jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kirsten Bobzin | W. Wietheger | E. Burbaum | Tim Hosenfeldt | Nazlim Bagcivan | Mehmet Öte | Björn Müller | Carsten Kunde | Anna-Lena Elsner
Source: Journal of Thermal Spray Technology (article, 2022-04-22)
Field: Materials Science — Ceramics and Composites

Comparison of Ceramic Insulation Coatings via Impedance Spectroscopy Abstract Alumina is often used for electrical insulation. However, different material systems promise to increase the insulation due to their material characteristics. Because of the process properties including high cooling rates, thermally sprayed coatings generally differ from sintered material, which also effect the electrical properties. Within this study, different thermally sprayed coatings are analyzed via impedance spectroscopy to evaluate the capacitive and the electrical insulation behavior. Besides comparing the frequency-dependent resistance, equivalent circuit diagrams were used to calculate the relative permittivity of the coatings. X-ray diffractograms reveal the phase stability of the coatings during thermal spraying. X-ray diffraction was additionally conducted to classify the systems and the respective effects. In particular, the investigated mullite-based coatings exhibit slightly increased impedance values compared to conventionally used alumina-based coating systems. Materials science ; Mullite ; Dielectric spectroscopy ; Composite material ; Coating ; Ceramic ; Electrical impedance ; Capacitive sensing ; Permittivity ; Relative permittivity ; Dielectric ; Optoelectronics ; Electrical engineering ; Electrochemistry Advanced ceramic materials synthesis ; High-Temperature Coating Behaviors ; Semiconductor materials and devices
Authors: Jassem Abbasi | Pål Østebø Andersen
Source: arXiv (Cornell University) (preprint, 2022-05-29)
Field: Materials Science — Materials Chemistry

Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs) In recent years, the gap between Deep Learning (DL) methods and analytical or numerical approaches in scientific computing is tried to be filled by the evolution of Physics-Informed Neural Networks (PINNs). However, still, there are many complications in the training of PINNs and optimal interleaving of physical models. Here, we introduced the concept of Physical Activation Functions (PAFs). This concept offers that instead of using general activation functions (AFs) such as ReLU, tanh, and sigmoid for all the neurons, one can use generic AFs that their mathematical expression is inherited from the physical laws of the investigating phenomena. The formula of PAFs may be inspired by the terms in the analytical solution of the problem. We showed that the PAFs can be inspired by any mathematical formula related to the investigating phenomena such as the initial or boundary conditions of the PDE system. We validated the advantages of PAFs for several PDEs including the harmonic oscillations, Burgers, Advection-Convection equation, and the heterogeneous diffusion equations. The main advantage of PAFs was in the more efficient constraining and interleaving of PINNs with the investigating physical phenomena and their underlying mathematical models. This added constraint significantly improved the predictions of PINNs for the testing data that was out-of-training distribution. Furthermore, the application of PAFs reduced the size of the PINNs up to 75% in different cases. Also, the value of loss terms was reduced by 1 to 2 orders of magnitude in some cases which is noteworthy for upgrading the training of the PINNs. The iterations required for finding the optimum values were also significantly reduced. It is concluded that using the PAFs helps in generating PINNs with less complexity and much more validity for longer ranges of prediction. Artificial neural network ; Physical system ; Physical law ; Applied mathematics ; Interleaving ; Boundary value problem ; Computer science ; Mathematical optimization ; Physics ; Statistical physics ; Mathematics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Mohammadjavad Fazeli | Mehdi Escrochi | Zohreh Sadat Hosseini | Behzad Vaferi
Source: Scientific Reports (article, 2022-03-10)
Field: Chemistry — Analytical Chemistry

Experimental analyzing the effect of n-heptane concentration and angular frequency on the viscoelastic behavior of crude oil containing asphaltene Asphaltene often produces problems in upstream and downstream sections of crude oil transportation and processing equipment. These issues are directly related to the asphaltene precipitation in transportation pipelines, separation columns, heat exchangers, and storage tanks. This research investigates the impact of angular frequency and n-heptane concentration on asphaltene precipitation and rheological behavior of two oil samples from the Mansouri oil field in Iran, i.e., 23 and 71. The viscosity tests revealed that these oil samples and their mixtures with n-heptane exhibit Newtonian behavior. Moreover, increasing the n-heptane concentration increases the asphaltene precipitation and dramatically decreases crude oil viscosity. The frequency tests revealed that the presence of n-heptane has an unfavorable effect on crude oil's viscoelastic behavior. Therefore, it is necessary to find the optimum range of angular frequency and n-heptane concentration to minimize the asphaltene content of crude oil and provide them with appropriate viscoelastic behavior. Increasing the angular frequency continuously increases all oil samples' loss modulus and strengthens their liquid-like manner. The experimental results confirmed that the angular frequency higher than 33.6 rad/s and 75% volume concentration of n-heptane is the best condition for the oil sample of 23. On the other hand, the angular frequency higher than 23.4 rad/s and 75% volume concentration of n-heptane is the best condition for the oil sample of 71. In these conditions, the oil samples of 23 and 71 not only have appropriate viscoelastic behavior, but they also experience 97.2% and 96.3% reductions in their viscosity, respectively. Heptane ; Viscoelasticity ; Asphaltene ; Rheology ; Viscosity ; Crude oil ; Materials science ; Dynamic modulus ; Chemistry ; Petroleum engineering ; Dynamic mechanical analysis ; Composite material ; Organic chemistry ; Geology ; Polymer Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydraulic Fracturing and Reservoir Analysis
Authors: Dionysios Raptis | Charalampos Livas | George Stavroglou | Rafaela Maria Giappa | Emmanuel Tylianakis | Taxiarchis Stergiannakos | George E. Froudakis
Source: Molecules (article, 2022-05-26)
Field: Chemistry — Inorganic Chemistry

