jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rudolf Naef | William E. Acree
Source: Molecules (article, 2021-02-17)
Field: Chemistry — Organic Chemistry

Calculation of the Vapour Pressure of Organic Molecules by Means of a Group-Additivity Method and Their Resultant Gibbs Free Energy and Entropy of Vaporization at 298.15 K The calculation of the vapour pressure of organic molecules at 298.15 K is presented using a commonly applicable computer algorithm based on the group-additivity method. The basic principle of this method rests on the complete breakdown of the molecules into their constituting atoms, further characterized by their immediate neighbour atoms. The group contributions are calculated by means of a fast Gauss-Seidel fitting algorithm using the experimental data of 2036 molecules from literature. A ten-fold cross-validation procedure has been carried out to test the applicability of this method, which confirmed excellent quality for the prediction of the vapour pressure, expressed in log(pa), with a cross-validated correlation coefficient Q<sup>2</sup> of 0.9938 and a standard deviation σ of 0.26. Based on these data, the molecules' standard Gibbs free energy ΔG°<sub>vap</sub> has been calculated. Furthermore, using their enthalpies of vaporization, predicted by an analogous group-additivity approach published earlier, the standard entropy of vaporization ΔS°<sub>vap</sub> has been determined and compared with experimental data of 1129 molecules, exhibiting excellent conformance with a correlation coefficient R<sup>2</sup> of 0.9598, a standard error σ of 8.14 J/mol/K and a medium absolute deviation of 4.68%. Additive function ; Vaporization ; Chemistry ; Molecule ; Gibbs free energy ; Vapor pressure ; Thermodynamics ; Correlation coefficient ; Standard deviation ; Enthalpy of vaporization ; Standard molar entropy ; Standard enthalpy of formation ; Absolute deviation ; Entropy (arrow of time) ; Enthalpy ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Statistics ; Mathematics ; Physics Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Thermal and Kinetic Analysis
Authors: Yisen Liu | Songbin Zhou | Wei Han | Chang Li | Weixin Liu | Zefan Qiu | Hong Chen
Source: Foods (article, 2021-04-06)
Field: Chemistry — Analytical Chemistry

Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R<sup>2</sup>) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively. Overfitting ; Convolutional neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Ensemble learning ; Machine learning ; Initialization ; Estimator ; Artificial neural network ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Stacy M. Copp | Anna Gonzàlez‐Rosell
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Large-scale investigation of the effects of nucleobase sequence on fluorescence excitation and Stokes shifts of DNA-stabilized silver clusters DNA-stabilized silver clusters (AgN-DNAs) exhibit diverse sequence-programmed fluorescence, making these tunable nanoclusters promising sensors and bioimaging probes. Recent advances in the understanding of AgN-DNA structures and optical properties have largely relied on detailed characterization of single species isolated by chromatography. Because most AgN-DNAs are unstable under chromatography, such studies do not fully capture the diversity of these clusters. As an alternative method, we use high-throughput synthesis and spectroscopy to measure steady state Stokes shifts of hundreds of AgN-DNAs. Steady state Stokes shift is of interest because its magnitude is determined by energy relaxation processes which may be sensitive to specific cluster geometry, attachment to the DNA template, and structural engagement of solvent molecules. We identify 305 AgN-DNA samples with single-peaked emission and excitation spectra, a characteristic of pure solutions and single emitters, which thus likely contain a dominant emissive AgN-DNA species. Steady state Stokes shifts of these samples vary widely, are in agreement with values reported for purified clusters, and are several times larger than for typical organic dyes. We then examine how DNA sequence selects AgN-DNA excitation energies and Stokes shifts, comment on possible mechanisms for energy relaxation processes in AgN-DNAs, and discuss how differences in AgN-DNA structure and DNA conformation may result in the wide distribution of optical properties observed here. These results may aid computational studies seeking to understand the fluorescence process in AgN-DNAs and the relations of this process to AgN-DNA structure. Nucleobase ; Fluorescence ; DNA ; Sequence (biology) ; Excitation ; Stokes shift ; DNA sequencing ; Materials science ; Chemical physics ; Biophysics ; Nanotechnology ; Chemistry ; Physics ; Biology ; Optics ; Biochemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Diego Lascano | Alejandro Lerma-Canto | Vicent Fombuena | Rafael Balart | Nestor Montañés Muñoz | Luis Quiles‐Carrillo
Source: Polymers (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

Kinetic Analysis of the Curing Process of Biobased Epoxy Resin from Epoxidized Linseed Oil by Dynamic Differential Scanning Calorimetry The curing process of epoxy resin based on epoxidized linseed oil (ELO) is studied using dynamic differential scanning calorimetry (DSC) in order to determine the kinetic triplet (<i>E<sub>a</sub></i>, f(α) and <i>A</i>) at different heating rates. The apparent activation energy, <i>E<sub>a</sub></i>, has been calculated by several differential and integral isoconversional methods, namely Kissinger, Friedman, Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS) and Starink. All methods provide similar values of <i>E<sub>a</sub></i> (between 66 and 69 kJ/mol), and this shows independence versus the heating rate used. The epoxy resins crosslinking is characterized by a multi-step process. However, for the sake of the simplicity and to facilitate the understanding of the influence of the oxirane location on the curing kinetic, this can be assimilated to a single-step process. The reaction model has a high proportion of autocatalytic process, fulfilling that <i>α<sub>M</sub></i> is between 0 and <i>α<sub>p</sub></i> and <i>α<sub>M</sub></i> < αp∞. Using as reference the model proposed by Šesták-Berggren, by obtaining two parameters (<i>n</i> and <i>m</i>) it is possible to obtain, on the one hand, the kinetic parameters and, on the other hand, a graphical comparison of the degree of conversion, <i>α</i>, versus temperature (<i>T</i>) at different heating rates with the average <i>n</i> and <i>m</i> values of this model. The good accuracy of the proposed model with regard to the actual values obtained by DSC gives consistency to the obtained parameters, thus suggesting the crosslinking of the ELO-based epoxy has apparent activation energies similar to other petroleum-derived epoxy resins. Differential scanning calorimetry ; Epoxy ; Autocatalysis ; Activation energy ; Curing (chemistry) ; Linseed oil ; Materials science ; Order of reaction ; Composite material ; Kinetic energy ; Kinetics ; Chemical engineering ; Polymer chemistry ; Thermodynamics ; Chemistry ; Organic chemistry ; Reaction rate constant ; Catalysis Thermal and Kinetic Analysis ; Polymer composites and self-healing ; Epoxy Resin Curing Processes
Authors: Yongke Sun | Qizhao Lin | Xin He | Youjie Zhao | Fei Dai | Jian Qiu | Yong Cao
Source: International Journal of Computational Intelligence Systems (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Wood Species Recognition with Small Data: A Deep Learning Approach Wood species recognition is an important work in the wood trade and wood commercial activities. Although many recognition methods were presented in recent years, the existing wood species recognition methods mainly use shallow recognition models with low accuracy and are still unsatisfying for many real-world applications. Besides, their generalization ability is not strong. In this paper, a novel deep-learning-based wood species recognition method was proposed, which improved the accuracy and generalization greatly. The method uses 20X amplifying glass to acquire wood images, extracts the image features with ResNet50 neural network, refines the features with linear discriminant analysis (LDA), and recognizes the wood species with a KNN classifier. Our data was small, but we adopted transfer learning to improve our method. About 3000 wood images were used in our wood species recognition experiments and our method was executed in 25 rare wood species and the results showed our method had better generalization performance and accuracy. Compared with traditional deep learning our results were obtained from a small amount of data, which just confirmed the effectiveness of our method. Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Deep learning ; Machine learning Wood and Agarwood Research ; Forest Ecology and Biodiversity Studies ; Remote Sensing and LiDAR Applications
Authors: Berit Zeller‐Plumhoff | D. Tolnai | Martin Wolff | Imke Greving | Norbert Hort | Regine Willumeit‐Römer
Source: Advanced Engineering Materials (article, 2021-05-27)
Field: Materials Science — Biomaterials

Utilizing Synchrotron Radiation for the Characterization of Biodegradable Magnesium Alloys—From Alloy Development to the Application as Implant Material Magnesium alloys are highly attractive for their application as structural materials as well as medical implants. A range of alloying systems exists which are investigated, e.g., in terms of alloy microstructure changes, in particular during different processing steps or mechanical testing, and in terms of the associated corrosion performance of the material. Synchrotron radiation and in particular synchrotron radiation microcomputed tomography and nanotomography yield a unique opportunity to investigate such changes and processes in 3D at high resolution and in situ, thus significantly broadening our knowledge base. Herein, the benefits of using synchrotron radiation for the investigation of magnesium alloys with particular respect to its application as a biodegradable implant are demonstrated. Advances in experimental environments for in situ testing are reviewed, and all stages of materials testing are covered in which synchrotron radiation has been used, i.e., from developing and processing of the material, to corrosion testing and assessing implant integration and stability ex vivo. This review incorporates advances both in micro‐ and nanotomographic imaging regimes and further includes complementary techniques, such as X‐ray diffraction, small angle X‐ray scattering, X‐ray fluorescence, and diffraction tomography. Finally, an outlook into future developments is provided. Materials science ; Synchrotron radiation ; Characterization (materials science) ; Synchrotron ; Corrosion ; Alloy ; Microstructure ; Diffraction ; Beamline ; Optics ; Metallurgy ; Nanotechnology Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloys Composites Properties
Authors: Lázaro Betancourt | Jeovanis Gil | Yonghyo Kim | Viktória Doma | Uğur Çakır | Aniel Sánchez | Jimmy Rodriguez Murillo | Magdalena Kuras | Indira Plá | Yutaka Sugihara | Roger Appelqvist | Elisabet Wieslander | Charlotte Welinder | Erika Velásquez | Natália Pinto de Almeida | Nicole Woldmar | Matilda Marko-Varga | Krzysztof Pawłowski | Jonatan Eriksson | Beáta Szeitz | Bo Baldetorp | Christian Ingvar | Håkan Olsson | Lotta Lundgren | Henrik Lindberg | Henriett Oskolás | Boram Lee | Ethan Berge | Marie Sjögren | Carina Eriksson | Dasol Kim | Ho Jeong Kwon | Beatrice S. Knudsen | Melinda Rezeli | Runyu Hong | Péter Horvatovich | Tasso Miliotis | Toshihide Nishimura | Harubumi Kato | Erik Steinfelder | Madalina Oppermann | Ken Miller | Francesco Florindi | Qimin Zhou | Gilberto B. Domont | Luciana Pizzatti | Fábio César Sousa Nogueira | Péter Horváth | Letícia Szadai | József Tı́már | Sarolta Kárpáti | Attila Marcell Szász | Johan Malm | David Fenyö | Henrik Ekedahl | István Balázs Németh | György Marko‐Varga
Source: Clinical and Translational Medicine (article, 2021-07-01)
Field: Chemistry — Spectroscopy

The human melanoma proteome atlas—Defining the molecular pathology The MM500 study is an initiative to map the protein levels in malignant melanoma tumor samples, focused on in-depth histopathology coupled to proteome characterization. The protein levels and localization were determined for a broad spectrum of diverse, surgically isolated melanoma tumors originating from multiple body locations. More than 15,500 proteoforms were identified by mass spectrometry, from which chromosomal and subcellular localization was annotated within both primary and metastatic melanoma. The data generated by global proteomic experiments covered 72% of the proteins identified in the recently reported high stringency blueprint of the human proteome. This study contributes to the NIH Cancer Moonshot initiative combining detailed histopathological presentation with the molecular characterization for 505 melanoma tumor samples, localized in 26 organs from 232 patients. Proteome ; Melanoma ; Human proteome project ; Computational biology ; Pathology ; Histopathology ; Biology ; Proteomics ; Medicine ; Cancer research ; Bioinformatics ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Melanoma and MAPK Pathways
Authors: Preeti Anand | J.P. Pandey | Dev Mani Pandey
Source: Journal of Genetic Engineering and Biotechnology (article, 2021-02-19)
Field: Materials Science — Biomaterials

Study on cocoonase, sericin, and degumming of silk cocoon: computational and experimental Present study enlightens on the detailed characteristics of cocoonase and sericin proteins, comparative degumming activity, and image analysis of cocoonase enzyme and Na<sub>2</sub>CO<sub>3</sub> chemical-treated silk sheets. Obtained findings illustrated about use of cocoonase enzyme in the degumming of silk cocoon at larger scale that will be a boon to the silk industry. Sericin ; SILK ; Fibroin ; Sericulture ; Chemistry ; Bombyx mori ; Materials science ; Biochemistry ; Gene ; Composite material Silk-based biomaterials and applications ; Invertebrate Immune Response Mechanisms ; Insect Resistance and Genetics
Authors: Vardaan Chauhan | Timo Kärki | Juha Varis
Source: Processes (article, 2021-10-19)
Field: Materials Science — Polymers and Plastics

Optimization of Compression Molding Process Parameters for NFPC Manufacturing Using Taguchi Design of Experiment and Moldflow Analysis This paper presents the application of Taguchi design of experiment and Autodesk Moldflow® simulation in finding the optimal processing parameters for the manufacturing of natural fiber–polymer composite products. The material used in the study is a composite of recycled thermoplastic reinforced with 10% wood fibers. For the study, four critical processing parameters, namely compression time, mold temperature, melt temperate, and pressure, were selected for optimization. Process analysis was carried out in Moldflow® utilizing a combination of process parameters based on an L9 orthogonal array. Later, the warpage output from Moldflow® simulation was converted into a signal-to-noise (S/N) ratio response, and the optimum values of each processing parameter were obtained using the smaller-the-better quality characteristic. The results show that the optimum values were 60 °C, 40 s, 210 °C, and 600 kN for the mold temperature, compression time, melt temperature, and pressure, respectively. Afterward, a confirmation test was performed to test the optimum parameters. Using analysis of variance (ANOVA), melt temperature was found to be the most significant processing parameter, followed by mold temperature, compression time, and pressure. Taguchi methods ; Mold ; Materials science ; Orthogonal array ; Design of experiments ; Composite material ; Compression molding ; Composite number ; Molding (decorative) ; Thermoplastic ; Process variable ; Compression (physics) ; Process (computing) ; Computer science ; Mathematics Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Injection Molding Process and Properties
Authors: Vladimí­r Mašán | Arkadiusz Matwijczuk | Agnieszka Niemczynowicz | R. A. Kycia | Dariusz Karcz | Bożena Gładyszewska | Lidia Ślusarczyk | Patrik Burg
Source: Scientific Reports (article, 2021-09-28)
Field: Chemistry — Analytical Chemistry

Chemometric approach to characterization of the selected grape seed oils based on their fatty acids composition and FTIR spectroscopy Addressing the issues arising from the production and trade of low-quality foods necessitates developing new quality control methods. Cooking oils, especially those produced from the grape seeds, are an example of food products that often suffer from questionable quality due to various adulterations and low-quality fruits used for their production. Among many methods allowing for fast and efficient food quality control, the combination of experimental and advanced mathematical approaches seems most reliable. In this work a method for grape seed oils compositional characterization based on the infrared (FTIR) spectroscopy and fatty acids profile is reported. Also, the relevant parameters of oils are characterized using a combination of standard techniques such as the Principal Component Analysis, k-Means, and Gaussian Mixture Model (GMM) fitting parameters. Two different approaches to perform unsupervised clustering using GMM were investigated. The first approach relies on the profile of fatty acids, while the second is FT-IR spectroscopy-based. The GMM fitting parameters in both approaches were compared. The results obtained from both approaches are consistent and complementary and provide the tools to address the characterization and clustering issues in grape seed oils. Fourier transform infrared spectroscopy ; Composition (language) ; Characterization (materials science) ; Chemistry ; Chemical composition ; Food science ; Spectroscopy ; Organic chemistry ; Materials science ; Chemical engineering ; Nanotechnology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Meat and Animal Product Quality
Authors: Ilka Wittig | Pedro Felipe Malacarne
Source: International Journal of Molecular Sciences (review, 2021-07-21)
Field: Chemistry — Spectroscopy

Complexome Profiling: Assembly and Remodeling of Protein Complexes Many proteins have been found to operate in a complex with various biomolecules such as proteins, nucleic acids, carbohydrates, or lipids. Protein complexes can be transient, stable or dynamic and their association is controlled under variable cellular conditions. Complexome profiling is a recently developed mass spectrometry-based method that combines mild separation techniques, native gel electrophoresis, and density gradient centrifugation with quantitative mass spectrometry to generate inventories of protein assemblies within a cell or subcellular fraction. This review summarizes applications of complexome profiling with respect to assembly ranging from single subunits to large macromolecular complexes, as well as their stability, and remodeling in health and disease. Proteomics ; Chemistry ; Nucleic acid ; Mass spectrometry ; Biomolecule ; Biochemistry ; Computational biology ; Biology ; Chromatography Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Enzyme Structure and Function
Authors: Youzhi Luo | Keqiang Yan | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2021-02-01)
Field: Materials Science — Materials Chemistry

GraphDF: A Discrete Flow Model for Molecular Graph Generation We consider the problem of molecular graph generation using deep models. While graphs are discrete, most existing methods use continuous latent variables, resulting in inaccurate modeling of discrete graph structures. In this work, we propose GraphDF, a novel discrete latent variable model for molecular graph generation based on normalizing flow methods. GraphDF uses invertible modulo shift transforms to map discrete latent variables to graph nodes and edges. We show that the use of discrete latent variables reduces computational costs and eliminates the negative effect of dequantization. Comprehensive experimental results show that GraphDF outperforms prior methods on random generation, property optimization, and constrained optimization tasks. Latent variable ; Invertible matrix ; Graph ; Modulo ; Computer science ; Algorithm ; Discrete optimization ; Theoretical computer science ; Mathematical optimization ; Mathematics ; Discrete mathematics ; Optimization problem ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods