jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shichao Zhu | Zhuoming Song | Shengyu Shi | Mengmeng Wang | Gang Jin
Source: Sensors (article, 2019-08-08)
Field: Chemistry — Analytical Chemistry

Fusion of Near-Infrared and Raman Spectroscopy for In-Line Measurement of Component Content of Molten Polymer Blends Spectral measurement techniques, such as the near-infrared (NIR) and Raman spectroscopy, have been intensively researched. Nevertheless, even today, these techniques are still sparsely applied in industry due to their unpredictable and unstable measurements. This paper put forward two data fusion strategies (low-level and mid-level fusion) for combining the NIR and Raman spectra to generate fusion spectra or fusion characteristics in order to improve the in-line measurement precision of component content of molten polymer blends. Subsequently, the fusion value was applied to modeling. For evaluating the response of different models to data fusion strategy, partial least squares (PLS) regression, artificial neural network (ANN), and extreme learning machine (ELM) were applied to the modeling of four kinds of spectral data (NIR, Raman, low-level fused data, and mid-level fused data). A system simultaneously acquiring in-line NIR and Raman spectra was built, and the polypropylene/polystyrene (PP/PS) blends, which had different grades and covered different compounding percentages of PP, were prepared for use as a case study. The results show that data fusion strategies improve the ANN and ELM model. In particular, mid-level fusion enables the in-line measurement of component content of molten polymer blends to become more accurate and robust. Raman spectroscopy ; Fusion ; Partial least squares regression ; Chemometrics ; Polypropylene ; Materials science ; Component (thermodynamics) ; Principal component analysis ; Sensor fusion ; Spectroscopy ; Near-infrared spectroscopy ; Biological system ; Analytical Chemistry (journal) ; Computer science ; Artificial intelligence ; Optics ; Chemistry ; Machine learning ; Composite material ; Physics ; Chromatography ; Thermodynamics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Erik C. Abbott | Alexandria Brenkmann | Craig S. Galbraith | Joshua Ong | Ian J. Schwerdt | Brent D. Albrecht | Tolga Taşdizen | Luther W. McDonald
Source: Radiochimica Acta (article, 2019-04-18)
Field: Materials Science — Materials Chemistry

Dependence of UO<sub>2</sub> surface morphology on processing history within a single synthetic route Abstract This study aims to determine forensic signatures for processing history of UO 2 based on modifications in intermediate materials within the uranyl peroxide route. Uranyl peroxide was calcined to multiple intermediate U-oxides including Am-UO 3 , α-UO 3 , and α-U 3 O 8 during the production of UO 2 . The intermediate U-oxides were then reduced to α-UO 2 via hydrogen reduction under identical conditions. Powder X-ray diffractometry (p-XRD) and X-ray photoelectron spectroscopy (XPS) were used to analyze powders of the intermediate U-oxides and resulting UO 2 to evaluate the phase and purity of the freshly synthesized materials. All U-oxides were also analyzed via scanning electron microscopy (SEM) to determine the morphology of the freshly prepared powders. The microscopy images were subsequently analyzed using the Morphological Analysis for Materials (MAMA) version 2.1 software to quantitatively compare differences in the morphology of UO 2 from each intermediate U-oxide. In addition, the microscopy images were analyzed using a machine learning model which was trained based on a VGG 16 architecture. Results show no differences in the XRD or XPS spectra of the UO 2 produced from each intermediate. However, results from both the segmentation and machine learning proved that the morphology was quantifiably different. In addition, the morphology of UO 2 was very similar, if not identical, to the intermediate material from which it was prepared, thus making quantitative morphological analysis a reliable forensic signature of processing history. X-ray photoelectron spectroscopy ; Scanning electron microscope ; Uranyl ; Morphology (biology) ; Calcination ; Chemistry ; Oxide ; Crystallography ; Analytical Chemistry (journal) ; Chemical engineering ; Materials science ; Uranium ; Catalysis ; Metallurgy ; Geology ; Chromatography Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear Physics and Applications
Authors: Zhengyan Xia | Yiming Sun | Chengyong Cai | Yong He | Pengcheng Nie
Source: Sensors (article, 2019-04-28)
Field: Chemistry — Analytical Chemistry

Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of r<sub>p</sub><sup>2</sup> (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum. Chlorogenic acid ; Partial least squares regression ; Chemistry ; Chromatography ; Calibration ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Jörgen Samuelsson | Finnur Freyr Eiríksson | Dennis Åsberg | Margrét Þorsteinsdóttir | Torgny Fornstedt
Source: Journal of Chromatography A (article, 2019-03-29)
Field: Chemistry — Spectroscopy

Determining gradient conditions for peptide purification in RPLC with machine-learning-based retention time predictions Chemistry ; Calibration ; Chromatography ; In silico ; Retention time ; Peptide ; Selectivity ; Biological system ; Calibration curve ; Stationary phase ; Mathematics Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Mass Spectrometry Techniques and Applications
Authors: Anjana Talapatra | Shahin Boluki | Pejman Honarmandi | Αλέξανδρος Σολωμού | Guang Zhao | Seyede Fatemeh Ghoreishi | Abhilash Molkeri | Douglas Allaire | Ankit Srivastava | Xiaoning Qian | Edward R. Dougherty | Dimitris C. Lagoudas | Raymundo Arróyave
Source: Frontiers in Materials (article, 2019-04-24)
Field: Materials Science — Materials Chemistry

Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across Scales Over the last decade, there has been a paradigm shift away from labor-intensive and time-consuming materials discovery methods, and materials exploration through informatics approaches is gaining traction at present. Current approaches are typically centered around the idea of achieving this exploration through high-throughput (HT) experimentation/computation. Such approaches, however, do not account for the practicalities of resource constraints which eventually result in bottlenecks at various stage of the workflow. Regardless of how many bottlenecks are eliminated, the fact that ultimately a human must make decisions about what to do with the acquired information implies that HT frameworks face hard limits that will be extremely difficult to overcome. Recently, this problem has been addressed by framing the materials discovery process as an optimal experiment design problem. In this article, we discuss the need for optimal experiment design, the challenges in it’s implementation and finally discuss some successful examples of materials discovery via experiment design. Computer science ; Workflow ; Data science ; Business process discovery ; Scientific discovery ; Risk analysis (engineering) ; Management science ; Work in process ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Wei Kong | Mengtong Zhang | Zhen Han | Qiang Zhang
Source: Applied Sciences (article, 2019-01-31)
Field: Materials Science — Materials Chemistry

A Theoretical Model for the Triple Phase Boundary of Solid Oxide Fuel Cell Electrospun Electrodes Electrospinning is a new state-of-the-art technology for the preparation of electrodes for solid oxide fuel cells (SOFC). Electrodes fabricated by this method have been proven to have an experimentally superior performance compared with traditional electrodes. However, the lack of a theoretic model for electrospun electrodes limits the understanding of their benefits and the optimization of their design. Based on the microstructure of electrospun electrodes and the percolation threshold, a theoretical model of electrospun electrodes is proposed in this study. Electrospun electrodes are compared to fibers with surfaces that were coated with impregnated particles. This model captures the key geometric parameters and their interrelationship, which are required to derive explicit expressions of the key electrode parameters. Furthermore, the length of the triple phase boundary (TPB) of the electrospun electrode is calculated based on this model. Finally, the effects of particle radius, fiber radius, and impregnation loading are studied. The theory model of the electrospun electrode TPB proposed in this study contributes to the optimization design of SOFC electrospun electrode. Triple phase boundary ; Electrode ; Materials science ; Electrospinning ; Percolation theory ; Solid oxide fuel cell ; Composite material ; Percolation (cognitive psychology) ; RADIUS ; Oxide ; Nanotechnology ; Anode ; Polymer ; Computer science ; Electrical engineering ; Metallurgy ; Chemistry ; Topology (electrical circuits) ; Engineering Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Alfarius Eko Nugroho | Hiroshi Morita
Source: Journal of Natural Medicines (review, 2019-05-15)
Field: Chemistry — Spectroscopy

Computationally-assisted discovery and structure elucidation of natural products Computer hardware development coupled with the development of quantum chemistry, new computational models and algorithms, and user-friendly interfaces have lowered the barriers to the use of computation in the discovery and structure elucidation of natural products. Consequently, the use of computational chemistry software as a tool to discover and determine the structure of natural products has become more common in recent years. In this review, we provide several examples of recent studies that used computer technology to facilitate the discovery and structure determination of various natural products. Computer science ; Natural (archaeology) ; Software ; Computation ; Data science ; Biochemical engineering ; Algorithm ; Engineering ; Programming language ; Biology Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: María Besora | Andrea Olmos | Riccardo Gava | Bárbara Noverges | Gregorio Asensio | Ana Caballero | Feliu Maseras | Pedro J. Pérez
Source: Angewandte Chemie International Edition (article, 2019-12-11)
Field: Chemistry — Organic Chemistry

A Quantitative Model for Alkane Nucleophilicity Based on C−H Bond Structural/Topological Descriptors A first quantitative model for calculating the nucleophilicity of alkanes is described. A statistical treatment was applied to the analysis of the reactivity of 29 different alkane C-H bonds towards in situ generated metal carbene electrophiles. The correlation of the recently reported experimental reactivity with two different sets of descriptors comprising a total of 86 parameters was studied, resulting in the quantitative descriptor-based alkane nucleophilicity (QDEAN) model. This model consists of an equation with only six structural/topological descriptors, and reproduces the relative reactivity of the alkane C-H bonds. This reactivity can be calculated from parameters emerging from the schematic drawing of the alkane and a simple set of sums. Alkane ; Nucleophile ; Reactivity (psychology) ; Chemistry ; Electrophile ; Carbene ; Computational chemistry ; Organic chemistry ; Hydrocarbon Cyclopropane Reaction Mechanisms ; Synthetic Organic Chemistry Methods ; Catalytic C–H Functionalization Methods
Authors: Davor Valinger
Source: Chemical and Biochemical Engineering Quarterly (article, 2019-01-16)
Field: Chemistry — Analytical Chemistry

Development of Near Infrared Spectroscopy Models for Quantitative Prediction of the Content of Bioactive Compounds in Olive Leaves The objective of this work was to evaluate the ability of artificial neural networks (ANN) in near infrared (NIR) spectra calibration models to predict the total polyphenolic content, antioxidant activity, and extraction yield of the olive leaves aqueous extracts prepared with three extraction procedures (conventional extraction, microwave-assisted extraction, and microwave-ultrasound-assisted extraction). Partial least squares (PLS) models were developed from principal component analyses (PCA) scores of NIR spectra of olive leaf aqueous extracts in terms of total polyphenols concentration, antioxidant activity, and extraction yield for each extraction procedure. PLS models were used to view which PCA scores are the best suited as input for ANN based on three output variables. ANN showed very good correlation of NIRs and all tested variables, especially in the case of total polyphenolic content (TPC). Therefore, ANN can be used for the prediction of total polyphenol concentrations, antioxidant activity, and extraction yield of plant extracts based on the NIR spectra. Infrared spectroscopy ; Spectroscopy ; Near infrared reflectance spectroscopy ; Content (measure theory) ; Chemistry ; Near-infrared spectroscopy ; Infrared ; Botany ; Biological system ; Biology ; Organic chemistry ; Mathematics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Yuhui Zhao | Jinlong Yu | Peng Shan | Ziheng Zhao | Xueying Jiang | Shuli Gao
Source: Molecules (article, 2019-04-02)
Field: Chemistry — Analytical Chemistry

PLS Subspace-Based Calibration Transfer for Near-Infrared Spectroscopy Quantitative Analysis In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results. Partial least squares regression ; Subspace topology ; Calibration ; Mathematics ; Feature (linguistics) ; Pattern recognition (psychology) ; Artificial intelligence ; Algorithm ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Xiaoze Shi | Shuai Zhang | Xuecheng Chen | Ewa Mijowska
Source: Nanomaterials (article, 2019-04-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evaluation of Nanoporous Carbon Synthesized from Direct Carbonization of a Metal–Organic Complex as a Highly Effective Dye Adsorbent and Supercapacitor The synthesis of interconnected nanoporous carbon (NPC) material from direct annealing of ultra-small Al-based metal-organic complex (Al-MOC) has been demonstrated. NPC presents a large accessible area of 1,054 m²/g, through the Methylene Blue (MB) adsorption method, which is comparable to the high specific surface area (SSA) of 1,593 m²/g, through an N₂ adsorption/desorption analysis. The adsorption properties and mechanisms were tested by various dye concentrations, pH, and temperature conditions. The high MB accessible area and the good electrical conductivity of the interconnected NPC, led to a large specific capacitance of 205 F/g, with a potential window from 0 to 1.2 V, in a symmetric supercapacitor, and a large energy density of 10.25 Wh/kg, in an aqueous electrolyte, suggesting a large potential in supercapacitors. Supercapacitor ; Carbonization ; Adsorption ; Materials science ; Nanoporous ; Specific surface area ; Chemical engineering ; Methylene blue ; Activated carbon ; Electrolyte ; Aqueous solution ; Metal-organic framework ; Annealing (glass) ; Capacitance ; Nanotechnology ; Chemistry ; Organic chemistry ; Electrode ; Composite material ; Photocatalysis ; Physical chemistry Supercapacitor Materials and Fabrication ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Falk Schulze‐Küppers | Stefan Baumann | Wilhelm Albert Meulenberg | H.J.M. Bouwmeester
Source: Journal of Membrane Science (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

Influence of support layer resistance on oxygen fluxes through asymmetric membranes based on perovskite-type oxides SrTi1-Fe O3- Asymmetric membranes of mixed ionic-electronic conducting perovskite-type oxides SrTi 1-x Fe x O 3- (STF, x 0.3, 0.5 and 0.7) were prepared by inverse sequential tape-casting. Both porous support (~600 m) and functional membrane layer (~20 m) for a given membrane assembly were made from the same composition to ensure thermochemical compatibility between the layers. Oxygen fluxes were assessed in the range 650 -1020 C, using either (non-pressurized) ambient air or pure oxygen as feed gas at the support side of the asymmetric membrane and argon as sweep gas. Notably, similar oxygen fluxes (~1.2 10 6 mol cm 2 s 1 ) are measured through the membranes of different compositions above 950 C when using ambient air as feed gas. This observation is interpreted to reflect the major role of the support layer resistance in rate-limiting the oxygen fluxes through the STF asymmetric membranes, which conclusion is supported by comparison of the oxygen fluxes with those measured previously through asymmetric membranes of Ba 0.5 Sr 0.5 Co 0.8 Fe 0.2 O 3- (BSCF) and La 0.6 Sr 0.4 Co 0.2- Fe 0.8 O 3- (LSCF). A simple diffusion-convection model is used to account for the observed gas phase polarization in the porous support layers limiting the oxygen fluxes. Membrane ; Oxygen ; Oxygen transport ; Partial pressure ; Materials science ; Perovskite (structure) ; Concentration polarization ; Limiting current ; Chemical engineering ; Polarization (electrochemistry) ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Crystallography ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Gabriel Kerbart | Christelle Harnois | Christelle Bilot | Sylvain Marinel
Source: Journal of the European Ceramic Society (article, 2019-03-22)
Field: Chemistry — Organic Chemistry

Pressure-assisted microwave sintering: A rapid process to sinter submicron sized grained MgAl2O4 transparent ceramics Materials science ; Sintering ; Ceramic ; Microstructure ; Fracture toughness ; Spinel ; Grain size ; Microwave ; Composite material ; Transmittance ; Transparent ceramics ; Metallurgy ; Grain growth ; Optoelectronics Microwave-Assisted Synthesis and Applications ; Microwave Dielectric Ceramics Synthesis ; Advanced ceramic materials synthesis
Authors: Puerta Adrián | R. Galán Alexis | Roderick Abdilla | Kaylie Demanuele | Miguel X. Fernandes | Giovanna Bosica | José M. Padrón
Source: Journal of Molecular and Clinical Medicine (article, 2019-04-20)
Field: Chemistry — Organic Chemistry

Naphthol-derived Betti bases as potential SLC6A14 blockers Betti bases (aminobenzylnaphthols) have not been studied extensively to explore their possible pharmacological applications. Our group prepared a small and focused library of twenty-three Betti bases from the multicomponent reaction of 2-naphthol with primary or secondary cyclic amines and representative aromatic aldehydes. The compounds were prepared in 52-90% yield using environmentally friendly procedures. The E-factor and the atom economy for our process were 3.92 and 94%, respectively. The study of the anti-proliferative activity against human solid tumor cell lines pointed out that these Betti bases represent privileged scaffolds and could be used for the development of pharmacologically-active compounds in cancer therapeutics. The 50% growth inhibitory (GI50) values of the most potent compounds were in the low micromolar range. Fourteen of these Betti bases were less active in HBL-100 breast cancer cells than towards the breast cancer cell line T-47D. A subset of these Betti bases was further tested against the human breast cancer cell lines MCF-7 and MDA-MB-453. The results indicated a correlation in the sensitivity of T-47D cells to Betti bases. We explored computationally the interaction of the Betti bases with SLC6A14, a Na+- and Cl-- dependent influx transporter of both neutral and cationic amino acids that is overexpressed in T-47D cells. SLC6A14 is inhibited by α-methyl-tryptophan, which blocks cell growth via deprivation of amino acid influx. The docking studies indicated that our Betti bases might behave as tryptophan mimetics, blocking this solute carrier transporter and inducing the anti-proliferative effects. Importantly, these Betti bases showed good cytotoxic selectivity towards cancer cells with no activity against normal human fibroblast cells BJ-hTERT. Betti number ; Chemistry ; Tryptophan ; MCF-7 ; Amino acid transporter ; Indole test ; Amino acid ; Stereochemistry ; Docking (animal) ; Cancer cell ; Human breast ; Biochemistry ; Combinatorial chemistry ; Transporter ; Cancer ; Biology ; Gene ; Genetics Synthesis and Biological Evaluation ; Chemical Synthesis and Analysis ; Click Chemistry and Applications