Surface Modification Strategy for Enhanced NO2 Capture in Metal–Organic Frameworks The interaction strength of nitrogen dioxide (NO<sub>2</sub>) with a set of 43 functionalized benzene molecules was investigated by performing density functional theory (DFT) calculations. The functional groups under study were strategically selected as potential modifications of the organic linker of existing metal-organic frameworks (MOFs) in order to enhance their uptake of NO<sub>2</sub> molecules. Among the functional groups considered, the highest interaction energy with NO<sub>2</sub> (5.4 kcal/mol) was found for phenyl hydrogen sulfate (-OSO<sub>3</sub>H) at the RI-DSD-BLYP/def2-TZVPP level of theory-an interaction almost three times larger than the corresponding binding energy for non-functionalized benzene (2.0 kcal/mol). The groups with the strongest NO<sub>2</sub> interactions (-OSO<sub>3</sub>H, -PO<sub>3</sub>H<sub>2</sub>, -OPO<sub>3</sub>H<sub>2</sub>) were selected for functionalizing the linker of IRMOF-8 and investigating the trend in their NO<sub>2</sub> uptake capacities with grand canonical Monte Carlo (GCMC) simulations at ambient temperature for a wide pressure range. The predicted isotherms show a profound enhancement of the NO<sub>2</sub> uptake with the introduction of the strongly-binding functional groups in the framework, rendering them promising modification candidates for improving the NO<sub>2</sub> uptake performance not only in MOFs but also in various other porous materials. Density functional theory ; Benzene ; Metal-organic framework ; Linker ; Molecule ; Chemistry ; Binding energy ; Hydrogen molecule ; Organic molecules ; Hydrogen ; Computational chemistry ; Physical chemistry ; Adsorption ; Organic chemistry ; Computer science ; Physics ; Atomic physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Radu Revnic | G. STIUFIUC | Valentin Toma | Anca Onaciu | Alin Moldovan | Adrian Bogdan Țigu | Eva Fischer‐Fodor | Romulus Tetean | E. Burzo | Rareş Ştiufiuc
Source: International Journal of Molecular Sciences (article, 2022-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Facile Microwave Assisted Synthesis of Silver Nanostars for Ultrasensitive Detection of Biological Analytes by SERS We report a very simple, rapid and reproducible method for the fabrication of anisotropic silver nanostars (AgNS) that can be successfully used as highly efficient SERS substrates for different bioanalytes, even in the case of a near-infra-red (NIR) excitation laser. The nanostars have been synthesized using the chemical reduction of Ag<sup>+</sup> ions by trisodium citrate. This is the first research reporting the synthesis of AgNS using only trisodium citrate as a reducing and stabilizing agent. The key elements of this original synthesis procedure are rapid hydrothermal synthesis of silver nanostars followed by a cooling down procedure by immersion in a water bath. The synthesis was performed in a sealed bottom flask homogenously heated and brought to a boil in a microwave oven. After 60 s, the colloidal solution was cooled down to room temperature by immersion in a water bath at 35 °C. The as-synthesized AgNS were washed by centrifugation and used for SERS analysis of test molecules (methylene blue) as well as biological analytes: pharmaceutical compounds with various Raman cross sections (doxorubicin, atenolol & metoprolol), cell lysates and amino acids (methionine & cysteine). UV-Vis absorption spectroscopy, (Scanning) Transmission Electron Microscopy ((S)TEM) and Atomic Force Microscopy (AFM) have been employed for investigating nanostars' physical properties. Trisodium citrate ; Raman spectroscopy ; Biomolecule ; Materials science ; Reducing agent ; Transmission electron microscopy ; Nanotechnology ; Nuclear chemistry ; Nanoparticle ; Chemistry ; Chemical engineering ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Zhe Chuan Feng | Weihao Li | Di Cui
Source: International journal of agricultural and biological engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Detection of endogenous foreign bodies in Chinese hickory nuts by hyperspectral spectral imaging at the pixel level It is difficult to differentiate small, but harmful, shell fragments of Chinese hickory nuts from their kernels since they are very similar in color. Including shell fragments of Chinese hickory nuts by mistake may create safety hazards for consumers. Therefore, there is a need to develop an effective method to differentiate the shells from the kernels of Chinese hickory nuts. In this study, a deep learning approach based on a two-dimensional convolutional neural network (2D CNN) and long short-term memory (LSTM) integrated with hyperspectral imaging for distinguishing the shells and kernels of Chinese hickory nuts at the pixel level was proposed. Two classical classification methods, principal component analysis-K-nearest neighbors (PCA-KNN) and the support vector machine (SVM), were employed to establish identification models for comparison. The results showed that the 2D CNN-LSTM model achieved the best performance with an overall classification accuracy of 99.0%. Moreover, the shells in mixtures of shells and kernels were detected based on the proposed deep learning method and visualized for subsequent operations for the removal of foreign bodies. Keywords: Chinese hickory nut, endogenous foreign body, hyperspectral spectral imaging, pixel level, detection DOI: 10.25165/j.ijabe.20221502.6881 Citation: Feng Z, Li W H, Cui D. Detection of endogenous foreign bodies in Chinese hickory nuts by hyperspectral spectral imaging at the pixel level. Int J Agric & Biol Eng, 2022; 15(2): 204–210. Hyperspectral imaging ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Pixel ; Principal component analysis ; Convolutional neural network ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Diego A. Maldonado | Antony D. Vassileiou | Blair F. Johnston | Alastair J. Florence | Cameron J. Brown
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Data mining crystallization kinetics The database contains information on crystallization kinetics that describes crystal growth and primary nucleation for small molecules. In total, there are more than 336 datapoints distributed in more than 90 different chemical entities. Crystallization ; Nucleation ; Kinetics ; Crystal growth ; Crystal (programming language) ; Materials science ; Crystallography ; Computer science ; Data mining ; Chemistry ; Physics ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Ngoc-Thao-Hien Le | Tom Vermeyen | Roy Aerts | Wouter Herrebout | Luc Pieters | Emmy Tuenter
Source: Molecules (article, 2022-12-26)
Field: Chemistry — Organic Chemistry

Epimeric Mixture Analysis and Absolute Configuration Determination Using an Integrated Spectroscopic and Computational Approach—A Case Study of Two Epimers of 6-Hydroxyhippeastidine Structural elucidation has always been challenging, and misassignment remains a stringent issue in the field of natural products. The growing interest in discovering unknown, complex natural structures accompanies the increasing awareness concerning misassignments in the community. The combination of various spectroscopic methods with molecular modeling has gained popularity in recent years. In this work, we demonstrated, for the first time, its power to fully elucidate the 2-dimensional and 3-dimensional structures of two epimers in an epimeric mixture of 6-hydroxyhippeastidine. DFT calculation of chemical shifts was first performed to assist the assignment of planar structures. Furthermore, relative and absolute configurations were established by three different ways of computer-assisted structure elucidation (CASE) coupled with ORD/ECD/VCD spectroscopies. In addition, the significant added value of OR/ORD computations to relative and absolute configuration determination was also revealed. Remarkably, the differentiation of two enantiomeric scaffolds (crinine and haemanthamine) was accomplished via OR/ORD calculations with cross-validation by ECD and VCD. Absolute configuration ; Enantiomer ; Chemistry ; Epimer ; Computation ; Absolute (philosophy) ; Computational chemistry ; Stereochemistry ; Computer science ; Algorithm Chemical synthesis and alkaloids ; Molecular spectroscopy and chirality ; Alkaloids: synthesis and pharmacology
Authors: Satoshi Watanabe | Shotaro Hiraide | Hayato Kunimitsu | Atsushi Fujiwara | Minoru T. Miyahara
Source: Frontiers in Materials (article, 2022-01-25)
Field: Chemistry — Inorganic Chemistry

Mechanism of CO2 Capacity Reduction of Flexible Metal-Organic Framework Caused by Water Adsorption Elastic layer-structured metal-organic framework (MOF)-11 {ELM-11: [Cu(BF 4 ) 2 (4,4′-bipyridine) 2 ]}, which is a crystalline porous material, is a promising adsorbent for high-throughput and high-efficiency separation processes of CO 2 because of its peculiar adsorption characteristics originating from the flexibility of the crystal framework. However, the exposure of ELM-11 to water vapor has been reported to reduce its CO 2 capacity, which is problematic for processing feed gases that contain a certain concentration of water vapor. In this study, we investigated the stability of ELM-11 against water vapor exposure to reveal the mechanism of CO 2 capacity reduction. Our combined measurements of adsorption isotherms and X-ray diffraction patterns indicated that the CO 2 capacity reduction was caused by the partial formation of a crystalline subphase upon adsorption of water molecules and the subsequent formation of an amorphous phase to relax the crystalline grain boundaries. Because a higher supply rate of water molecules resulted in a larger amount of subphase formation, we concluded that the structural subphase was a metastable kinetically controlled structure, formed through the rate-dependent adsorption of water molecules. These results suggest that slowing the adsorption rate is an effective approach to suppress the formation of the subphase; therefore, we proposed the covering of ELM-11 surfaces with porous shells. We used ELM-12 {[Cu(CF 3 SO 3 ) 2 (4,4′-bipyridine) 2 ]} as a shell material because of its robust stability against water adsorption and affinity with ELM-11. The ELM-12 shell decreased the adsorption rate of water molecules compared with that of bare ELM-11, resulting in the suppression of subphase formation and preventing CO 2 capacity reduction. Although further optimization of the shell thickness and coverage is required to keep the capacity completely unchanged, controlling the adsorption rate of water molecules is successfully demonstrated to be possible with shell formation, which is key for industrial applications of ELM-11. Adsorption ; Metal-organic framework ; Molecule ; Chemical engineering ; Water vapor ; Amorphous solid ; Chemistry ; Porosity ; Materials science ; Metal ; Crystallography ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Yunsheng Bai | Atefeh Sohrabizadeh | Yizhou Sun | Jason Cong
Source: Proceedings of the 59th ACM/IEEE Design Automation Conference (article, 2022-07-10)
Field: Materials Science — Materials Chemistry

Improving GNN-based accelerator design automation with meta learning Recently, there is a growing interest in developing learning-based models as a surrogate of the High-Level Synthesis (HLS) tools, where the key objective is rapid prediction of the quality of a candidate HLS design for automated design space exploration (DSE). Training is usually conducted on a given set of computation kernels (or kernels in short) needed for hardware acceleration. However, the model must also perform well on new kernels. The discrepancy between the training set and new kernels, called domain shift, frequently leads to model accuracy drop which in turn negatively impact the DSE performance. In this paper, we investigate the possibility of adapting an existing meta-learning approach, named MAML, to the task of design quality prediction. Experiments show the MAML-enhanced model outperforms a simple baseline based on fine tuning in terms of both offline evaluation on hold-out test sets and online evaluation for DSE speedup results1. Computer science ; Speedup ; Automation ; Task (project management) ; Design space exploration ; Artificial intelligence ; Computation ; Set (abstract data type) ; Machine learning ; Computer engineering ; Embedded system ; Algorithm ; Programming language ; Parallel computing Machine Learning in Materials Science ; VLSI and Analog Circuit Testing ; Advanced Neural Network Applications
Authors: Wassim Gabriel | Victor Giurcoiu | Ludwig Lautenbacher | Mathias Wilhelm
Source: PROTEOMICS (article, 2022-05-17)
Field: Chemistry — Spectroscopy

Predicting fragment intensities and retention time of iTRAQ‐ and TMTPro‐labeled peptides with Prosit‐TMT Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3% - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins. Retention time ; Fragment (logic) ; Chemistry ; Chromatography ; Computational biology ; Biology ; Computer science Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Cancer, Hypoxia, and Metabolism
Authors: Siti Saripa Rabiah Lazim | Nazmi Mat Nawi | Siti Khairunniza Bejo | Abdul Rashid Mohamed Shariff | Najidah Abdullah
Source: International Food Research Journal (article, 2022-12-06)
Field: Chemistry — Analytical Chemistry

Prediction and classification of soluble solid contents to determine the maturity level of watermelon using visible and shortwave near infrared spectroscopy The present work investigated the potential application of a portable and low-cost spectroscopic technique to predict the soluble solid content (SSC) for determining the maturity level of watermelons. A total of 63 watermelon samples were used in the present work, representing three different maturity levels: unmatured, matured, and over-matured. Before spectral acquisition, each watermelon sample was cut into half, producing 126 fruit portions. Visible shortwave near infrared (VSNIR) spectrometer was used to record the spectral data from the skin surface of each portion. The SSC of each portion was measured using a digital refractometer. Partial least square (PLS) regression method was used to establish both calibration and prediction models to predict the SSC values from the watermelon samples. Support vector machine (SVM) classifier was used to categorise spectral data into the respective maturity levels. Results showed that the coefficient of determination (R2) values for calibration models of unmatured, matured, and over-matured were 0.65, 0.81, and 0.78, respectively. For the prediction model, the R2 values for unmatured, matured, and over-matured were 0.60, 0.74, and 0.76, respectively. The SVM yielded good classification accuracy of 85%. The present work demonstrated that the proposed spectroscopic method could be applied to predict and classify the maturity level of watermelons based on their skin condition. Maturity (psychological) ; Support vector machine ; Calibration ; Partial least squares regression ; Mathematics ; Coefficient of determination ; Analytical Chemistry (journal) ; Remote sensing ; Artificial intelligence ; Chemistry ; Computer science ; Statistics ; Chromatography ; Geology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity