jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Silvan Englisch | Janis Wirth | Dominik Drobek | Benjamin Apeleo Zubiri | Erdmann Spiecker
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Extending lab-based X-ray nanotomography of low Z and porous materials to larger sample volumes without compromising resolution In this study, we present a method to extend the 3D volume reconstructed by X-ray nanotomography (Nano-CT) in a lab-based ZEISS Xradia 810 Ultra system while keeping the spatial resolution close to (50 nm). Furthermore, we demonstrate the improvement of image and segmentation quality for different low Z and porous materials. The advantage of this method is corroborated by determining the 3D resolution through Fourier ring/shell correlation (FRC/FSC). Sample (material) ; Materials science ; Porosity ; Content (measure theory) ; Resolution (logic) ; High resolution ; Analytical Chemistry (journal) ; Nanotechnology ; Composite material ; Computer science ; Chromatography ; Chemistry ; Mathematics ; Artificial intelligence ; Remote sensing ; Geography Electron and X-Ray Spectroscopy Techniques ; Advanced X-ray and CT Imaging ; Advanced X-ray Imaging Techniques
Authors: Nathan Shoman | Benjamin B. Cipiti | Thomas Grimes | Ben Wilson | R. W. Gladen
Source:  (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Insights from Applied Machine Learning for Safeguarding a PUREX Reprocessing Facility. Abstract not provided. PUREX ; Safeguarding ; Computer science ; Nuclear engineering ; Waste management ; Engineering ; Chemistry ; Medicine ; Chromatography Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Dominic Bara | Emily G. Meekel | Ignas Pakamorė | Claire Wilson | Sanliang Ling | Ross S. Forgan
Source: ChemRxiv (preprint, 2021-07-28)
Field: Chemistry — Inorganic Chemistry

Exploring and Expanding the Fe-Terephthalate Metal-Organic Framework Phase Space by Coordination and Oxidation Modulation The synthesis of phase pure metal-organic frameworks (MOFs) – network solids of metal clusters connected by organic linkers – is often complicated by the possibility of forming multiple diverse phases from one metal-ligand combination. For example, there are at least six Fe-terephthalate MOFs reported to date, with many examples of erroneous assignment of phase based on diffraction data alone. Herein, we show that modulated self-assembly can be used to influence the kinetics of self-assembly of Fe-terephthalate MOFs. We comprehensively assess the effect of addition of both coordinating modulators and pH modulators to the outcome of syntheses, as well as probing the influence of the oxidation state of the Fe precursor (oxidation modulation) and the role of the counteranion on the phase(s) formed. In doing so, we shed light on the thermodynamic landscape of this phase system, uncover mechanistics of modulation, provide robust routes to phase pure materials, often as single crystals, and introduce two new Fe-terephthalate MOFs to an already complex system. The results highlight the potential of modulated self-assembly to bring precision control and new structural diversity to systems that have already received significant study. Phase (matter) ; Materials science ; Metal-organic framework ; Metal ; Ligand (biochemistry) ; Coordination complex ; Modulation (music) ; Oxidation state ; Kinetics ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Metallurgy ; Physics ; Receptor Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Nanocluster Synthesis and Applications
Authors: Kevin Hoffman | Jae Yoon Sung | André Zazzera
Source: arXiv (Cornell University) (preprint, 2021-04-26)
Field: Chemistry — Spectroscopy

Multi-Output Random Forest Regression to Emulate the Earliest Stages of\n Planet Formation In the current paradigm of planet formation research, it is believed that the\nfirst step to forming massive bodies (such as asteroids and planets) requires\nthat small interstellar dust grains floating through space collide with each\nother and grow to larger sizes. The initial formation of these pebbles is\ngoverned by an integro-differential equation known as the Smoluchowski\ncoagulation equation, to which analytical solutions are intractable for all but\nthe simplest possible scenarios. While brute-force methods of approximation\nhave been developed, they are computationally costly, currently making it\ninfeasible to simulate this process including other physical processes relevant\nto planet formation, and across the very large range of scales on which it\noccurs. In this paper, we take a machine learning approach to designing a\nsystem for a much faster approximation. We develop a multi-output random forest\nregression model trained on brute-force simulation data to approximate\ndistributions of dust particle sizes in protoplanetary disks at different\npoints in time. The performance of our random forest model is measured against\nthe existing brute-force models, which are the standard for realistic\nsimulations. Results indicate that the random forest model can generate highly\naccurate predictions relative to the brute-force simulation results, with an\n$R^{2}$ of 0.97, and do so significantly faster than brute-force methods.\n Statistical physics ; Planet ; Computer science ; Random forest ; Brute force ; Asteroid ; Applied mathematics ; Physics ; Mathematics ; Astrobiology ; Astrophysics ; Artificial intelligence Molecular Spectroscopy and Structure ; Advanced Thermodynamic Systems and Engines ; Spacecraft and Cryogenic Technologies
Authors: Kongphope Chaarmart | Sureeporn Narongwongwattana | Ronnarit Rittiron | Worawat Sa-Ngiamvibool
Source: Instrumentation Mesure Métrologie (article, 2021-10-31)
Field: Chemistry — Analytical Chemistry

Evaluation of Chemical Quality on Juices and Wine Produced from Mamao Fruit (Antidesma Puncticulatum Miq.) Within Near-Infrared Spectroscopy The chemical quality of juices and wine produced from Mamao fruit was evaluated by Fourier transform near-infrared (FT-NIR). The calibration equation was created by the cross-validation method to be si+mulated the accuracy. Statistical values composed of correlation coefficient (R), standard error of cross-validation (SECV) and bias were used. Brix values and acidity values of Mao juice and the Brix, acidity, and alcohol values of Mao wine products were evaluated through the standard and cross-validation relation. It was found that was observed with NIR spectrometer to be absorbed in the same IR wavelength (1450 nm) which indicated that the water is the main composition. Based on FT-NIR analysis, the spectrum latices of juices and wine were revealed in the same range of the absorption bands at 1450 nm and 1940 nm to be confirmed the water composition. Also, the FT-NIR spectra from region 2258-2312 nm in Mao wine product have been predicted to the Ethanol functions. Wine ; Brix ; Chemistry ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Grape wine ; Fourier transform infrared spectroscopy ; Food science ; Chromatography ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities
Authors: Tatiana Lyashenko
Source: Mechanics And Mathematical Method (article, 2021-06-01)
Field: Materials Science — General Materials Science

STRUCTURED SYSTEMS OF FACTORS AND EXPERIMENTAL-STATISTICAL MODELS IN STUDIES OF BUILDING COMPOSITES The introduction to the article presents the objects of research, for which the proposed approach to modelling and the types of the models are intended. These are high quality composite building materials, the multicomponent dispersed systems, with the components that could be multicomponent themselves. The need to use for their design the mathematical models of the dependences of structure parameters and properties of the material on the factors of composition and processes of production and operation is noted. Then the background of experimental-statistical (ES) models application in research and development of composite materials is considered. What they are needed for is emphasized. The peculiarities of these models are noted in contrast to the models of other classes, including the usual regression ones. The necessity of experiment design to build ES-models is also emphasized. Special types of ES-models are named, for the cases when it is necessary to consider linearly related factors ("mixtures"). The expediency of structurizing the system of all the factors under consideration and the corresponding ES-models is indicated to. The concept of a priori and a posteriori structuring of factor systems when modeling is proposed. Systems that could include subsystems of linearly related factors and subsystems of mutually independent factors separated out at the stage of a priori structuring have been called “mixtures, technologies" systems and classified by the type of factor domains. System designations are given. Special polynomial models developed for them are presented. For the analysis of modelling results, a posteriori allocation of the factor regions and the use of tools of composition-process fields methodology are proposed. The local fields of material properties in coordinates of one or another group of the factors and their transformation under the influence of the factors of another group can be analysed, using numerical generalizing indices of the local fields and the secondary models for these indices. The path from obtaining data of designed natural experiment for building the primary ES-models to the secondary models, for generalizing indices, based on the results of computational experiment, is schematically shown. A priori and a posteriori ; Structuring ; Computer science ; Polynomial ; Statistical model ; Mathematical model ; Type (biology) ; Contrast (vision) ; Quality (philosophy) ; Regression analysis ; Industrial engineering ; Mathematics ; Artificial intelligence ; Machine learning ; Engineering ; Statistics Material Properties and Applications ; Industrial Engineering and Technologies ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Yanfei Li | Xianying Feng | Yandong Liu | Xingchang Han
Source: Research Square (preprint, 2021-06-09)
Field: Chemistry — Analytical Chemistry

Apple Quality Identification and Classification by Computer Vision Based on Deep Learning Abstract This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on Convolutional Neural Networks (CNN) which aimed at accurate and fast grading of apple quality. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. Two other methods, which were Google Inception v3 model and traditional imaging process method, were also used for apple quality classification. The greatest training accuracy of the Google Inception v3 model was 92% with 91.2% validation accuracy. The 78.14% accuracy was obtained by traditional method based on histogram of oriented gradient (HOG) and gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The three models were tested using independent 300 apples testing set, getting accuracy of 95.33%, 91.33%, and 77.67%, respectively. The results showed that the proposed model was more helpful and accurate for classification of apple quality. Furthermore, the training times of three methods were 27, 51, and 287 minutes, respectively. The proposed model can be considered a cost-effective method for fast grading of apple quality. Artificial intelligence ; Support vector machine ; Computer science ; Convolutional neural network ; Pattern recognition (psychology) ; Grading (engineering) ; Histogram ; Classifier (UML) ; Histogram of oriented gradients ; Machine learning ; Image (mathematics) ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Gints Romanovskis | Karlis Bumanis | Gundars Kulikovskis | P. Rivz̆a
Source: Engineering for Rural Development (article, 2021-05-27)
Field: Chemistry — Organic Chemistry

Location and identification of oak wood defect by deep learning As the value of oak wood increases, it is necessary to think of solutions that help make use as much material as possible during the industrial processing of wood. In order to study the possibilities of efficient use of wood, the specific oak wood features and defects were compiled from images obtained on a scanner prototype. The scanner, based on two TeleDyne Linea line cameras, captured high-quality images at 17,000 by 900 resolution. When the images were marked, two different mask r-cnn models were trained, using the instance segmentation method. The original model (first) contained only the data obtained from the scanner, while the training of the second model used the original images and additional images that were artificially adjusted to expand the data set. New images where generated using keras framework. There were only two data augmentation methods used like "brighten change" and "gaussian noise". These methods where chosen, because they did not change the object physical location on image. All new images where generated based on labelled images, that way the new images did not need to be relabelled. The original image json file was attached to artificially generated images. By using this method, a lot of marking process time saved. The original model showed an average of 73%, while the second model, which used data augmentation, improves the accuracy of the guess by an average of 16%, reaching an average of ~ 89%. The resulting network model was successful in identifying and localizing problems specific to oak wood. Identification (biology) ; Computer science ; Java ; Artificial intelligence ; Programming language ; Biology ; Botany Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: J. A. Adeyanju | J. O. Olajide | Emmanuel Olusola Oke | Jelili Babatunde Hussein | Chiamaka Jane Ude
Source: Acta Universitatis Cibiniensis. Series E: Food Technology (article, 2021-12-01)
Field: Chemistry — Organic Chemistry

Prediction of the thermo-physical properties of deep-fat frying plantain chips (<i>ipekere</i>) using artificial neural network Abstract This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips ( ipekere ). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m 3 while thermal diffusivity and conductivity were least affected with 0.192 x 10 −6 m 2 /s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R 2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips. Artificial neural network ; Thermal diffusivity ; Physical property ; Biological system ; Thermal conductivity ; Factorial experiment ; Mathematics ; Computer science ; Materials science ; Process engineering ; Artificial intelligence ; Machine learning ; Thermodynamics ; Engineering ; Composite material ; Physics Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Spectroscopy and Chemometric Analyses
Authors: Jia Q. Truong | Stephanie Nguyen | John B. Bruning | Keith E. Shearwin
Source: Journal of Visualized Experiments (article, 2021-01-16)
Field: Materials Science — Materials Chemistry

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening Protein structure elucidation using X-ray crystallography requires both high quality diffracting crystals and computational solution of the diffraction phase problem. Novel structures that lack a suitable homology model are often derivatized with heavy atoms to provide experimental phase information. The presented protocol efficiently generates derivatized protein crystals by combining random microseeding matrix screening with derivatization with a heavy atom molecule I3C (5-amino-2,4,6-triiodoisophthalic acid). By incorporating I3C into the crystal lattice, the diffraction phase problem can be efficiently solved using single wavelength anomalous dispersion (SAD) phasing. The equilateral triangle arrangement of iodine atoms in I3C allows for rapid validation of a correct anomalous substructure. This protocol will be useful to structural biologists who solve macromolecular structures using crystallography-based techniques with interest in experimental phasing. Phaser ; Protein crystallization ; Equilateral triangle ; Diffraction ; Phase problem ; Derivatization ; Crystallography ; Substructure ; Chemistry ; Algorithm ; Topology (electrical circuits) ; Computer science ; Physics ; Optics ; Mathematics ; Crystallization ; Combinatorics ; Geometry ; Mass spectrometry Enzyme Structure and Function ; Protein Structure and Dynamics ; Porphyrin Metabolism and Disorders
Authors: Pavithira Sathinathan | Peck Loo Kiew
Source: International Journal of Biomass and Renewables (article, 2021-04-30)
Field: Materials Science — Polymers and Plastics

REGRESSION ANALYSIS FOR THE ADSORPTION OF OIL USING HUMAN HAIR AS NATURAL ADSORBENT The abundance of human hair waste (known as keratin biomass) in most parts of the world and its accumulation in waste streams are causing many environmental problems. Human hair’s potential as a natural adsorbent to remove oil from wastewater was emphasised in this study. Mathematical and deep learning approaches were adopted to develop the regression models of oil adsorption using gents and ladies’ hair wastes. The experimental results were obtained from literature review to perform regression analysis using Artificial Neural Network (ANN) in Matlab, Microsoft Excel, and Design Expert 6.0.6. The efficiency of these tools was compared in predicting the oil removal percentage within a specified range of adsorption parameters. This was done by comparing the R2 value of the established adsorption models. In this study, the effect of different adsorption parameters, namely pH, contact time, and adsorbent dosage, on the oil removal percentage was included in the regression analysis. Subsequently, using the regression model with the highest R2 valuefor both gents and ladies’ hair adsorbent, a graphical user interface (GUI) was developed for the oil adsorption process to ease users in predicting the oil removal percentage within the specified range of adsorption parameters. The results showed that the adsorption model with the highest R2 value of 0.9570 for gents hair and 0.9650 for ladies hair was developed using the ANN tool, implying its superiority in correlating the adsorption parameters to the oil removal percentage. However, in creating the GUI layout for the oil adsorption process, the regression model generated by the RSM regression tool (the second-highest R2 at 0.8290 for gents hair and 0.6158 for ladies hair) was adopted to the limitations in retrieving the regression model from the ANN tool. The outcome of this study is expected to benefit users without prior knowledge of the oil adsorption process using human hair adsorbent to predict the removal percentage within a specified range of adsorption parameters, without the hassle of conducting experimental work.&#x0D; Keywords: Human hair waste, keratin biomass, regression, oil adsorption, graphical user interface Adsorption ; Natural (archaeology) ; Chemistry ; Chromatography ; Organic chemistry ; Biology Textile materials and evaluations ; Dye analysis and toxicity
Authors: Graham R. Fleming | Vladimir Chernyak | Akihito Ishizaki
Source: The Journal of Physical Chemistry B (article, 2021-11-04)
Field: Chemistry — Spectroscopy

Tribute to Yoshitaka Tanimura ADVERTISEMENT RETURN TO ISSUESpecial Issue Prefac...Special Issue PrefaceNEXTTribute to Yoshitaka TanimuraGraham R. Fleming*Graham R. FlemingDepartment of Chemistry, University of California, Berkeley, California 94720, United States*Email: [email protected]More by Graham R. Fleminghttps://orcid.org/0000-0003-0847-1838, Vladimir Y. Chernyak*Vladimir Y. ChernyakDepartment of Chemistry, Wayne State University, Detroit, Michigan 48202, United States*Email: [email protected]More by Vladimir Y. Chernyak, and Akihito Ishizaki*Akihito IshizakiInstitute for Molecular Science, National Institute of Natural Sciences, Okazaki 444-8585, Japan*Email: [email protected]More by Akihito Ishizakihttps://orcid.org/0000-0002-0246-4461Cite this: J. Phys. Chem. B 2021, 125, 43, 11785–11786Publication Date (Web):November 4, 2021Publication History Published online4 November 2021Published inissue 4 November 2021https://pubs.acs.org/doi/10.1021/acs.jpcb.1c08551https://doi.org/10.1021/acs.jpcb.1c08551introductionACS PublicationsCopyright © Published 2021 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views1292Altmetric-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 InRedditEmail PDF (2 MB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Infrared light,Infrared spectroscopy,Molecular dynamics,Raman spectroscopy,Students Get e-Alerts Tribute ; Art ; Art history Molecular spectroscopy and chirality ; Synthesis and Properties of Aromatic Compounds ; Advanced Chemical Physics Studies