, suitable for our data such as k-nearest neighbors, multilayer perceptron and random forest are performed. Each algorithm is tested on a real dataset by a 10-fold crossvalidation method. The correlation coefficient, mean absolute error and root mean squared error is calculated for each algorithm and benchmarked. Results show that Random Forest Classifier is the most effective classifier for our data. Additionally, Wilcoxon Signed-Rank statistical test is conducted whether Random Forest Classifier is the most effective classifier for some k-fold cross validation. Artificial intelligence ; Computer science ; Random forest ; Sunflower oil ; Sunflower ; Machine learning ; Correlation coefficient ; Classifier (UML) ; Perceptron ; Pattern recognition (psychology) ; Mathematics ; Artificial neural network ; Chemistry Spectroscopy and Chemometric Analyses
Authors: R. del Cuvillo | J.A. Artero-Guerrero | J. Pernas-Sánchez | Jorge López Puente
Source: EPJ Web of Conferences (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Soft projectile impact forces measurement using Hopkinson bars: application to ice, artificial bird and rubber This work presents an experimental campaign of impacts of soft projectiles to measure the induced force during the impact. Three different materials acting as soft impactors that could strike against a aeronautical structural component: ice, artificial bird and rubber have been impacted at several velocities against an aluminium Hopkinson bar. This device has been instrumented with semiconductor strain gauges that allow to obtain the induced compression strain. Additionally, all the impacts were recorded using high-speed video cameras, allowing the kinematic analysis of the projectile during the impact. After the results study, it has been concluded that there is a linear dependency between the kinetic energy and the peak force for all three materials. Added to that, it has been proved that the higher peak force corresponds to ice, despite the kinetic energy, followed by rubber and finally the artificial bird. In addition, while ice and artificial bird projectiles get radially dispersed after the impact, rubber spheres rebound due to its different behaviour. The obtained data is of great interest to design structures which could be subjected to impacts of soft materials such as aeronautic structures Projectile ; Split-Hopkinson pressure bar ; Natural rubber ; Kinetic energy ; Strain gauge ; Materials science ; Compression (physics) ; Kinematics ; Impact ; Structural engineering ; Mechanics ; Strain rate ; Composite material ; Engineering ; Physics High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Transportation Safety and Impact Analysis
Authors: J. Doyne Sartor | Eric I. Corwin
Source: arXiv (Cornell University) (preprint, 2021-08-20)
Field: Materials Science — Materials Chemistry

Predicting Defects in Soft Sphere Packings Near Jamming Using The Force Network Ensemble Amorphous systems of soft particles above jamming have more contacts than are needed to achieve mechanical equilibrium. The force network of a granular system with a fixed contact network is thus underdetermined, and can be characterized as a random instantiation within the space of the force network ensemble. In this work, we show that by examining the boundaries of this space of allowed force networks, defect contacts which are not necessary for stability of the system can be uniquely identified. We further show that in the near jamming limit, this identification is nearly always correct and the defect contacts are broken under decompression of the system. Jamming ; Statistical physics ; Limit (mathematics) ; Computer science ; Classical mechanics ; Physics ; Mathematics ; Mathematical analysis ; Condensed matter physics Material Dynamics and Properties ; Pickering emulsions and particle stabilization ; Sports Dynamics and Biomechanics
Authors: Jingchuan Xue | Rico J. E. Derks | William Wwebb | Aries Aisporna | Martin Giera | Gary Siuzdak
Source: ChemRxiv (preprint, 2021-02-25)
Field: Chemistry — Spectroscopy

EISA-MRM: Multiple Reaction Monitoring with Single Quadrupole Mass Spectrometry Enhanced in-source fragmentation annotation combined with single quadrupole multiple reaction monitoring (EISA-MRM) has been designed for quantitative mass spectrometry analysis. EISA contrasts to traditional electrospray as a soft ionization technology and is proving to be advantageous since the resulting fragment ions are identical as those generated in tandem mass spectrometry. Criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis was used for the EISA-MRM quantitative analyses and experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for quantitative analysis was comparable to QqQ-MRM analyses at up to 5 orders of magnitude and the EISA-MRM and QqQ-MRM of the cell and plasma extracts showed similar matrix effects. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labelled standards demonstrated EISA-MRM accuracy in the range of 91-110% for the amino acids, 76-129% for the fatty acids, and precision with a CV < 10%. In order to enhance specificity and sensitivity, a newly developed Correlated SIM Chromatogram (CSC) algorithm was designed to facilitate MRM quality analyses. The EISA-MRM quantitative analysis with CSC informatics enables both precursor and in-source fragment ions to be correlated within a single quadrupole mass spectrometer. Chemistry ; Selected reaction monitoring ; Triple quadrupole mass spectrometer ; Mass spectrometry ; Chromatography ; Fragmentation (computing) ; Tandem mass spectrometry ; Analytical Chemistry (journal) ; Electrospray ionization ; Computer science Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Yisheng Chai | Dashan Shang | SaeHwan Chun | Young Sun | KeeHoon Kim
Source: arXiv (Cornell University) (preprint, 2021-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Charge-driven transtive devices via electric field control of magnetism\n in a helimagnet Transtor and memtranstor are the fourth basic linear and memory elements,\nwhich allows direct coupling of charge (q) to magnetic flux ({\\phi}) via linear\nand non-linear ME effects, respectively. It is found here that large variation\nof magnetization by electric field is realized in both linear and nonlinear\nhysteretic styles in a magnetoelectric Y-type hexaferrite\nBa0.5Sr1.5Zn2(Fe0.92Al0.08)12O22 single-crystal. Moreover, based on the spin\ncurrent model, the underlying microscopic mechanisms for generating the two\ntypes of linear and nonlinear M vs E curves are understood as E induced changes\nof cone angle and sign of P respectively, establishing the charge-driven\ntranstor and memtranstor in the Y-type hexaferrite system. This work points to\na promising pathway to develop unique circuit functionalities using the\nmagnetoelectric materials.\n Magnetization ; Magnetism ; Nonlinear system ; Charge (physics) ; Electric field ; Condensed matter physics ; Coupling (piping) ; Magnetic field ; Work (physics) ; Magnetic flux ; Field (mathematics) ; Sign (mathematics) ; Physics ; Type (biology) ; Materials science ; Mathematics ; Mathematical analysis ; Quantum mechanics Multiferroics and related materials ; Magnetic Properties and Synthesis of Ferrites ; Magnetic properties of thin films
Authors: Fernando Jiménez‐Grávalos | Dimas Suárez
Source: ChemRxiv (preprint, 2021-03-16)
Field: Chemistry — Physical and Theoretical Chemistry

A Quantum Chemical Topology Picture of Intermolecular Electrostatic Interactions and Charge Penetration Energy &lt;div&gt;Basing on the Interacting Quantum Atoms approach, we present herein a conceptual and theoretical framework of short-range electrostatic interactions, whose accurate description is still a challenging problem in molecular modeling. For all the non-covalent complexes in the S66 database, the fragment-based and atomic decomposition of the electrostatic binding energies is performed using both the charge density of the dimers and the unrelaxed densities of the monomers. This energy decomposition together with dispersion corrections gives rise to a pairwise approximation to the total binding energy. It also provides energetic descriptors at varying distance that directly address the atomic and molecular electrostatic interactions as described by point-charge or multipole-based potentials. Additionally, we propose a consistent definition of the charge penetration energy within quantum chemical topology, which is mainly characterized in terms of the intramolecular electrostatic energy. Finally, we discuss some practical implications of our results for the design and validation of electrostatic potentials.&lt;/div&gt; Electrostatics ; Multipole expansion ; Electric potential energy ; Intermolecular force ; Intramolecular force ; Chemical physics ; Quantum ; Interaction energy ; Atoms in molecules ; Physics ; Topology (electrical circuits) ; Quantum chemistry ; Binding energy ; Chemistry ; Computational chemistry ; Atomic physics ; Molecule ; Quantum mechanics ; Energy (signal processing) Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure