jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hedieh Khalili | Azadeh Ebrahimian Pirbazari | Fatemeh Esmaeili Khalil Saraei | Seyed Hamed Mousavi
Source: Preprints.org (article, 2021-06-08)
Field: Chemistry — Analytical Chemistry

Simultaneous Removal of Basic Dyes from Binary Systems by Modified Orange Peel and Modeling the Process by an Intelligent Tool This study presents the consecutive modification of orange peel (OP) by NaOH and sodium dodecyl sulfate (SDS) for simultaneous elimination of basic dyes from the binary system and modeling the adsorption process using an intelligent tool. The natural and modified biosorbents were characterized by variety of analyses such as: field emission scanning electron microscopy with energy dispersive X-ray, N2 physisorption and Fourier transform infrared spectroscopy techniques. The influence of various variables on dye removal like pH, the quantity of biosorbents, dyes concentration, contact time, and temperature in the binary system were investigated and optimized by an artificial neural network (ANN) model as an intelligent tool. The optimum quantity of the sorbent was found to be 0.30 g for orange peel (OP) and 0.25 g for NaOH-treated OP (NOP) and SDS-decorated NOP (SNOP) at pH = 7. The kinetics and thermodynamics investigations showed that the removal of dyes obeyed the pseudo-second-order kinetic model and were spontaneous and exothermic in nature. Moreover, in order to describe the mechanism of sorption process, desorption studies of dyes were carried out. The desorption percentages of methylene blue (MB) in water and HCl were found to be in the range of 1.93%&amp;ndash;4.76% and 18.87%&amp;ndash;28.76%, respectively; in addition, the desorption percentages of crystal violet (CV) in water and HCl were obtained to be in the range of 4.11%&amp;ndash;7.41% and 32.84%&amp;ndash;43.00%, respectively; which could be a recommendation ion exchange or electrostatic attachment of dyes onto biosorbents. The ANN predictions matched with the experimental data very well (0.95308 &amp;lt; R2 &amp;lt; 0.99191 and 0.98335 &amp;lt; R2 &amp;lt; 0.99773 for MB and CV, respectively) which indicated high accuracy of the ANN model. In addition, the relative importance of each parameter was calculated by Garson&amp;rsquo;s equation. Crystal violet ; Chemistry ; Sorption ; Desorption ; Methylene blue ; Adsorption ; Orange G ; Methyl orange ; Fourier transform infrared spectroscopy ; Binary system ; Nuclear chemistry ; Chemical engineering ; Analytical Chemistry (journal) ; Chromatography ; Organic chemistry ; Photocatalysis ; Catalysis Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal ; Water Quality Monitoring and Analysis
Authors: Bojana Svrkota | Jovana Krmar | Ana Protić | Mira Zečević | Biljana Otašević
Source: Journal of the Serbian Chemical Society (article, 2021-11-09)
Field: Chemistry — Spectroscopy

Optimization of chromatographic separation of aripiprazole and impurities: Quantitative structure-retention relationship approach A new optimization strategy based on the mixed quantitative structure? retention relationship (QSRR) model is proposed for improving the RPHPLC separation of aripiprazole and its impurities (IMP A-E). Firstly, experimental parameters (EPs), namely mobile phase composition and flow rate, were varied according to Box?Behnken design and thereafter, an artificial neural network (ANN) as a QSRR model was built correlating EPs and selected molecular descriptors (ovality, torsion energy and non-1,4-van der Waals energy) with the log-transformed retention times of the analytes. Values of the root mean square error (RMSE) were used for an estimation of the quality of the ANNs (0.0227, 0.0191 and 0.0230 for the training, verification and test set, respectively). The separations of critical peak pairs on chromatogram (IMP AB and IMP D-C) were optimized using ANNs for which the EPs served as inputs and the log-transformed separation criteria s as the outputs. They were validated by application of leave-one-out cross-validation (RMSE values 0.065 and 0.056, respectively). The obtained ANNs were used for plotting response surfaces upon which the analyses chromatographic conditions resulting in optimal analytes retention behaviour and the optimal values of the separation criteria s were defined. The optimal conditions were 54 % of methanol at the beginning and 79 % of methanol at the end of gradient elution programme with a mobile phase flow rate of 460 ?L min-1. Chromatography ; Analyte ; Methanol ; Chemistry ; Elution ; Mean squared error ; Quality by Design ; Chromatographic separation ; Volumetric flow rate ; Artificial neural network ; High-performance liquid chromatography ; Analytical Chemistry (journal) ; Mathematics ; Computer science ; Thermodynamics ; Statistics ; Artificial intelligence Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Chromatography in Natural Products
Authors: Mary L. Comer | Jeff Simmons
Source: IEEE Signal Processing Magazine (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

The Markov Random Field in Materials Applications: A synoptic view for signal processing and materials readers The Markov random field (MRF) is one of the most widely used models in image processing, constituting a prior model for addressing problems such as image segmentation, object detection, and reconstruction. What is not often appreciated is that the MRF owes its origin to the physics of solids, making it an ideal prior model for processing microscopic observations of materials. While both fields know of their respective interpretations of the MRF, each knows very little about the other’s version of it. Hence, both fields have “blind spots,” where some concepts readily appreciated by one field are completely obscured from the other. With this in mind, the objectives of this article are to 1) develop a synoptic view of the MRF, the related Gibbs distribution, and the Hammersley–Clifford theorem that links them, in such a way that signal processing and materials readers will see them from the same perspective; and 2) explain physics-based regularization using the MRF and describe how it can provide insight into the performance of MRF-based segmentation methods. While the MRF has already been used in many machine learning contexts, we will use a simpler, more transparent method to illustrate the fundamental behavior of the MRF, with the understanding that this behavior should be inherent in more complex learning approaches. Markov random field ; Computer science ; Markov chain ; Artificial intelligence ; Segmentation ; Image processing ; Signal processing ; Field (mathematics) ; Perspective (graphical) ; Image segmentation ; Regularization (linguistics) ; Markov process ; Random field ; Pattern recognition (psychology) ; Computer vision ; Machine learning ; Image (mathematics) ; Mathematics ; Statistics Machine Learning in Materials Science ; Medical Image Segmentation Techniques ; Geochemistry and Geologic Mapping
Authors: SangHui Kim | Joongoo Kang
Source: Physical review. B./Physical review. B (article, 2021-05-28)
Field: Materials Science — Materials Chemistry

General gauge symmetry in the theory and simulation of heat transport in nonsolid materials The Green-Kubo theory of atomic heat transport relies on the ambiguous decomposition of total energy into ``atomic'' energies of individual atoms. The challenge is to understand how the transport coefficient thermodynamically emerges from such ill-defined atomic energies. Here, we show that a simple symmetry principle for atomic energies (that is, ``all possible ways of distributing energy among atoms are equivalent'') dictates the general theory of atomic heat transport, in which, by construction, the same heat conductivity \ensuremath{\kappa} is ensured regardless of the choice of atomic energies. This approach is analogous to the way in which general covariance (``all coordinate systems are equivalent'') is used to construct the general theory of relativity. To this end, we define atomic gauges that regulate the redundant degrees of freedom in atomic energies. The atomic gauge symmetry then uniquely determines the gauge-invariant form of macroscopic energy transfer, which is identified as heat. The gauge theory not only lays a firm foundation of the Green-Kubo formalism of heat transport, but also offers a variational method for calculating \ensuremath{\kappa} of nonsolid materials, as demonstrated using machine learning inferred atomic energies of ${\mathrm{Cu}}_{2}\mathrm{S}$, an intriguing solid-liquid hybrid material for thermoelectrics. Physics ; Gauge theory ; Atomic units ; Atomic nucleus ; Quantum mechanics ; Theoretical physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Mayara L. de Oliveira Peres | Estácio Tavares Wanderley Neto | Alfredo Antonio Alencar Exposito de Queiroz | Álvaro Antônio Alencar de Queiroz
Source: IEEE Sensors Letters (article, 2021-02-10)
Field: Materials Science — Polymers and Plastics

Intelligent Electrospun Thermochromic Composite Nanofibers for Temperature Measurements Thermochromic nanofibers (NFs) have emerged as an important class of nanostructured materials and have found a broad range of technologic applications. Herein, an optical fiber temperature sensor composed of coating electrospun thermochromic NFs on the end-face of fiber is reported. A composite coating consisting of an aqueous dispersion of a thermochromic material (cobalt-doped zinc phosphate or also called cobalt-doped hopeite -CoHo), in polyglycerol dendrimer (PGD), was directly electrospun onto the end-face surface of multimode optical fiber for the fabrication of temperature optical fiber sensor. Morphological features and optical properties of the resulting electrospun CoHo:PGD NFs were assessed. The diameter of the electrospun CoHo:PGDNFs deposited on the end-face of optical fiber was 500 ± 14 nm. The spectroscopic absorption of the electrospun CoHo:PGDNFs at 630 nm is measured to determine their relationship with temperature. The results under variable temperature from 25 to 250 °C indicate good linearity and show the potential of using CoHo:PGDNFs sensing applications for control in industrial processes that require a steady or changing temperature. To extract the chromaticity profile of the CoHo:PGDNFs and their temperature dependence, a multilayer feed-forward artificial neural network (ANN) was used. ANN has been successfully employed to extract temperature information from the chromatic changes of CoHo:PGDNFs. Thermochromism ; Materials science ; Electrospinning ; Nanofiber ; Fiber ; Optical fiber ; Composite material ; Nanotechnology ; Optics ; Polymer ; Chemistry Conducting polymers and applications ; Electrospun Nanofibers in Biomedical Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Mino Woo | Marc Stettler
Source: Industrial & Engineering Chemistry Research (article, 2021-10-20)
Field: Materials Science — Materials Chemistry

Feasibility Study on the Use of Artificial Neural Networks to Model Catalytic Oxidation in a Metallic Foam Reactor This study investigates the feasibility of using artificial neural networks (ANNs) to predict catalytic oxidation in diesel after-treatment systems and compares their performance to that of physics-based models. Existing physics models are revisited to generate baseline data for binary reactions of major species (CO, C3H6, and NO) measured in a lab-scale microreactor comprising a metallic foam catalytic substrate. The physics model performs well to predict the measured light-off curves, which are the species conversions with ramping temperature, and the R2 value is above 0.84 across a wide range of operating conditions. However, the model cannot perfectly capture the retarding trends observed in the CO and C3H6 conversion curves after light-off. In contrast, the ANN model is capable of accurately predicting the light-off curves for operating conditions seen during the training process. This might be practically useful but is inherently limited by the availability of experimental data for training. To compensate for the drawbacks of both approaches, this study suggests a hybrid model in which a pretrained ANN is used to calculate reaction rates in the physics models. Despite the more complex data generation process for training ANNs, the hybrid model captures the light-off curves including the retarding trend and is less sensitive to the range of test conditions without renormalization as compared to the pure ANN model. This study investigates the feasibility of ANNs by comparing the pros and cons among the physics models, pure ANN, and hybrid models and suggests a step toward the most appropriate uses of ANNs in modeling exhaust after-treatment in practical applications. Artificial neural network ; Microreactor ; Process (computing) ; Range (aeronautics) ; Computer science ; Materials science ; Biological system ; Catalysis ; Artificial intelligence ; Chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Vehicle emissions and performance
Authors: Anna Archetti | Renjie Lin | Nathanaël Restori | Fatemeh Kiani | Ted V. Tsoulos | Giulia Tagliabue
Source: Figshare (preprint, 2021-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermally-reconfigurable metalens Thanks to the compact design and multi-functional light-manipulation capabilities, reconfigurable metalenses, which consist of arrays of sub-wavelength meta-atoms, offer unique opportunities for advanced optical systems, from microscopy to augmented reality platforms. Although poorly explored in the context of reconfigurable metalens, thermo-optical effects in resonant silicon nanoresonators have recently emerged as a viable strategy to realize tunable meta-atoms. In this work, we report the proof-of-concept design of an ultrathin (300 nm thick) and thermo-optically reconfigurable silicon metalens operating at a fixed, visible wavelength (632 nm). Importantly, we demonstrate continuous, linear modulation of the focal-length up to 21% (from 165 $μ$m at 20$°$C to 135 $μ$m at 260$°$C). Operating under right-circularly polarized light, our metalens exhibits an average conversion efficiency of 26%, close to mechanically modulated devices, and has a diffraction-limited performance. Overall, we envision that, combined with machine-learning algorithms for further optimization of the meta-atoms, thermally-reconfigurable metalenses with improved performance will be possible. Also, the generality of this approach could offer inspiration for the realization of active metasurfaces with other emerging material within field of thermo-nanophotonics. Nanophotonics ; Context (archaeology) ; Materials science ; Optoelectronics ; Optics ; Realization (probability) ; Metamaterial ; Diffraction ; Wavelength ; Silicon ; Nanotechnology ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Weitao Du | He Zhang | Yuanqi Du | Qi Meng | Wei Chen | Bin Shao | Tie‐Yan Liu
Source:  (preprint, 2021-10-26)
Field: Materials Science — Materials Chemistry

Equivariant vector field network for many-body system modeling. Modeling many-body systems has been a long-standing challenge in science, from classical and quantum physics to computational biology. Equivariance is a critical physical symmetry for many-body dynamic systems, which enables robust and accurate prediction under arbitrary reference transformations. In light of this, great efforts have been put on encoding this symmetry into deep neural networks, which significantly boosts the prediction performance of down-streaming tasks. Some general equivariant models which are computationally efficient have been proposed, however, these models have no guarantee on the approximation power and may have information loss. In this paper, we leverage insights from the scalarization technique in differential geometry to model many-body systems by learning the gradient vector fields, which are SE(3) and permutation equivariant. Specifically, we propose the Equivariant Vector Field Network (EVFN), which is built on a novel tuple of equivariant basis and the associated scalarization and vectorization layers. Since our tuple equivariant basis forms a complete basis, learning the dynamics with our EVFN has no information loss and no tensor operations are involved before the final vectorization, which reduces the complex optimization on tensors to a minimum. We evaluate our method on predicting trajectories of simulated Newton mechanics systems with both full and partially observed data, as well as the equilibrium state of small molecules (molecular conformation) evolving as a statistical mechanics system. Experimental results across multiple tasks demonstrate that our model achieves best or competitive performance on baseline models in various types of datasets. Equivariant map ; Hessian matrix ; Vectorization (mathematics) ; Computer science ; Leverage (statistics) ; Deep learning ; Tensor (intrinsic definition) ; Artificial neural network ; Physical system ; Basis (linear algebra) ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Applied mathematics ; Mathematics ; Physics ; Geometry ; Parallel computing ; Pure mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Parallel Computing and Optimization Techniques
Authors: Abdul Talib Din | M. A. M. Piah | Abdul Rahim Abdullah | F. Abdullah
Source:  (article, 2021-07-12)
Field: Materials Science — Materials Chemistry

Classification of Degraded Polymer Insulator Using Support Vector Machine The ability to monitor closely the surface degradation condition of polymer insulator will be really beneficial to the power utility company in order to ensure smooth and safe power transmitted to the consumer. If the level of degradation condition could be classified, then it could ease the maintenance team to take proper action as to avoid any undesirable event from happening. In this study, it has implemented the leakage current signal parameters data in the classification process of degraded field-aged insulator. These signal parameters are extracted from the Spectrogram. Prior to this analysis, the leakage current signal is captured during the testing method of inclined plane tracking. The physical evaluations such as arithmetical mean of surface roughness and static contact angle are also measured for the purpose of comparison of surface conditions. The Support Vector Machine is implemented in the machine learning test, in which the percentage of classification accuracy between degraded sample and the controlled sample is recorded. To validate the classification results obtained, the insulator sheds under test was going through the Spray Method to determine the criteria of hydrophobicity class in Table 1 of the IEC TS 62073:2016. By using the percentage of total harmonic distortion data, the consistency results of the classification accuracy percentage have been successfully determined the two significant classes and the transition class between them. However, there is an existence of insignificant classes if the root means squared leakage current data is implemented. Therefore, by implementing the appropriate leakage current signal parameter data, the degradation classification could be determined accurately. Support vector machine ; Insulator (electricity) ; Computer science ; Artificial intelligence ; Polishing ; Engineering ; Electrical engineering ; Mechanical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Industrial Vision Systems and Defect Detection
Authors: Christopher Kuenneth | William Schertzer | Rampi Ramprasad
Source: Macromolecules (article, 2021-08-02)
Field: Materials Science — Materials Chemistry

Correction to “Copolymer Informatics with Multitask Deep Neural Networks” ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to “Copolymer Informatics with Multitask Deep Neural Networks”Christopher KuennethChristopher KuennethMore by Christopher Kuennethhttps://orcid.org/0000-0002-6958-4679, William SchertzerWilliam SchertzerMore by William Schertzer, and Rampi Ramprasad*Rampi RamprasadMore by Rampi Ramprasadhttps://orcid.org/0000-0003-4630-1565Cite this: Macromolecules 2021, 54, 15, 7321Publication Date (Web):August 2, 2021Publication History Published online2 August 2021Published inissue 10 August 2021https://doi.org/10.1021/acs.macromol.1c01539Copyright © 2021 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views446Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (391 KB) Get e-Alerts Get e-Alerts Notice ; Citation ; Computer science ; Informatics ; Information retrieval ; Social media ; World Wide Web ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials