jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seiji Takeda | Toshiyuki Hama | Hsiang-Han Hsu | Victoria A. Piunova | Dmitry Yu. Zubarev | Daniel P. Sanders | Jed W. Pitera | Makoto Kogoh | Takumi Hongo | Yenwei Cheng | Wolf Bocanett | Hideaki Nakashika | Akihiro Fujita | Tsuchiya Yuta | Katsuhiko Hino | Kentaro Yano | Shuichi Hirose | Hiroki Toda | Yasumitsu Orii | Daiju Nakano
Source: arXiv (Cornell University) (preprint, 2020-04-24)
Field: Materials Science — Materials Chemistry

Molecular Inverse-Design Platform for Material Industries The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance. However, the extra-vast combinatorial design space of material structures exceeds human experts' capability to explore all, thereby hampering material development. In this paper, we present a material industry-oriented web platform of an AI-driven molecular inverse-design system, which automatically designs brand new molecular structures rapidly and diversely. Different from existing inverse-design solutions, in this system, the combination of substructure-based feature encoding and molecular graph generation algorithms allows a user to gain high-speed, interpretable, and customizable design process. Also, a hierarchical data structure and user-oriented UI provide a flexible and intuitive workflow. The system is deployed on IBM's and our client's cloud servers and has been used by 5 partner companies. To illustrate actual industrial use cases, we exhibit inverse-design of sugar and dye molecules, that were carried out by experimental chemists in those client companies. Compared to general human chemist's standard performance, the molecular design speed was accelerated more than 10 times, and greatly increased variety was observed in the inverse-designed molecules without loss of chemical realism. Workflow ; Computer science ; Combinatorial design ; Inverse ; IBM ; Nanotechnology ; Database ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Jiajia Zhou | Blanca del Rosal | Daniel Jaque | Seiichi Uchiyama | Dayong Jin
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-09-28)
Field: Materials Science — Materials Chemistry

Advances and challenges for fluorescence nanothermometry Fluorescent nanothermometers can probe the change of local temperature in living cells and <em>in vivo</em>. This field attracts major international efforts in developing both temperature-responsive materials and detection procedures to achieve sub-degree temperature resolutions in biosystems. The newly demonstrated nanothermometers have shown superior sensing performance and multifunctionality, which enables state-of-the-art functional imaging techniques towards improved spatial, temporal, and temperature resolutions for monitoring metabolism of intracellular organelles and internal organs. While the technology becomes mature, along with some debatable records, recent studies have also shown that these nanoscopic thermometers could come across possible biased sensing during the fluorescence-based detection. In this review, we introduce the design principle and advances of fluorescence nanothermometry, discuss the scenarios that may lead to biased sensing, analyze the challenges ahead from both fundamental levels and their practical implementations, and propose new directions to improve the mutual understandings of this interdisciplinary research field. Fluorescence ; Computer science ; Physics ; Optics Advanced Nanomaterials in Catalysis ; Polydiacetylene-based materials and applications
Authors: Caiqing Jian | Xinyu Cheng | Jian Zhang | Lihui Wang
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Scalar Coupling Constant Prediction Using Graph Embedding Local Attention Encoder Scalar coupling constant (SCC) plays a key role in the analysis of three-dimensional structure of organic matter, however, the traditional SCC prediction using quantum mechanical calculations is very time-consuming. To calculate SCC efficiently and accurately, we proposed a graph embedding local self-attention encoder (GELAE) model, in which, a novel invariant structure representation of the coupling system in terms of bond length, bond angle and dihedral angle was presented firstly, and then a local self-attention module embedded with the adjacent matrix of a graph was designed to extract effectively the features of coupling systems, finally, with a modified classification loss function, the SCC was predicted. To validate the superiority of the proposed method, we conducted a series of comparison experiments using different structure representations, different attention modules, and different losses. The experimental results demonstrate that, compared to the traditional chemical bond structure representations, the rotation and translation invariant structure representations proposed in this work can improve the SCC prediction accuracy; with the graph embedded local self-attention, the mean absolute error (MAE) of the prediction model in the validation set decreases from 0.1603 Hz to 0.1067 Hz; using the classification based loss function instead of the scaled regression loss, the MAE of the predicted SCC can be decreased to 0.0963 HZ, which is close to the quantum chemistry standard on CHAMPS dataset. Invariant (physics) ; Embedding ; Scalar (mathematics) ; Encoder ; Dihedral angle ; Algorithm ; Computer science ; Graph embedding ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Physics ; Statistics ; Molecule ; Geometry ; Hydrogen bond Machine Learning in Materials Science ; Protein Structure and Dynamics ; Molecular spectroscopy and chirality
Authors: Valentina Matović | Jasna Trbojević‐Stanković | Branislava Jeftić | Lidija Matija
Source: Srpski arhiv za celokupno lekarstvo (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Glucose concentration monitoring using near-infrared spectrum of spent dialysis fluid in hemodialysis patients Introduction/Objective. Diabetic nephropathy leading to end-stage renal disease is a major health problem worldwide. Hemodialysis (HD) treatment is associated with glycemia variations. Diabetic patients on HD might benefit from a non-invasive online glycemia monitoring system. The aim of this study was to assess the glucose concentration from the matrix of the spent dialysate fluid using near-infrared (NIR) spectroscopy. Methods. Blood samples and spent dialysate were collected in the 15th minute of the HD treatment from 15 patients. The spent dialysis fluid was characterized by a NIR spectrometer in the range of 900?1300 nm. In order to apply the artificial neural network (ANN) and train it, the MATLAB NFTOOL program was used. The testing and training of the ANN were executed using the NIR spectrum of the spent dialysis fluid as input, and the glucose concentration as output. Results. A significant correlation in excess of 93% between the NIR spectrum of the spent dialysate and the blood glucose concentration (3?9 mmol/l) was found. Conclusions. NIR spectroscopy is a non-invasive and reliable method of glycemia monitoring which can be used in maintaining HD patients. Medicine ; Hemodialysis ; Dialysis ; Diabetic nephropathy ; Dialysis adequacy ; Internal medicine ; Biomedical engineering ; Kidney Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Alauddin Ahmed
Source: ChemRxiv (preprint, 2020-12-22)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Guided Equations for Super-Fast Prediction of Methane Storage Capacities of COFs Covalent organic framework (COF) is a prominent class of nanoporous materials under consideration for vehicular methane storage. However, evaluating a COF for its methane capacity involves multiple experimental or computational steps, which is expensive and time consuming. Consequently, the discovery of high-capacity COFs for methane storage is very slow. Here we developed equations for super-fast prediction of deliverable methane capacities of COFs from a small number (3 to 7) of physically meaningful and measurable crystallographic features. We provided a set of equations with different fidelities for on-demand predictions based on the accessibility of crystallographic features. We found that an equation with only three crystallographic primary features, as variables, can predict deliverable capacities of 84,800 COFs with a root-mean-square error (RMSE) of 10 cm&lt;sup&gt;3&lt;/sup&gt; (standard temperature and pressure, STP) cm&lt;sup&gt;-3&lt;/sup&gt; and mean absolute percentage error (MAPE) of 5%. However, the highest fidelity equation developed here contains seven crystallographic primary features of COFs with RMSE and MAPE of 8.1 cm&lt;sup&gt;3&lt;/sup&gt; (STP) cm&lt;sup&gt;-3&lt;/sup&gt; and 4.2%, respectively. With that, we predicted methane storage capacities of 468,343 previously unexplored COFs using the highest fidelity equation and identified several hundred promising candidates with record-setting performance. CUBE_PBB_BA2, a hypothetical COF not yet synthesized, sets the new record of balancing gravimetric (0.396 g g-1) and volumetric (221 cm&lt;sup&gt;3&lt;/sup&gt; (STP) cm&lt;sup&gt;-3&lt;/sup&gt;) deliverable methane storage capacities under the pressure swing between 65 and 5.8 bar at 298K. Also, 3D-HNU5, a previously synthesized COF, has shown the potential to achieve the gravimetric and volumetric methane storage U.S. Department of Energy target (0.5 g g&lt;sup&gt;-1&lt;/sup&gt; and 315 cm&lt;sup&gt;3&lt;/sup&gt; (STP) cm&lt;sup&gt;-3&lt;/sup&gt;) simultaneously with uptakes of 0.755 g g&lt;sup&gt;-1&lt;/sup&gt; and 334 cm&lt;sup&gt;3&lt;/sup&gt; (STP) cm&lt;sup&gt;-3&lt;/sup&gt; at 100 bar/270 K. Methane ; Mean squared error ; Gravimetric analysis ; Deliverable ; Nanoporous ; Mean absolute percentage error ; Chemistry ; Mathematics ; Computer science ; Statistics ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Tesfamariam Teklu Gebretsadik | Teklit Gebremedhin
Source: Journal of Metals Materials and Minerals (article, 2020-03-26)
Field: Chemistry — Analytical Chemistry

Structural studies of natural zeolites and optimizations for water hardness softening media This study was aimed at investigating the mineralogical composition and structural features of natural zeolites using XRF, XRD, TGA, FT-IR and SEM. Natural zeolites were subjected to acid-activation and subsequently with NaCl salt to enhance surface area for water hardness softening applications. Peaks around 3560 and 1650 cm-1 belongs to Oâ€“H stretching and Oâ€“H bending vibrations which confirmed the presence of moisture within zeolite structures. This result was further supported by evolution of moisture above 108Â°C from thermogravimetric analysis. Fibrous structures which are full of rough texture were examined by SEM. The XRD pattern confirmed Stellerite and Barrerite compositions were dominant crystal structures. The mineralogical analysis determined by XRF showed SiO2, Al2O3 and CaO were dominant oxides. The specific surface area of raw and acid-activated zeolites was estimated by the Searsâ€™ method and hence, the experimental values are 10.2 m2Â·g-1 and 35.8 m2Â·g-1, respectively. Batch adsorption parameters such as contact time, pH, adsorbent dose and initial concentrations were studied and optimized. Maximum adsorption capacity (8.04 mgÂ·g-1) was recorded at optimum conditions. The experimental data was fitted-well with Freundlich isotherm model. Thermogravimetric analysis ; Softening ; Zeolite ; Adsorption ; Materials science ; Freundlich equation ; Water softening ; Moisture ; Texture (cosmology) ; Mineralogy ; Chemical engineering ; Composite material ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Catalysis Spectroscopy and Chemometric Analyses
Authors: Erik V. Johnstone | Daniel J. Bailey | Sebastian M. Lawson | Martin C. Stennett | Claire L. Corkhill | Minseok Kim | Jong Heo | Daiju Matsumura | Neil C. Hyatt
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Synthesis and characterization of iodovanadinite using PdI<sub>2,</sub> an iodine source for the immobilisation of radioiodine The synthesis of a palladium-containing iodovanadinite derivative, hypothetically "PdPb<sub>9</sub>(VO<sub>4</sub>)<sub>6</sub>I<sub>2</sub>", was attempted using PdI<sub>2</sub> as a source of iodine in searching for a novel waste form for radioiodine. Stoichiometric amounts of Pb<sub>3</sub>(VO<sub>4</sub>)<sub>2</sub> and PdI<sub>2</sub> were batched and reacted at elevated temperatures in sealed vessels. Batched material was also subjected to high-energy ball-milling (HEBM) in order to reduce reaction time and the potential for iodine volatilization during subsequent reaction at 200-500 °C. The resulting products were characterized using X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray analysis, IR spectroscopy, thermal analysis and Pd K XANES. Results showed that PdI<sub>2</sub> can function as a sacrificial iodine source for the formation of iodovanadinite, prototypically Pb<sub>10</sub>(VO<sub>4</sub>)<sub>6</sub>I<sub>2</sub>, however, the incorporation of Pd into this phase was not definitively observed. The sacrificial reaction mechanism involved the decomposition of PdI<sub>2</sub> to Pd metal and nascent I<sub>2</sub>, with the latter incorporated into the iodovanadinite Pb<sub>10</sub>(VO<sub>4</sub>)<sub>6</sub>I<sub>2</sub> phase. In comparison to processing using standard solid state reaction techniques, the use of HEBM prior to high temperature reaction generates a more homogeneous end-product with better iodine retention for this system. Overall, the key novelty and importance of this work is in demonstrating a method for direct immobilisation of undissolved PdI<sub>2</sub> from nuclear fuel reprocessing, in a composite wasteform in which I-129 is immobilised within a durable iodovandinite ceramic, encapsulating Pd metal. Iodine ; Characterization (materials science) ; Chemistry ; Radiochemistry ; Nuclear chemistry ; Nanotechnology ; Materials science ; Organic chemistry Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: H. Cherrad | M. Addou | Mehdi Hssein | K. Bahedi | Mohamed Jbilou | A. Mrigal | Elmehdi Salmani | Mustapha Rouchdi | Ahmed Mezred | Hajar Ftouhi | Mustapha Diani | Mohamed El Jouad
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Theoretical and experimental Investigation of Structural, electronic and optical properties of neodymium doped ZnO The morphological and cathodoluminescent properties of Nd doped zinc oxide thin films deposited by reactive chemical pulverization spray pyrolysis technique on heated glass substrates at 450 °C are being reported. The films were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and cathodoluminescent (CL) spectroscopy. We have also used the Generalised Gradient Approximation (GGA)-the Self Interaction Corrected (SIC) to determine Electronic structure and x ray absorption. Scanning electron microscope ; Materials science ; Zinc ; Doping ; Neodymium ; Spray pyrolysis ; Spectroscopy ; Analytical Chemistry (journal) ; Diffraction ; Thin film ; Nanotechnology ; Chemistry ; Optoelectronics ; Optics ; Laser ; Metallurgy ; Composite material ZnO doping and properties ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Y.T. Wijaya | Adi Yulandi | A.W. Gunawan | Yanti Yanti
Source: Food Research (article, 2020-01-15)
Field: Chemistry — Organic Chemistry

In silico study of anthocyanin and ternatin flavonoids for the treatment of inflammation-related diseases using molecular docking analysis Inflammatory markers such as cyclooxygenase (COX)-2, inducible nitric oxide synthase (iNOS), myeloperoxidase (MPO), and prostaglandin (PEG) are widely known as major targets in discovering natural anti-inflammatory drugs for the treatment of inflammationrelated diseases. Non-steroidal anti-inflammatory drugs (NSAIDs) such as ibuprofen and aspirin are mostly used at present, however, some NSAIDS have been reported to cause gastrointestinal side effect due to ligand-protein interaction. Molecular docking is a promising tool to study such modes of interaction. In this study, we evaluated the potential use of anthocyanin and ternatin flavonoids as natural anti-inflammatory agents for treatment of inflammatory-related diseases using in silico molecular docking assay. Automated docking study using Protein-Ligand ANT System (PLANTS) and AutoDock Vina was performed with various ligand molecules, including ibuprofen, anthocyanin, and ternatin against the protein crystal structures of COX-1, COX-2, iNOS, and MPO. The in silico data demonstrated that ibuprofen bound effectively to the active site of COX-1 and MPO with minimum binding energy, yet the compound required more energy to bind the active site of COX-2. Ternatin flavonoid was bound to COX-2 and iNOS with minimum binding energy. In terms of binding energy, anthocyanin flavonoid was found to be effective for inhibiting COX-1, COX-2, and iNOS. These results suggested that anthocyanin and ternatin flavonoids may potentially be developed as anti-inflammatory drug candidate for the treatment of inflammatory-related diseases. Chemistry ; Docking (animal) ; In silico ; Pharmacology ; Myeloperoxidase ; AutoDock ; Biochemistry ; Flavonoid ; Anthocyanin ; Ibuprofen ; Inflammation ; Biology ; Medicine ; Immunology ; Antioxidant Synthesis and biological activity ; Computational Drug Discovery Methods ; Phytochemicals and Antioxidant Activities
Authors: A. V. Shchegolkov | Ф. Ф. Комаров | И. Д. Парфимович | O. V. Milchanin | А. V. Shchegolkov | Анастасия Витальевна Хробак | A. V. Semenkova
Source: Vektor nauki Tol yattinskogo gosudarstvennogo universiteta (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

The influence of carbon nanotubes on the electric conductivity of thermosetting plastics and elastomers In the technology of electricity-conducting polymer composites, the up-to-date topic is the application of modifiers with nanoscale geometric parameters. Such materials are both single-wall and multi-wall carbon nanotubes. The use of multi-wall carbon nanotubes as additives to polymers allows achieving good results associated with the electrical conductivity manifestation at the low percentage ratios of the introduced carbon nanotubes. The study considered two different types of polymers: epoxy resin and silicone. For modification, the authors used the multilayer carbon nanotubes (MCNT). For silicone, the authors studied the electrical conductivity at the mechanical deformations, in particular, at torsion with different twist angles. The study considered the influence of MCNT with different bulk density on the electrical conductivity of composites. At the 100 Hz measurement frequencies for identical additive concentrations with 1, 2, 4, and 8 wt.% in composites with the “Taunit-M” MCNT, the conductivity is about by 2 orders of magnitude larger than in composites with “Taunit” MCNT. A polymer modified by an MCNT with a specific surface area of 202.3 m 2 /g has a lower specific resistivity (3.8 × 10 5 Ohm×cm). The MCNT modifier with a specific surface area of 202.3 m 2 /g, which is smaller in comparison with other types of MCNT, and a bulk density of 42.6 kg/m 3 allows obtaining the lowest electrical resistivity. An MCNT with a high specific surface area of 541.5 m 2 /g causes the formation of electrical conductivity by 2 orders of magnitude lower than an MCNT with a specific surface area of 202.3 m 2 /g. The study identified that at the mass content of MCNT 1.5 and 2 mass.%, the composites are characterized by an increase in electrical resistivity at torsion angles from 0 to 900°. Materials science ; Carbon nanotube ; Composite material ; Electrical resistivity and conductivity ; Thermosetting polymer ; Conductivity ; Epoxy ; Silicone ; Polymer ; Elastomer Carbon Nanotubes in Composites
Authors: Yufei Han | Qian Zhuang | Bo Sun | Wenping Lv | Sheng Wang | Qingjie Xiao | Bin Pang | Youli Zhou | Fuxing Wang | Pengliang Chi | Qisheng Wang | Zhen Li | Lizhe Zhu | Fuping Li | Dong Deng | Ying‐Chih Chiang | Zhenfei Li | Ruobing Ren
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-07-23)
Field: Chemistry — Spectroscopy

Crystal structure of steroid reductase SRD5A reveals conserved steroid reduction mechanism Abstract Steroid hormones are essential in stress response, immune system regulation, and reproduction in mammals. Steroids with 3-oxo-Δ 4 structure, such as testosterone, androstenedione and progesterone, could be catalyzed by steroid 5α-reductases (SRD5As) to generate their corresponding 3-oxo-5α steroids, which are essential for multiple physiological and pathological processes. Abnormal activities of SRD5As will lead to benign prostatic hyperplasia, alopecia, prostatic cancer or infertility due to the poor quality of sperms. However, the detailed reduction mechanisms of SRD5As remain elusive. Here we report the crystal structure of PbSRD5A, which shares 60.6% and 51.5% sequence similarities with human SRD5A1 and −2 respectively, from Proteobacteria bacterium in complex with the cofactor NADPH at 2.0 Å resolution. PbSRD5A exists as a monomer comprised of seven transmembrane segments (TMs). The TM1-4 enclose a hydrophobic cavity for steroids substrates binding, whereas TM5-7 coordinate with cofactor NADPH through extensive hydrogen bonds network. Homology-based structural models of HsSRD5A1 and −2, together with extensive biochemical characterizations, for the first time unveiled the substrate recognition of SRD5As and provide an important framework for further understanding of the mechanism of NADPH mediated steroids 3-oxo-Δ 4 reduction. Based on these analyses, the design of therapeutic molecules targeting SRD5As with improved specificity and therapeutic efficacy would be possible. One Sentence Summary Structural and biochemical characterizations decipher the evolutionarily conserved mechanism in steroid 5α-reductases catalyzing NADPH mediated steroids reduction. Steroid ; Cofactor ; Oxidoreductase ; Biology ; Reductase ; Biochemistry ; Chemistry ; Cell biology ; Hormone ; Enzyme Mass Spectrometry Techniques and Applications ; Hormonal and reproductive studies ; Aldose Reductase and Taurine
Authors: Manoj Kumar Baghel | Inampudi Sailaja | Ivvala Anand Shaker
Source: International Journal of Current Research and Review (article, 2020-01-01)
Field: Materials Science — Biomaterials

Nanotechnology: A Curative Approach to Combat HIV-AIDS Human immunodeficiency virus (HIV) ; Nanotechnology ; Medicine ; Virology ; Materials science Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Tim Hsu | William K. Epting | Hokon Kim | Harry Abernathy | Gregory Hackett | Anthony D. Rollett | A. Salvador | Elizabeth A. Holm
Source: JOM (preprint, 2020-12-02)
Field: Materials Science — Materials Chemistry

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials Microstructure ; Tortuosity ; Materials science ; Generative grammar ; Computer science ; Artificial intelligence ; Geometry ; Composite material ; Mathematics ; Porosity Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Domain Adaptation and Few-Shot Learning