jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rustem Zairov | Alexey P. Dovzhenko | Kirill A. Sarkanich | Irek R. Nizameev | Andrey V. Luzhetskiy | Svetlana N. Sudakova | Sergey N. Podyachev | В. А. Бурилов | Ivan Vatsouro | Alberto Vomiero | Asiya R. Mustafina
Source: Preprints.org (preprint, 2021-10-28)
Field: Materials Science — Materials Chemistry

Single Excited Dual Band Luminescent Hybrid Carbon Dots-terbium Chelate Nanothermometer The report introduces hybrid polyelectrolyte-stabilized colloids combining blue and green emitting building blocks, that are citrate carbon dots (CDs) and [TbL]+ chelate complexes with 1,3-diketonate derivatives of calix[4]arene. The joint incorporation of green and blue emitting blocks into the polysodium polystyrenesulfonate (PSS) aggregates is carried out through the solvent-exchange synthetic technique. The coordinative binding between Tb3+ centers and CD surface groups in initial DMF solutions both facilitates joint incorporation of [TbL]+ complexes and the CDs into the PSS-based nanobeads and affects fluorescence properties of [TbL]+ complexes and CDs, as well as their ability to temperature sensing. The variation of the synthetic conditions is represented herein as a tool for tuning the fluorescent response of the blue and green emitting blocks upon heating and cooling. The revealed regularities enable developing either dual band luminescent colloids for monitoring temperature changes within 25-50 C through double color emission, or to transform the colloids into ratiometric temperature sensors via simple concentration variation of [TbL]+ and CDs in initial DMF solution. Novel hybrid carbon dots-terbium chelate PSS-based nanoplatform opens an avenue for new generation of sensitive and customizable single excited dual band nanothermometers. Terbium ; Fluorescence ; Luminescence ; Materials science ; Excited state ; Colloid ; Chelation ; Carbon fibers ; Quantum dot ; Nanotechnology ; Photochemistry ; Chemical engineering ; Chemistry ; Optoelectronics ; Composite number ; Organic chemistry Carbon and Quantum Dots Applications ; Gas Sensing Nanomaterials and Sensors ; Electrochemical sensors and biosensors
Authors: Omar Benzine | Zhiwen Pan | Courtney Calahoo | Michał Boćkowski | Morten M. Smedskjær | Walter Schirmacher | Lothar Wondraczek
Source: Scientific Reports (article, 2021-12-27)
Field: Materials Science — Ceramics and Composites

Vibrational disorder and densification-induced homogenization of local elasticity in silicate glasses We report the effect of structural compaction on the statistics of elastic disorder in a silicate glass, using heterogeneous elasticity theory with the coherent potential approximation (HET-CPA) and a log-normal distribution of the spatial fluctuations of the shear modulus. The object of our study, a soda lime magnesia silicate glass, is compacted by hot-compression up to 2 GPa (corresponding to a permanent densification of ~ 5%). Using THz vibrational spectroscopic data and bulk mechanical properties as inputs, HET-CPA evaluates the degree of disorder in terms of the length-scale of elastic fluctuations and the non-affine part of the shear modulus. Permanent densification decreases the extent of non-affine elasticity, resulting in a more homogeneous distribution of strain energy, while also decreasing the correlation length of elastic heterogeneity. Complementary <sup>29</sup>Si magic angle spinning NMR spectroscopic data provide a short-range rationale for the effect of compression on glass structure in terms of a narrowing of the Si-O-Si bond-angle and the Si-Si distance. Silicate ; Elasticity (physics) ; Homogenization (climate) ; Silicate glass ; Materials science ; Composite material ; Chemical engineering ; Biology ; Engineering Glass properties and applications ; Material Dynamics and Properties ; Building materials and conservation
Authors: Shashikant Kumar | Edgar Josué Landinez Borda | Babak Sadigh | Siya Zhu | Sebastian Hamel | Brian Gallagher | Vasily V. Bulatov | John E. Klepeis | Amit Samanta
Source: The Journal of Chemical Physics (article, 2021-12-14)
Field: Materials Science — Materials Chemistry

Accurate parameterization of the kinetic energy functional The absence of a reliable formulation of the kinetic energy density functional has hindered the development of orbital free density functional theory. Using the data-aided learning paradigm, we propose a simple prescription to accurately model the kinetic energy density of any system. Our method relies on a dictionary of functional forms for local and nonlocal contributions, which have been proposed in the literature, and the appropriate coefficients are calculated via a linear regression framework. To model the nonlocal contributions, we explore two new nonlocal functionals-a functional that captures fluctuations in electronic density and a functional that incorporates gradient information. Since the analytical functional forms of the kernels present in these nonlocal terms are not known from theory, we propose a basis function expansion to model these seemingly difficult nonlocal quantities. This allows us to easily reconstruct kernels for any system using only a few structures. The proposed method is able to learn kinetic energy densities and total kinetic energies of molecular and periodic systems, such as H<sub>2</sub>, LiH, LiF, and a one-dimensional chain of eight hydrogens using data from Kohn-Sham density functional theory calculations for only a few structures. Density functional theory ; Kinetic energy ; Orbital-free density functional theory ; Statistical physics ; Hybrid functional ; Function (biology) ; Energy functional ; Energy (signal processing) ; Time-dependent density functional theory ; Physics ; Simple (philosophy) ; Computer science ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Dakshinamurthy Sivakumar | Matthias Stein
Source: Biomolecules (article, 2021-05-28)
Field: Chemistry — Organic Chemistry

Binding of SARS-CoV Covalent Non-Covalent Inhibitors to the SARS-CoV-2 Papain-Like Protease and Ovarian Tumor Domain Deubiquitinases The urgent need for novel and effective drugs against the SARS-CoV-2 coronavirus pandemic has stimulated research worldwide. The Papain-like protease (PLpro), which is essential for viral replication, shares a similar active site structural architecture to other cysteine proteases. Here, we have used representatives of the Ovarian Tumor Domain deubiquitinase family OTUB1 and OTUB2 along with the PLpro of SARS-CoV-2 to validate and rationalize the binding of inhibitors from previous SARS-CoV candidate compounds. By forming a new chemical bond with the cysteine residue of the catalytic triad, covalent inhibitors irreversibly suppress the protein’s activity. Modeling covalent inhibitor binding requires detailed knowledge about the compounds’ reactivities and binding. Molecular Dynamics refinement simulations of top poses reveal detailed ligand-protein interactions and show their stability over time. The recently discovered selective OTUB2 covalent inhibitors were used to establish and validate the computational protocol. Structural parameters and ligand dynamics are in excellent agreement with the ligand-bound OTUB2 crystal structures. For SARS-CoV-2 PLpro, recent covalent peptidomimetic inhibitors were simulated and reveal that the ligand-protein interaction is very dynamic. The covalent and non-covalent docking plus subsequent MD refinement of known SARS-CoV inhibitors into DUBs and the SARS-CoV-2 PLpro point out a possible approach to target the PLpro cysteine protease from SARS-CoV-2. The results show that such an approach gives insight into ligand-protein interactions, their dynamic character, and indicates a path for selective ligand design. Virology ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Papain ; Covalent bond ; Coronavirus disease 2019 (COVID-19) ; Protease ; 2019-20 coronavirus outbreak ; Coronavirus ; Biology ; Enzyme ; Chemistry ; Medicine ; Biochemistry ; Pathology Click Chemistry and Applications ; Computational Drug Discovery Methods ; Research on Leishmaniasis Studies
Authors: Mehmet Aziz Yirik | Kumsal Ecem Colpan | Saskia Schmidt | Maria Sorokina | Christoph Steinbeck
Source: Preprints.org (preprint, 2021-11-29)
Field: Chemistry — Physical and Theoretical Chemistry

Review on Chemical Graph Theory and Its Application in Computer-Assisted Structure Elucidation The chemical graph theory is a subfield of mathematical chemistry which applies classic graph theory to chemical entities and phenomena. Chemical graphs are main data structures to represent chemical structures in cheminformatics. Computable properties of graphs lay the foundation for (quantitative) structure activity and structure property predictions - a core discipline of cheminformatics. It has a historic relevance for natural sciences, such as chemistry, biochemistry and biology, and is in the heart of modern disciplines, such as cheminformatics and bioinformatics. This review first covers the history of chemical graph theory, then provides an overview of its various techniques and applications for CASE, and finally summarises modern tools using chemical graph theory for CASE. Cheminformatics ; Graph theory ; Computer science ; Graph ; Theoretical computer science ; Property (philosophy) ; Mathematical chemistry ; Chemistry ; Data science ; Mathematics ; Computational chemistry ; Epistemology ; Combinatorics History and advancements in chemistry ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Aija Trimdale-Deksne | Anatoly Mishnev | Agris Be̅rziņš
Source: Pharmaceutics (article, 2021-05-16)
Field: Materials Science — Materials Chemistry

Combined Use of Structure Analysis, Studies of Molecular Association in Solution, and Molecular Modelling to Understand the Different Propensities of Dihydroxybenzoic Acids to Form Solid Phases The arrangement of hydroxyl groups in the benzene ring has a significant effect on the propensity of dihydroxybenzoic acids (diOHBAs) to form different solid phases when crystallized from solution. All six diOHBAs were categorized into distinctive groups according to the solid phases obtained when crystallized from selected solvents. A combined study using crystal structure and molecule electrostatic potential surface analysis, as well as an exploration of molecular association in solution using spectroscopic methods and molecular dynamics simulations were used to determine the possible mechanism of how the location of the phenolic hydroxyl groups affect the diversity of solid phases formed by the diOHBAs. The crystal structure analysis showed that classical carboxylic acid homodimers and ring-like hydrogen bond motifs consisting of six diOHBA molecules are prominently present in almost all analyzed crystal structures. Both experimental spectroscopic investigations and molecular dynamics simulations indicated that the extent of intramolecular bonding between carboxyl and hydroxyl groups in solution has the most significant impact on the solid phases formed by the diOHBAs. Additionally, the extent of hydrogen bonding with solvent molecules and the mean lifetime of solute-solvent associates formed by diOHBAs and 2-propanol were also investigated. Hydrogen bond ; Chemistry ; Intramolecular force ; Molecule ; Molecular dynamics ; Solvent ; Ring (chemistry) ; Benzene ; Crystal structure ; Crystal (programming language) ; Computational chemistry ; Crystallography ; Stereochemistry ; Organic chemistry Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Crystallography and molecular interactions
Authors: Konstantinos Karvanis | Soňa Rusnáková | Ondřej Krejčí | Alena Kalendová
Source: REVIEWS ON ADVANCED MATERIALS SCIENCE (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Thermal analysis of postcured aramid fiber/epoxy composites Abstract In this study, aramid fiber-reinforced polymer (AFRP) composites were prepared and then postcured under specific heating/cooling rates. By dynamic mechanical analysis, the viscoelastic properties of the AFRP composites at elevated temperatures and under various frequencies were determined. Thermomechanical analysis (TMA), in the modes of creep-recovery and stress–relaxation tests, was also performed. Furthermore, differential scanning calorimetry was also used, and the decomposition of the AFRP composites, aramid fibers, and pure postcured epoxy, in two different atmospheres, namely, air atmosphere and nitrogen (N 2 ) atmosphere, was explored by the thermogravimetric analysis (TGA). From this point of view, the aramid fibers showed remarkably thermal resistance, in N 2 atmosphere, and the volume fraction of fibers (Φ f ) was calculated to be Φ f = 51%. In the TGA experiments, the postcured AFRP composites showed very good thermal resistance, both in air and N 2 atmosphere, and this characteristic in conjunction with their relatively high T g , which is in the range of 85–95°C, depending on the frequency and the determination method, classifies these composites as potential materials in applications where the resistance in high temperatures is a required characteristic. Materials science ; Aramid ; Thermogravimetric analysis ; Composite material ; Epoxy ; Thermomechanical analysis ; Differential scanning calorimetry ; Fiber ; Thermal analysis ; Creep ; Thermal ; Thermal expansion ; Chemical engineering Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Mechanical Behavior of Composites
Authors: Stefan Immel | Matthias Köck | Michael Reggelin
Source: Marine Drugs (article, 2021-12-22)
Field: Chemistry — Spectroscopy

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data. Diastereomer ; Chirality (physics) ; Bayesian inference ; Intramolecular force ; Statistical physics ; Anisotropy ; Bayesian probability ; Chemistry ; Physics ; Geometry ; Molecular physics ; Mathematics ; Computer science ; Stereochemistry ; Artificial intelligence ; Quantum mechanics Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Zhihua Zhao | Yin-Ping Wang | Céline Delmas | Christophe Mingotaud | Jean‐Daniel Marty | Myrtil L. Kahn
Source: Nanoscale Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Mechanistic insights into the anisotropic growth of ZnO nanoparticles deciphered through 2D size plots and multivariate analysis The control and understanding of the nucleation and growth of nano-objects are key points for improving and/or considering the new applications of a given material at the nanoscale. Mastering the morphology is essential as the final properties are drastically affected by the size, shape, and surface structure. Yet, a number of challenges remain, including evidencing and understanding the relationship between the experimental parameters of the synthesis and the shape of the nanoparticles. Here we analyzed jointly and in detail the formation of anisotropic ZnO nanoparticles under different experimental conditions by using two different analytical tools enabling the analysis of TEM images: 2D size plots and multivariate statistical analysis. Well-defined crystalline ZnO nanorods were obtained through the hydrolysis of a dicyclohexyl zinc precursor in the presence of a primary fatty amine. Such statistical tools allow one to fully understand the effect of experimental parameters such as the hydrolysis rate, the mixing time before hydrolysis, the length of the ligand aliphatic chain, and the amount of water. All these analyses suggest a growth process by oriented attachment. Taking advantage of this mechanism, the size and aspect ratio of the ZnO nanorods can be easily tuned. These findings shed light on the relative importance of experimental parameters that govern the growth of nano-objects. This general methodological approach can be easily extended to any type of nanoparticle. Nucleation ; Multivariate statistics ; Nanoparticle ; Nanoscopic scale ; Nanotechnology ; Materials science ; Anisotropy ; Key (lock) ; Biological system ; Multivariate analysis ; Computer science ; Chemical physics ; Chemistry ; Physics ; Optics ; Machine learning ; Biology ; Thermodynamics ZnO doping and properties ; Quantum Dots Synthesis And Properties ; Copper-based nanomaterials and applications
Authors: Iseult Lynch | Antreas Afantitis | Dario Greco | Mária Dušinská | Miguel Á. Bañares | Georgia Melagraki
Source: Nanomaterials (article, 2021-01-07)
Field: Materials Science — Materials Chemistry

Editorial for the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance Ensuring the safe and responsible use of nanotechnologies and nanoscale materials is imperative to maximize consumer confidence and drive commercialization of nano-enabled products that underpin innovation and advances in every industrial sector [...]. Commercialization ; Corporate governance ; Risk governance ; Business ; Risk analysis (engineering) ; Nanotechnology ; Materials science ; Marketing Nanoparticles: synthesis and applications
Authors: Li Wang | Qinqin Wang | Yuanzhong Wang | Yunmei Wang
Source: Journal of Analytical Methods in Chemistry (article, 2021-07-21)
Field: Chemistry — Analytical Chemistry

Comparison of Geographical Traceability of Wild and Cultivated Macrohyporia cocos with Different Data Fusion Approaches Poria originated from the dried sclerotium of <i>Macrohyporia cocos</i> is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated <i>M. cocos</i>, this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of <i>M. cocos</i>. Therefore, we traced the origin of <i>M. cocos</i> from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques, like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid-, and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method-Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis-were considered in data fusion. The results indicate the following: (1) The difference between wild and cultivated samples did exist in terms of the content analysis of vital chemical components and fingerprint analysis. (2) Wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The mid-level Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification. Principal component analysis ; Traceability ; Pattern recognition (psychology) ; Artificial intelligence ; Linear discriminant analysis ; Mathematics ; Partial least squares regression ; Data mining ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Andries J. Daniels | Carlos Poblete-Echeverría | H.H. Nieuwoudt | Nicolene Botha | Umezuruike Linus Opara
Source: Frontiers in Plant Science (article, 2021-10-29)
Field: Chemistry — Analytical Chemistry

Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks Table grape browning is a complex physiological disorder that occurs during cold storage. There is a need to investigate novel and innovative ways to manage the problem that hampers the progressive and sustainable growth of table grape industries. Given the complex nature of the browning phenomenon, techniques such as near-infrared (NIR) spectroscopy can be utilized for the non-destructive classification of different browning phenotypes. In this study, NIR coupled with partial least squares discriminant analysis (PLS-DA) and artificial neural networks (ANN) were used to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact 'Regal Seedless' table grape bunches that were cold-stored over different periods. Friction browning appears as circular spots close to the pedicel area that are formed when table grape berries move against each other, and chocolate browning appears as discoloration, which originates mostly from the stylar-end of the berry, although the whole berry may appear brown in severe instances. The evaluation of the models constructed using PLS-DA was done using the classification error rate (CER), specificity, and sensitivity and for the models constructed using ANN, the kappa score was used. The CER for chocolate browning (25%) was better than that of friction browning (46%) for weeks 3 and 4 for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 for friction browning were not as good as that of chocolate browning. With ANN, the kappa score was tested to classify table grape bunches as clear or having chocolate browning or friction browning and showed that chocolate browning could be classified with a strong agreement during weeks 3 and 4 and weeks 5 and 6 and that friction browning could be classified with a moderate agreement during weeks 3 and 4. These results open up new possibilities for the development of quality checks of packed table grape bunches before export. This has a significant impact on the table grape industry for it will now be possible to evaluate bunches non-destructively during packaging to determine the possibility of these browning types being present when reaching the export market. Browning ; Table grape ; Table (database) ; Berry ; Horticulture ; Chemistry ; Food science ; Artificial intelligence ; Botany ; Mathematics ; Biology ; Computer science ; Data mining Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Fermentation and Sensory Analysis