jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arijit Chowdhury | Sanjay Kimbahune | Kavya Gupta | Brojeshwar Bhowmick | Shalini Mukhopadhyay | Mithun B.S. | Sujit Shinde | Karan Bhavsar
Source:  (article, 2018-05-15)
Field: Chemistry — Analytical Chemistry

Non-destructive method to detect artificially ripened banana using hyperspectral sensing and RGB imaging Fruits provide essential nutrition in most natural form suitable for human beings. They are best when ripened naturally. However, industrialization has provided many ways for quick ripening and for extended shelf life of fruits. Detection of artificial ripening could be done by sophisticated methods like chemical analysis in lab or visual inspection by experts, which may not be feasible all the time. Of all the fruits, banana is the most consumed fruit around the world. Adulteration of banana can have devastating effects on masses on scale. It is figured, bananas are potentially ripened using carcinogens like Calcium Carbide(CaC<sub>2</sub>). In this paper, we propose and devise a novel and automatic method to classify the naturally and artificially ripened banana using spectral and RGB data. Our results show that using a Deep Learning (Neural Network) on RGB data, we achieve accuracy of up-to 90%.and using Random Forest and Multilayer Perceptron (MLP) feed forward Neural Network as classifiers on spectral data we can achieve accuracies of up-to 98.74% and 89.49% respectively. Hyperspectral imaging ; RGB color model ; Calcium carbide ; Ripening ; Computer science ; Artificial intelligence ; Carica ; Multilayer perceptron ; Perceptron ; Artificial neural network ; Pattern recognition (psychology) ; Remote sensing ; Horticulture ; Geography ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Yuki K. Wakabayashi | Takuma Otsuka | Yoshitaka Taniyasu | Hideki Yamamoto | Hiroshi Sawada
Source: Applied Physics Express (article, 2018-10-01)
Field: Materials Science — Materials Chemistry

Improved adaptive sampling method utilizing Gaussian process regression for prediction of spectral peak structures Materials informatics exploiting machine learning techniques has the potential to afford high-throughput experiments. In the various spectroscopies widely used in the research fields of materials science and physics, detailed peak structures provide vitally important information. Here, we propose an improved adaptive sampling method for such spectroscopies, which utilizes Gaussian process regression with a new heuristic evaluation function. Application of this method to various spectroscopies reveals that it enables prediction of detailed peak structures using fewer sampling points while preserving the required accuracy. Our method reduces the measurement time and cost and thus increases the throughput of sample characterization. Gaussian process ; Kriging ; Adaptive sampling ; Sampling (signal processing) ; Computer science ; Heuristic ; Gaussian ; Throughput ; Regression ; Process (computing) ; Sample (material) ; Machine learning ; Artificial intelligence ; Algorithm ; Data mining ; Mathematics ; Statistics ; Monte Carlo method ; Physics Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; X-ray Diffraction in Crystallography
Authors: Pengfei Du | A. Zebrowski | Jarosław Żola | Baskar Ganapathysubramanian | Olga Wodo
Source: npj Computational Materials (article, 2018-09-03)
Field: Materials Science — Polymers and Plastics

Microstructure design using graphs Abstract Thin films with tailored microstructures are an emerging class of materials with applications such as battery electrodes, organic electronics, and biosensors. Such thin film devices typically exhibit a multi-phase microstructure that is confined, and show large anisotropy. Current approaches to microstructure design focus on optimizing bulk properties, by tuning features that are statistically averaged over a representative volume. Here, we report a tool for morphogenesis posed as a graph-based optimization problem that evolves microstructures recognizing confinement and anisotropy constraints. We illustrate the approach by designing optimized morphologies for photovoltaic applications, and evolve an initial morphology into an optimized morphology exhibiting substantially improved short circuit current (68% improvement over a conventional bulk-heterojunction morphology). We show optimized morphologies across a range of thicknesses exhibiting self-similar behavior. Results suggest that thicker films (250 nm) can be used to harvest more incident energy. Our graph based morphogenesis is broadly applicable to microstructure-sensitive design of batteries, biosensors and related applications. Microstructure ; Materials science ; Anisotropy ; Nanotechnology ; Heterojunction ; Thin film ; Optoelectronics ; Composite material ; Optics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Ramezan Ali Taheri | Yasin Akhtari | Tahereh Tohidi Moghadam | Bijan Ranjbar
Source: Scientific Reports (article, 2018-06-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Assembly of Gold Nanorods on HSA Amyloid Fibrils to Develop a Conductive Nanoscaffold for Potential Biomedical and Biosensing Applications Today, Gold Nanorods have promised variety of applications in conjugation with biomolecules of interest. Discovery of functional amyloids has also been highlighted with possible use in designing high performance materials. To exploit dual properties of both Nano and Bio counterparts in new functional materials, this effort has focused on synthesis of a potential hybrid system of Gold nanorods (GNRs) and HSA amyloid fibrils to develop a conductive nanoscaffold. UV-Vis spectroscopy, Thioflavin T (ThT) assay, Far-UV Circular Dichroism (CD) spectropolarimetry, fluorescence and Transmission Electron microscopy were used to characterize formation of the nanostructures and amyloid fibrils. Surface plasmon resonance of GNRs was also monitored upon interaction with HSA amyloid fibrils, showing that the plasmonic component of the hybrid system has maintained its characteristic rod morphology without any perturbations. Analysis of Nyquist plots for the hybrid nanoscaffold showed that the electronic behavior of the hybrid system has been enhanced due to the presence of the assembled GNRs. Results of this investigation highlight the possibility of fabricating hybrid nano-bioscaffolds as promising candidates in versatile biomedical and biosensing applications. Nanorod ; Nanotechnology ; Biosensor ; Biomolecule ; Plasmon ; Materials science ; Surface plasmon resonance ; Nanobiotechnology ; Colloidal gold ; Nanoparticle ; Nanostructure ; Chemistry ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Konstantia Georgouli | Maria Teresa Moreira Osório | Jesús Martínez del Rincón | Anastasios Koidis
Source: Journal of Chemometrics (article, 2018-02-09)
Field: Chemistry — Analytical Chemistry

Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement Abstract Generating more accurate, efficient, and robust classification models in chemometrics, able to address real‐world problems in food analysis, is intrinsically related with the amount of available calibration samples. In this paper, we propose a data augmentation solution to increase the performance of a classification model by generating realistic data augmented samples. The feasibility of this solution has been evaluated on 3 main different experiments where Fourier transform mid infrared (FT‐IR) spectroscopic data of vegetable oils were used for the identification of vegetable oil species in oil admixtures. Results demonstrate that data augmented samples improved the classification rate by around 19% in a single instrument validation and provided a significant 38% improvement in classification when testing in more than 10 different spectroscopic instruments to the calibration one. Chemometrics ; Calibration ; Identification (biology) ; Computer science ; Vegetable oil ; Edible oil ; Process engineering ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Chemistry ; Machine learning ; Statistics ; Food science ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Di Wang | Lin Xie | Simon X. Yang | Fengchun Tian
Source: Sensors (article, 2018-09-25)
Field: Chemistry — Analytical Chemistry

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data. Support vector machine ; Genetic algorithm ; Principal component analysis ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Computer science ; Identification (biology) ; Artificial intelligence ; Data mining ; Algorithm ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Jan Kremer | Fei Sha | Christian Igel
Source: Research at the University of Copenhagen (University of Copenhagen) (article, 2018-03-31)
Field: Chemistry — Spectroscopy

Robust Active Label Correction. Active label correction addresses the problem of learning from input data for which noisy labels are available (e.g., from imprecise measurements or crowd-sourcing) and each true label can be obtained at a significant cost (e.g., through additional measurements or human experts). To minimize these costs, we are interested in identifying training patterns for which knowing the true labels maximally improves the learning performance. We approximate the true label noise by a model that learns the aspects of the noise that are class-conditional (i.e., independent of the input given the observed label). To select labels for correction, we adopt the active learning strategy of maximizing the expected model change. We consider the change in regularized empirical risk functionals that use different pointwise loss functions for patterns with noisy and true labels, respectively. Different loss functions for the noisy data lead to different active label correction algorithms. If loss functions consider the label noise rates, these rates are estimated during learning, where importance weighting compensates for the sampling bias. We show empirically that viewing the true label as a latent variable and computing the maximum likelihood estimate of the model parameters performs well across all considered problems. A maximum a posteriori estimate of the model parameters was beneficial in most test cases. An image classification experiment using convolutional neural networks demonstrates that the class-conditional noise model, which can be learned efficiently, can guide re-labeling in real-world applications. Computer science Analytical Chemistry and Chromatography ; Advanced Control Systems Optimization ; Fuzzy Logic and Control Systems
Authors: Tujin Shi | Matthew Gaffrey | Thomas Fillmore | Carrie Nicora | Lian Yi | Pengfei Zhang | Anil Shukla | H Wiley | Karin Rodland | Tao Liu | Richard Smith | Weijun Qian
Source: Communications Biology (article, 2018-07-31)
Field: Chemistry — Spectroscopy

Facile carrier-assisted targeted mass spectrometric approach for proteomic analysis of low numbers of mammalian cells There is an unmet technical challenge for mass spectrometry (MS)-based proteomic analysis of single mammalian cells. Quantitative proteomic analysis of single cells has been previously achieved by antibody-based immunoassays but is limited by the availability of high-quality antibodies. Herein we report a facile targeted MS-based proteomics method, termed cPRISM-SRM (carrier-assisted high-pressure, high-resolution separations with intelligent selection and multiplexing coupled to selected reaction monitoring), for reliable analysis of low numbers of mammalian cells. The method capitalizes on using "carrier protein" to assist processing of low numbers of cells with minimal loss, high-resolution PRISM separation for target peptide enrichment, and sensitive SRM for protein quantification. We have demonstrated that cPRISM-SRM has sufficient sensitivity to quantify proteins expressed at ≥200,000 copies per cell at the single-cell level and ≥3000 copies per cell in 100 mammalian cells. We envision that with further improvement cPRISM-SRM has the potential to move toward targeted MS-based single-cell proteomics. Proteomics ; Quantitative proteomics ; Mass spectrometry ; Peptide ; Chemistry ; Cell ; High resolution ; Single-cell analysis ; Computational biology ; Molecular biology ; Chromatography ; Biology ; Biochemistry ; Gene Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Chenguang Gao | Xiangling Zhang | Ye Yuan | Yu Lei | Jingtian Gao | Shuangjie Zhao | Chunyan He | Lichu Deng
Source: Ecotoxicology and Environmental Safety (article, 2018-09-28)
Field: Materials Science — Materials Chemistry

Removal of hexavalent chromium ions by core-shell sand/Mg-layer double hydroxides (LDHs) in constructed rapid infiltration system Hexavalent chromium ; Layered double hydroxides ; Adsorption ; Langmuir adsorption model ; Scanning electron microscope ; Chemical engineering ; Desorption ; Chemistry ; Chromium ; Nuclear chemistry ; Materials science ; Metallurgy ; Composite material ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Liz Nayibe Martínez Saavedra | Ricardo G. Penido | Lucas de Azevedo Santos | Teodorico C. Ramalho | Bruno Eduardo Lôbo Baêta | Márcio C. Pereira | Adilson C. Silva
Source: RSC Advances (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Molecularly imprinted polymers for selective adsorption of quinoline: theoretical and experimental studies The effects of solvent on the synthesis of molecularly imprinted polymers (MIPs) for the selective adsorption of quinoline were evaluated in this work. The MIPs were synthesized by the "bulk" method using the quinoline molecule (IQ) as a template in different solvents, such as toluene (MIPT) and chloroform (MIPC). The adsorbents were characterized by thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), and N<sub>2</sub> adsorption/desorption measurements. The influences of time, adsorbate concentration, and temperature on the adsorption of quinoline by MIPT and MIPC were evaluated. Maximum adsorption capacities (<i>q</i> <sub>e</sub>) of 35.23 and 24.10 mg g<sup>-1</sup> were obtained for MIPT and MIPC, respectively. Thermodynamic studies indicate that occur physisorption and a spontaneous process (Δ<sub>ads</sub> <i>G</i>° < 0) entropically directed. Finally, the highest selectivity and reusability of MIPC for quinoline adsorption was ascribed to the better interaction between the chloroform and monomer, which favors the formation of porous adsorbents with higher numbers of adsorption sites. Quinoline ; Molecularly imprinted polymer ; Selective adsorption ; Adsorption ; Chemistry ; Polymer ; Molecular imprinting ; Combinatorial chemistry ; Organic chemistry ; Selectivity ; Catalysis Analytical chemistry methods development ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Jun Dong | Feifan Xu | Zhaojun Dong | Yongsheng Zhao | Yan Yan | Hua Jin | Yangxue Li
Source: RSC Advances (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Fabrication of two dual-functionalized covalent organic polymers through heterostructural mixed linkers and their use as cationic dye adsorbents With the rapid development of industrialization, population growth and long-term droughts, the scarcity of clean water and increasing environmental pollution are becoming critical issues. Porous organic polymer materials have been proposed as good candidates for removing pollutant compounds from water supplies. However, because of their finite synthetic chemical reactions and monotonous building blocks, fabricating covalent organic polymer materials with multiple linkages and various different multi-functional components remains a huge challenge. Herein, two dual-functionalized covalent organic polymers (JLUE-COP-1 and JLUE-COP-2) were prepared through heterostructural mixed linkers <i>via</i> imine and hydrazone linkages based on Schiff base condensation. Possessing the advantages of porosity and π-conjugated phenyl rings, as well as functional -CO-NH- and -SO<sub>3</sub>H building units, the resultant dual-functionalized JLUE-COPs exhibited high capabilities for the removal of the cationic dye methylene blue (MB). Experiments were carried out to investigate the effects of pH, dye concentration, contact time and temperature on the performance of the JLUE-COPs. The kinetics, equilibrium properties, thermodynamics and mechanisms were studied. It was found that the adsorption processes of the two JLUE-COPs both followed the second-order kinetic model and can be described well by the Langmuir model. Notably, the differences in the surface and pore volumes of the two JLUE-COPs lead to differences in the adsorption rate, adsorption efficiency and maximum adsorption capacity. In addition, activation energy values and some thermodynamic parameters such as Δ<i>G</i> <sup><i>Θ</i></sup> , Δ<i>H</i> <sup><i>Θ</i></sup> and Δ<i>S</i> <sup><i>Θ</i></sup> suggest that the adsorption processes of dyes onto the JLUE-COPs are entropy-driven, endothermic and spontaneous. Our work thus paves the way for developing functionalized COPs as a new type of platform for removing cationic dyes from wastewater. Cationic polymerization ; Covalent bond ; Polymer ; Fabrication ; Adsorption ; Polymer chemistry ; Chemical engineering ; Chemistry ; Materials science ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Milan Parchovianský
Source: Ceramics - Silikaty (article, 2018-02-28)
Field: Materials Science — Materials Chemistry

CORROSION AND OXIDATION BEHAVIOR OF POLYMER DERIVED CERAMIC COATINGS WITH PASSIVE GLASS FILLERS ON AISI441 STAINLESS STEEL Polymer derived ceramic (PDC) polysilazane-based double layer composite coatings on steel substrates consisting of a PDC bond-coat, and a PDC-based top-coat containing ceramic passive (ZrO 2 ) and active (ZrSi 2 ) fillers, as well as a specially tailored passive glass filler with high melting temperature were developed. The corrosion and oxidation behaviour of the ferritic stainless steel AISI 441 with and without the PDC coating system was evaluated. SEM examination revealed the composite coatings were not dense but contained small closed pores with a diameter up to 100 nm. Cracks and occasionally also delamination were detected. The static corrosion tests under hydrothermal conditions at the temperature of 200C resulted in partial oxidation of the uncoated steel substrate, whereas the coated sample was protected by the PDC coating. No oxidation of the top coat was detected after the static corrosion test. The high temperature oxidation behaviour in a flowthrough oxygen atmosphere was investigated at the temperatures of 900C, 1000C and 1100C. X-ray diffraction confirmed extensive corrosion of the uncoated AISI 441 stainless steel accompanied by formation of a Cr 2 O 3 , TiO 2 and a (Mn, Cr) 3 O 4 spinel containing layer of corrosion products. Beneficial effect of the PDC coating was observed at the temperature of 900C, demonstrated by marked reduction of the weight gain of coated steel after 196 h of exposure to flowing oxygen. At higher temperatures the protective action of the PDC coat was not observed, and a thick layer of corrosion products was formed at the steel/coating interface. Materials science ; Corrosion ; Ceramic ; Composite material ; Polymer ; Metallurgy Silicone and Siloxane Chemistry ; Advanced ceramic materials synthesis ; High-Temperature Coating Behaviors
Authors: Chuanlin Zhao | C. W. Watt | Paul R. C. Kent | Steven H. Overbury | David R. Mullins | Florencia Calaza | Aditya Savara | Ye Xu
Source: The Journal of Physical Chemistry C (article, 2018-10-24)
Field: Materials Science — Materials Chemistry

Coupling of Acetaldehyde to Crotonaldehyde on CeO<sub>2–<i>x</i></sub>(111): Bifunctional Mechanism and Role of Oxygen Vacancies Selective C–C coupling of oxygenates is pertinent to the manufacture of fuel and chemical products from biomass and from derivatives of C1 compounds (i.e., oxygenates produced from methane and CO2). Here we report a combined experimental and theoretical study on the temperature-programmed reaction (TPR) of acetaldehyde (AcH) on a partially reduced CeO2–x(111) thin film surface. The experiments have been carried out under ultra-high-vacuum conditions without continuous gas exposure, allowing better isolation of active sites and reactive intermediates than in flow reaction conditions. AcH does not undergo aldol condensation in a typical TPR procedure, even though the enolate form of AcH (CH2CHO) is readily produced on CeO2–x(111) with oxygen vacancies. We find however that a tailored “double-ramp” TPR procedure is able to successfully produce an aldol adduct, crotonaldehyde (CrA). Using density functional theory calculations and microkinetic modeling we explore several possible C–C coupling pathways. We conclude that the double-ramp procedure allows surface oxygen vacancy dimers, stabilized by adsorbate occupation, to form dynamically during the TPR. The vacancy dimers in turn enable C–C coupling to occur between an enolate and an adjacent AcH molecule via a bifunctional enolate–keto mechanism that is distinct from conventional acid- or base-catalyzed aldol condensation reactions. The proposed mechanism indicates that CrA desorption is rate-limiting while C–C coupling is facile. Crotonaldehyde ; Bifunctional ; Acetaldehyde ; Oxygen ; Mechanism (biology) ; Coupling (piping) ; Chemistry ; Materials science ; Photochemistry ; Chemical physics ; Physics ; Organic chemistry ; Catalysis ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion