jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Feng | Susu Zhu | Fu‐Cheng Lin | Zhen-Zhu Su | Kang-pei Yuan | Yiying Zhao | Yong He | Chu Zhang
Source: Sensors (article, 2018-06-15)
Field: Chemistry — Analytical Chemistry

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts. Hyperspectral imaging ; Mold ; Artificial neural network ; VNIR ; Infrared ; Remote sensing ; Environmental science ; Materials science ; Artificial intelligence ; Computer science ; Geology ; Optics ; Composite material ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Menghan Hu | Guangtao Zhai | Yu Zhao | Zhaodi Wang
Source: Scientific Reports (article, 2018-04-23)
Field: Chemistry — Analytical Chemistry

Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data In the current work, we attempt to leverage the fewer wavelengths and samples to develop a classification model for classifying hard and soft blueberries using near infrared (NIR) data. To do this, random frog selection and active learning approaches are used in the spectral space and the sample queue, respectively. To reduce the spectral number, a random frog spectral selection approach was applied to collect wavelengths informative with hardness. Prediction model based on 22 selected spectra gave slightly better results than that based on the full spectra. In terms of the selection operation in the sample space, the query by committee was validated to be suitable for blueberry hardness classification with the accuracy, precision and recall of 78%, 74% and 98% when taking only 25 sample queries. Its standard deviation curves of performance metrics are also located in regions of low values (around 0.05) and fluctuated steadily in shape, winning over those of the other 4 active learning strategies and random method. In summary, the respective uses of random frog and query by committee in the NIR spectral vector and the sample queue showed the considerable potential for establishing a simple but robust classifier for hard and soft blueberries with very low labeling cost. Computer science ; Standard deviation ; Leverage (statistics) ; Random forest ; Sample (material) ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Classifier (UML) ; Artificial intelligence ; Support vector machine ; Statistics ; Machine learning ; Mathematics ; Physics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Asma Nouira | Nataliya Sokolovska | Jean‐Claude Crivello
Source: arXiv (Cornell University) (preprint, 2018-10-26)
Field: Materials Science — Materials Chemistry

CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications. This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data. Unsupervised learning methods such as Generative Adversarial Networks (GANs) can be efficiently used to produce new data. Cross-domain Generative Adversarial Networks were reported to achieve exciting results in image processing applications. However, in the domain of materials science, there is a need to synthesize data with higher order complexity compared to observed samples, and the state-of-the-art cross-domain GANs can not be adapted directly. In this contribution, we propose a novel GAN called CrystalGAN which generates new chemically stable crystallographic structures with increased domain complexity. We introduce an original architecture, we provide the corresponding loss functions, and we show that the CrystalGAN generates very reasonable data. We illustrate the efficiency of the proposed method on a real original problem of novel hydrides discovery that can be further used in development of hydrogen storage materials. Generative grammar ; Computer science ; Domain (mathematical analysis) ; Adversarial system ; Construct (python library) ; Task (project management) ; Theoretical computer science ; Artificial intelligence ; Image (mathematics) ; Generative adversarial network ; Algorithm ; Machine learning ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhuoran Li | Feifan Xu | Zhi Liu | Chuanyu Qin | Hao Ren | Yangxue Li
Source: RSC Advances (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Facile synthesis of novel porous self-assembling hydrogen-bonding covalent organic polymers and their applications towards fluoroquinolone antibiotics adsorption A series of porous hydrogen-bonding covalent organic polymers (H<sub>C</sub>OPs) have been synthesized based on three-composite building blocks through a quick and succinct method for fluoroquinolone antibiotics adsorption from aqueous solutions. The porous properties of the H<sub>C</sub>OPs were regulated and controlled by adjusting the lengths of linkers, and the crystallinity and stability were strengthened due to the introduction of hydrogen bonds in H<sub>C</sub>OPs. Taking advantage of the porous properties and π-conjugated phenyl rings, as well as functional -CO-NH- and -COOH groups, H<sub>C</sub>OPs removed organic pollutants from wastewater effectively and showed good reusability. The external adsorption behavior was analyzed using both kinetic analysis and isotherm analysis. The results showed that the adsorption obeys the pseudo-second order kinetic model and follows the Langmuir isotherm model. The obtained maximum adsorption capacity of the four H<sub>C</sub>OPs was arranged in sequence according to the specific surface areas and pore sizes. Furthermore, the internal mechanisms involving perforated porousness, electrostatic interaction, hydrophobic interaction, π-π electron-donor-acceptor (EDA) interaction and hydrogen bonding formation, were investigated in detail. We envisage broadly applying the H<sub>C</sub>OPs in the facile and effective management of environmental pollution. Hydrogen bond ; Adsorption ; Aqueous solution ; Chemical engineering ; Covalent bond ; Polymer ; Langmuir adsorption model ; Materials science ; Porosity ; Hydrophobic effect ; Crystallinity ; Langmuir ; Chemistry ; Organic chemistry ; Molecule ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Søren Heinze | Andreas T. Echtermeyer
Source: Applied Sciences (article, 2018-11-12)
Field: Materials Science — Materials Chemistry

A Practical Approach for Data Gathering for Polymer Cure Simulations Producing precision parts requires good control of the production parameters. When casting thermoset polymers an understanding of the curing process, with its heat release and associated temperature changes, is important. This paper describes how the cure of a polymer of unknown detailed chemical composition in a large part can be predicted and how the necessary material properties required for the predictions can be obtained. The approach given is a relatively simple method that a part manufacturer can perform. It will not characterize chemical reactions in detail, but it gives sufficient accuracy to describe the process. The procedures will be explained for an example of casting a large block of a filled two-component thermoset polyurethane. The prediction of the degree of cure, the associated heat and temperature increase during the curing of a polymer was successfully done using a standard finite element program with the input parameters reaction energy, the Arrhenius pre-factor and the kinetic function, which describes the chemical reaction. The three parameters could be obtained with standard Differential Scanning Calorimetry (DSC) equipment. The data were analyzed with the model-free isoconversional method combined with the compensation effect. The same set of parameters allowed the prediction of experimental cure behavior over two orders of magnitude of time and at a curing temperature range from room temperature up to 420 K. Thermosetting polymer ; Curing (chemistry) ; Materials science ; Differential scanning calorimetry ; Polymer ; Arrhenius equation ; Thermodynamics ; Activation energy ; Composite material ; Chemistry ; Physical chemistry ; Physics Thermal and Kinetic Analysis ; Epoxy Resin Curing Processes ; Injection Molding Process and Properties
Authors: N. Ouslimani
Source: International Journal of Waste Resources (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Removal of Directs Dyes from Wastewater by Cotton Fiber Waste Environment protection is a precondition for sustained growth and a better quality of life for all peoples on earth. Aqueous industrial effluents are the main sources of pollution. Among the compounds of these effluents, dyes are particularly resistant to discoloration by conventional methods, and discharges present many problems that must be supported. Wastewater ; Fiber ; Waste management ; Pulp and paper industry ; Environmental science ; Chemistry ; Engineering ; Organic chemistry Synthesis of Tetrazole Derivatives ; Water and Wastewater Treatment
Authors: Wayne W. Lukens | Sarah A. Saslow
Source: Dalton Transactions (article, 2018-01-01)
Field: Chemistry — Inorganic Chemistry

Facile incorporation of technetium into magnetite, magnesioferrite, and hematite by formation of ferrous nitrate<i>in situ</i>: precursors to iron oxide nuclear waste forms The fission product, 99Tc, presents significant challenges to the long-term disposal of nuclear waste due to its long half-life, high fission yield, and to the environmental mobility of pertechnetate (TcO4-), the stable Tc species in aerobic environments. Migration of 99Tc from disposal sites can potentially be prevented by incorporating it into durable waste forms based on environmentally stable minerals. Since Tc(iv) and Fe(iii) have the same ionic radius, Tc(iv) can replace Fe(iii) in iron oxides. Environmentally durable iron oxides include goethite (α-FeOOH), hematite (α-Fe2O3), and magnesioferrite (MgFe2O4). The incorporation of Tc into two of these, hematite and magnesioferrite, as well as magnetite (Fe3O4) by means of simple, aqueous chemistry is presented starting from TcO4- in 5 M nitric acid. A combination of X-ray diffraction and X-ray absorption fine structure spectroscopy reveals that Tc(iv) replaces Fe(iii) within the iron oxide structures. Following incorporation, Tc doped samples were suspended in deionized water under aerobic conditions, and the release rates of Tc were determined. The results of this work show that Tc leaches more quickly from Fe3O4 than from α-Fe2O3 or MgFe2O4. Modeling the leach rates and comparison with the leach rate of Tc from TiO2 indicate that release of Tc is controlled by solid state diffusion. Ferrous ; Magnetite ; Hematite ; Nuclear fission product ; Radiochemistry ; Radioactive waste ; Fission products ; Technetium ; Pertechnetate ; Yield (engineering) ; Chemistry ; Fission ; Nitrate ; Fission product yield ; Nitric acid ; Nuclear chemistry ; Inorganic chemistry ; Materials science ; Mineralogy ; Metallurgy ; Nuclear physics ; Physics Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: Xiangwen Chen | Chengyun Hua | Hang Zhang | Navaneetha K. Ravichandran | Austin J. Minnich
Source: Physical Review Applied (article, 2018-11-29)
Field: Materials Science — Materials Chemistry

Quasiballistic Thermal Transport from Nanoscale Heaters and the Role of the Spatial Frequency Engineering heat transport at the nanoscale is important for thermal management in modern technology, but precisely how thermal resistance is affected by geometry and length scale has been a topic of debate. This study identifies a key parameter, the spatial frequency of a heating pattern, that governs thermal transport at the nanoscale. This result provides a means to quantitatively manipulate thermal dissipation at the nanoscale, simply by considering the spatial frequencies of the heating pattern. Nanoscopic scale ; Thermal management of electronic devices and systems ; Thermal ; Dissipation ; Materials science ; Scale (ratio) ; Thermal resistance ; Length scale ; Key (lock) ; Spatial frequency ; Nanotechnology ; Mechanics ; Optics ; Physics ; Mechanical engineering ; Computer science ; Thermodynamics ; Engineering Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: María C. Bernini | Germán E. Gómez | Elena V. Brusau | Griselda E. Narda
Source: Israel Journal of Chemistry (article, 2018-09-24)
Field: Chemistry — Inorganic Chemistry

Reviewing Rare Earth Succinate Frameworks from the Reticular Chemistry Point of View: Structures, Nets, Catalytic and Photoluminescence Applications Abstract The intense development that Metal‐Organic Frameworks have experienced along the last two decades, reflects the interest on these compounds as a new generation of multifunctional materials with diverse applications. The first works of Prof. Omar Yaghi devoted to obtain open frameworks by combining rigid aromatic linkers and a variety of metal centers, encouraged the creativity of the scientific community to designcoordination polymers employing aliphatic ligands as multitopic building blocks . Here, a revision of the literature dedicated to rare earth coordination networks mainly based in succinate ligand and derivatives is performed. A structural analysis of 2D and 3D frameworks based on rare earth elements and succinate, was carried out considering their inner connectivities and topologies with focus on those compounds with potential photoluminescent and catalytic technological applications. Thus, a variety of optical behaviours regarding the emission mechanisms, colours, lifetimes, quantum yields as well as chemical/thermal sensing properties are presented and compared with related phases found in literature. The catalytic performance of several rare earth‐succinates in three important reactions is discussed in terms of their acidity, dimensionality and available active centers towards the formation of the desired products analysing parameters such as selectivity, yields and TOFs. Chemistry ; Catalysis ; Rare earth ; Photoluminescence ; Polymer ; Ligand (biochemistry) ; Variety (cybernetics) ; Nanotechnology ; Combinatorial chemistry ; Topology (electrical circuits) ; Organic chemistry ; Mineralogy ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes
Authors: El‐Sayed I. El‐Desoky | Eman M. Keshk | Aya Atef El‐Sawi | Mohamed Ahmed Abozeid | Laila Abou‐Zeid | Abdel‐Rahman H. Abdel‐Rahman
Source: Saudi Pharmaceutical Journal (article, 2018-03-27)
Field: Chemistry — Organic Chemistry

Synthesis, biological evaluation and in silico molecular docking of novel 1-hydroxy-naphthyl substituted heterocycles The versatile precursor 2-acetyl-4-allyl-1-hydroxy naphthalene was synthesized efficiently via Claisen rearrangement 2-acetyl-1-allyloxynaphthalene. The Claisen-Schmidt condensation of latter precursor afforded the corresponding chalcones which were exploited to synthesize a series of potential heterocycles such as pyrazoline, isoxazoline, benzocoumarin and benzoflavone. The synthesized products showed potent antioxidant and antimicrobial activities. Chalcone <b>3c</b>, naphthyl pyrazoline <b>6b</b> and hydroxycoumarin <b>13</b> exhibited the highest activity as antioxidants. Their binding mode showed specialized recognition of hydroxycoumarin <b>13</b> with the triad key amino acids at the active site of the oxidoreductase enzyme (PDB code 1DXO). 1-Hydroxynaphth-2-yl pyrazoline (<b>6b</b>) revealed the highest efficacy against both Gram positive and negative bacterial species. <i>In silico</i> molecular docking of pyrazoline <b>6b</b> endorsed its proper binding at the active site of the 2EX6 enzyme which explains its potent antibacterial activity in comparison with standard ampicillin. Chalcone ; Claisen rearrangement ; Chemistry ; Pyrazoline ; In silico ; Combinatorial chemistry ; Antimicrobial ; Docking (animal) ; Stereochemistry ; Naphthalene ; Organic chemistry ; Biochemistry Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Synthesis of Organic Compounds
Authors: Jinbo Pan | Qimin Yan
Source: Journal of Semiconductors (review, 2018-07-01)
Field: Materials Science — Materials Chemistry

Data-driven material discovery for photocatalysis: a short review In this short review, we introduce recent progress in the research field of data-driven material discovery and design for solar fuel generation. Construction of material databases under the materials genome initiative provides a great platform for material discovery and design by creating computational screening pipelines based on the materials' descriptors. In the field of solar water splitting, data-driven computational discovery approach has been effective in making material predictions. When combined with synergistic and complimentary experimental efforts, high-throughput computations based on density functional theory showed great predictive power for accelerated discovery of inorganic compounds as functional materials for solar fuel generation. As an example, we introduce the theory–experiment joint discovery of a large set of metal oxide photoanode materials that have been theoretically predicted to be efficient candidates and soon verified by synergistic experimental fabrication and characterization processes. In the field of two-dimensional materials, the application of data-driven approach has realized the prediction of many promising candidates with suitable direct band gaps and optimal band edges for the generation of chemical fuels from sunlight, greatly expanding the number of theoretically predicted 2D photoelectrocatalysts that are awaiting experimental verification. We discuss the challenges for the continued discovery and design of novel bulk and 2D compounds for photocatalysis via a data-driven approach. At the end of this review, we provide a brief outlook for future material discoveries in the field of solar fuel generation. Field (mathematics) ; Computer science ; Solar fuel ; Characterization (materials science) ; Nanotechnology ; Throughput ; Biochemical engineering ; Process engineering ; Systems engineering ; Photocatalysis ; Materials science ; Engineering ; Chemistry ; Telecommunications Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Vincenzo Guarino | G. Ausanio | Vincenzo Iannotti | Luigi Ambrosio | L. Lanotte
Source: eXPRESS Polymer Letters (article, 2018-01-01)
Field: Materials Science — Biomaterials

Electrospun nanofiber tubes with elastomagnetic properties for biomedical use Electrospinning technique has been successfully used to produce composite nanofibers combining magnetic nanoparticles with polymer matrices. Process conditions to assembly nanofibers in tubular systems, as well as their morphological and elastomagnetic properties, have been explored. A volume percentage of magnetic charge close to 30% has been achieved. The optimization of the fabrication method ensures that the particles are completely covered by a thin polymer shell, so safe-guarding bio-compatibility. In particular, the deformation induced by the direct elastomagnetic effect, applying a static or a pulsed magnetic field, has been investigated. Resultant devices exhibit good elastomagnetic stretchability at room temperature-longitudinal relative strain/exciting field ~410 -3 /(1.510 4 A/m) -, thus suggesting their potential use for applications in biomedical field as magneto-active components, as well as sensors and actuators. Materials science ; Nanofiber ; Electrospinning ; Polymer science ; Composite material ; Nanotechnology ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Umar Sa’ad Aliyu | Halimah Mohamed Kamari | Abdulkarim Muhammad Hamza | Abdulbaset A. Abdulla Awshah
Source: Journal of Science and Mathematics Letters (article, 2018-12-15)
Field: Materials Science — Ceramics and Composites

The Structural, Physical and Optical Properties of Borotellurite Glasses Incorporated with Silica from Rice Husk From the agricultural waste of rice (rice husk), high purity SiO2 with about 98.548 % purity was extracted using a very simple room temperature leaching method. Using the rice husk extracted silicate, a system of silicate borotellurite glasses was fabricated with composition equation [(TeO_2)_0.7 (B_2O_3)_0.3]_1-x (SiO_2)_x with x=0.0, 0.1, 0.2, 0.3 and 0.4. The density and molar volume were measured, and Fourier transform infrared (FTIR), X-ray diffraction (XRD) and UV-Vis analyses were performed on the glasses. Both the density and molar volume decreased and the XRD pattern confirmed the glasses’ amorphous nature. The FTIR showed the presence of TeO_3, TeO_4, BO_3, BO_2O, SiO_4, and H_3BO_3 structural units in the glasses. The concentrations of TeO_3, TeO_4, BO_3 and BO_2O structures were determined by the deconvolution of the FTIR spectra using an Origin software. The optical energy band gap, index of refraction, oxygen packing density (OPD), molar refractive index, metallization criterion and polaron radius were determined for the glasses. Based on the glass transparency, high refractive index value (2.3026 and 2.2937) and metallization criterion (0.4109 and 0.4132) of the glasses with x= 0.1 and 0.2 have potential for fiber and optical non-linear applications. Fourier transform infrared spectroscopy ; Refractive index ; Materials science ; Analytical Chemistry (journal) ; Husk ; Amorphous solid ; Band gap ; Silicate ; Mineralogy ; Molar volume ; Nuclear chemistry ; Chemistry ; Optics ; Crystallography ; Physics Glass properties and applications ; Pigment Synthesis and Properties ; Mineralogy and Gemology Studies