jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kévin Jacq | Yves Perrette | Bernard Fanget | Didier Coquin | Pierre Sabatier | William Rapuc | Claire Blanchet | Ruth Martinez Lamas | Maxime Debret | Fabien Arnaud
Source: HAL (Le Centre pour la Communication Scientifique Directe) (preprint, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging for lake sediment cores analysis The aim of this presentation is to overview some applications of hyperspectral imaging for core sediment analysis in paleoenvironmental studies. Sampling methods (millimetre or centimetre) and routine analyses are destructive and non-spatially resolved methods that consume time and material. Hyperspectral Imaging is a way to have the advantages of spectroscopy (non-destructive, fast analysis) and of imaging (high resolution, information is spatially referenced). coupling hyperspectral imaging with data mining methods makes possible to study several proxies at micrometric scale in each area of the core. Two hyperspectral cameras are used, a Visible-Near InfraRed VNIR (spectral range: 400-1000nm, spatial resolution: 60μm) and a Short Wave InfraRed SWIR (spectral range: 1000-2500nm, spatial resolution: 189μm). The two datasets produced can be fused in a unique one used to model environmental proxies. This methodology was achieved on a core from the lake Le Bourget (Western Alps, 53cm long and 9cm width). Quantitative prediction models can be made with partial least squares regression PLSR. This method links spectra with a reference analysis by the creation of a regression model. Assuming a scale homogeneity, it can be spread to all the spectra of the hyperspectral image to predict high spatially resolved proxies. Total Organic Carbon and Grain Size class models have been developed with a validation determination coefficient of 0.86 for TOC and 0.85 for clay. Concentration maps are used to study variation inside each stratigraphic unit event at the scale of laminae. These datasets can be used for classification. Based on pattern recognition and artificial neural network, it is possible to classify the type of lithology defined by the user, for example: summer or winter lamina, floods with labelled areas of less than 1% of the image. For varved sediments, this method can be used to count the varve and apply statistics on them. Hyperspectral imaging ; VNIR ; Remote sensing ; Partial least squares regression ; Image resolution ; Geology ; Environmental science ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Remote-Sensing Image Classification
Authors: Rama K. Vasudevan | Nouamane Laanait | Erik M. Ferragut | Kai Wang | David B. Geohegan | Kai Xiao | Maxim Ziatdinov | Stephen Jesse | Ondrej Dyck | Sergei V. Kalinin
Source: npj Computational Materials (preprint, 2018-06-22)
Field: Materials Science — Surfaces, Coatings and Films

Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images Abstract Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real time. To date, this has mostly been a manual endeavor comprising difficult frame-by-frame analysis that is simultaneously tedious and prone to error. Here, we turn toward the use of deep convolutional neural networks (DCNN) to automatically determine the Bravais lattice symmetry present in atomically resolved images. A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input image. Monte-Carlo dropout is used for determining the prediction probability, and results are shown for both simulated and real atomically resolved images from scanning tunneling microscopy and scanning transmission electron microscopy. A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical intuition. We then apply the trained network to electron beam-induced transformations in WS 2 , which allows tracking and determination of growth rate of voids. We highlight two key aspects of these results: (1) it shows that DCNNs can be trained to recognize diffraction patterns, which is markedly different from the typical “real image” cases and (2) it provides a method with in-built uncertainty quantification, allowing the real-time analysis of phases present in atomically resolved images. Bravais lattice ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Computer science ; Monte Carlo method ; Lattice (music) ; Fourier transform ; Frame rate ; Optics ; Statistical physics ; Pattern recognition (psychology) ; Physics ; Mathematics ; Chemistry ; Crystallography Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: David Albinsson
Source: Chalmers Research (Chalmers University of Technology) (article, 2018-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Combining Nanoplasmonics and Nanofluidics for Single Particle Catalysis Nanoparticles are, due to their large exposed surface area, widely used in the field of heterogeneous catalysis where they accelerate and steer chemical reactions. Although catalysis has been known about for centuries, the scrutiny of catalysts under realistic application conditions is still a major challenge. This difficulty originates from the fact that real catalyst materials are very complex, often consisting of large ensembles of nanoparticles that all are unique. Furthermore, the typically used macroscopic reactors in catalysis studies gives rise to locally, at the level of the active site, ill-defined reactant concentrations and diffusion limitations.To overcome these limitations, on one hand, techniques are being developed that are sensitive enough to probe individual catalytic particles and that at the same time can operate under realistic reaction conditions. On the other hand, strategies to more carefully control the amount and structure of catalyst material, as well as to precisely control mass transport to and from the active catalyst, are being investigated by scaling down the size of the used chemical reactor. To further push the limit of downsizing, in this thesis, I present a miniaturized reactor platform based on nanofluidic channels that have been carefully decorated with catalytic nanoparticles, and that is integrated with plasmonic nanospectroscopy readout. This optical technique relies on the nanoscale phenomenon known as the Localized Surface Plasmon Resonance (LSPR) and enables the study of individual metal nanoparticles in operando by means of dark-field scattering spectroscopy.As the first step in this development, we constructed a nanofluidic device with integrated plasmonic nanoparticles to detect minute changes in the liquid flowing through the channels, as well as molecules binding to the nanoparticles. As the second step, we developed the nanofluidic system with an integrated heater and to facilitate gas flow through the nanochannels with the possibility to connect to a mass spectrometer for on-line product analysis. This system was then successfully used to correlate activity with surface and bulk oxidation state changes taking place on individual catalytic Cu and Pt nanoparticles during CO oxidation, measured by means of plasmonic nanospectroscopy. To this end, in a separate study, I also employed the plasmonic approach to study the oxidation process of Cu nanoparticles both experimentally and by electrodynamics simulations. Nanofluidics ; Nanotechnology ; Catalysis ; Nanoparticle ; Plasmon ; Plasmonic nanoparticles ; Nanoscopic scale ; Materials science ; Surface plasmon resonance ; Particle (ecology) ; Chemistry ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Water Quality Monitoring and Analysis
Authors: Brittany M. Gallagher
Source: Rowan Digitals Works (Rowan University) (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Semi-synthesis of a novel library of alkaloids as potential selective analgesics Semi-synthetic compounds are an important part of the therapeutic drug discovery process. The goals of the project were to synthesize different generations of matrine analogues in order to gain many opportunities to manipulate the core structure. The long-term goal was, by manipulating the structure, the possibility to increase the biological activity of the original compound while also decreasing the toxicity. The first-generation matrine analogue allows for a synthesis similar to the Striker reaction producing aminonitriles. An alkyne coupling reaction was also possible with the modification, producing propargyl-amines. Both of the reactions resulted in the addition of a triple bond substituent to the final products; increasing complexity and making the products not easily metabolized in the body, therefore able reach the target of interest. The second-generation matrine derivative was reacted with epoxides to produce amino alcohols. Aminonitriles, propargyl-amines, and amino alcohols are known to be backbones of therapeutic drug compounds. The compounds synthesized were later experimented on chronic myeloid leukemia (CML) cells, also in combination with the manufactured drug Imatinib, in order to inhibit the BCR-ABL kinase activity in the CML cell lines E255K and T315I. Business ; Chemistry Chemical synthesis and alkaloids
Authors: Amalina Mohd Tajuddin | Nuraiza Ismail | Karimah Kassim | Hadariah Bahron
Source: Journal of Fundamental and Applied Sciences (article, 2018-01-23)
Field: Chemistry — Analytical Chemistry

Synthesized and extending the Bidentate Schiff base complexes using multilayer feedforward neural network Complexes of Pd(II) and Ni(II) have been synthesized with general composition ML2X2 (M =Pd(II), Ni(II); L = benzylsalicylideneimine and X = OCH3, F). All synthesized compoundshave been characterized using elemental analysis, magnetic susceptibility measurements,infrared and NMR spectral studies that led to the conclusion that the ligands act as bidentatemanner to form square planar geometry for all complexes. As an extending work, the modeldevelopment of these complexes using multilayer feedforward neural network wereperformed. NiL1d, PdL1d, NiL1c and PdL1c were fed to the training network as inputs andbacteria as output. Levenberg Marquardt training algorithm was used during the networktraining with 10 nodes in hidden layer. The results of testing network showed that theregression, R is 1, indicating that the developed model is good. This is supported by the small mean square error (MSE) is 1.948x10-28 at epochs 5. The finding in this study is significant, thus contributed to the design of antibacterial agent especially to the bidentate Schiff base complexes.Keywords: Schiff base, palladium(II), nickel(II), antibacterial, regression, neural network. Schiff base ; Denticity ; Feedforward neural network ; Artificial neural network ; Nickel ; Chemistry ; Base (topology) ; Feed forward ; Mean squared error ; Mathematics ; Nuclear chemistry ; Algorithm ; Materials science ; Computer science ; Polymer chemistry ; Artificial intelligence ; Organic chemistry ; Mathematical analysis ; Statistics ; Engineering ; Metal ; Control engineering Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Yunfei Shang | Guochen Bao | Jiajia Zhou | Ka‐Leung Wong | Cheng Yang | Dayong Jin
Source: UTS ePRESS (University of Technology Sydney) (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Lanthanide-Doped Upconversion Luminescent Materials for Point of Care Diagnosis © 2018, Editorial Office of Journal of the Chinese Society of Rare Earths. All right reserved. Point of care (POC) diagnosis has attracted much research interest due to its rapid, specific and sensitive detection of biomarkers and pathogens. This decentralized diagnostic approach undoubtedly has huge advantage over laboratory testing at large hospitals or community laboratories regarding cost-effectiveness, healthcare delivery, especially with the emerging and spreading of smart technologies. Among all the materials for POC, lanthanides have become a rising star because of their excellent photophysical properties such as long life time, characteristic luminescence. Here, we focus on the wide-spread platforms of POC diagnostic devices and the applications of lanthanide upconverting materials in this field. With the market and social trend analysis, we will give our future perspective about the applications of lanthanide materials in POC. Photon upconversion ; Doping ; Lanthanide ; Luminescence ; Materials science ; Optoelectronics ; Chemistry ; Organic chemistry Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes ; Medical Imaging Techniques and Applications
Authors: Shobharani Panchagnula
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2018-01-31)
Field: Chemistry — Analytical Chemistry

Determination of Chromium and Manganese in Water by Spectrophotometry and Differential Pulse Polorography –A Comparative Study Water samples were analyzed for manganese and chromium concentrations using U V visible spectrophotometer simultaneously by the measurement of absorbance of light at two wavelengths. The metals were oxidized to Cr 2 O 7 2and MnO 4 ions. Beer's law has been shown to apply closely. Cr 2 O 7 2has an absorption maximum at440 nm and MnO4-has one at 545 nm.(although a somewhat more intense maximum is at 525 nm, but there is less interference from Cr 2 O 7 2-at 545 nm). In the present study the spectral analysis of the said manganese and chromium concentrations was compared with that of the differential pulse polorographic analysis. The results were observed to be accurate and reproducible. Manganese ; Spectrophotometry ; Chromium ; Chemistry ; Differential (mechanical device) ; Environmental chemistry ; Analytical Chemistry (journal) ; Chromatography ; Physics ; Organic chemistry Analytical chemistry methods development ; Chromium effects and bioremediation ; Extraction and Separation Processes
Authors: Huey Ling Tan | Ying Pei Lim | Mohomad Sufian So’aib
Source: International Journal of Engineering & Technology (article, 2018-06-23)
Field: Materials Science — Biomaterials

Tracking self-assembly morphology of cationic peptide analogues using turbidimetric- potentiometric titration Peptide amphiphiles are promising molecular materials for drug delivery systems and regenerative medicine through formation of well-ordered nanostructures. Rationally designed self-associating synthetic peptide analogues containing arginine (FEFEFRFR) and lysine (FEFEFKFK) were studied to understand the effect of pH and side chain interactions and its influence on the resulting nanostructure formation. Changes in structural conformation in bulk were followed by turbidimetric-potentiometric titrations with pH ranges from 4 to 11 and 4 to 12.6 for FEFEFKFK and FEFEFRFR respectively. Fourier Transform Infrared Spectroscopy (FTIR) was used to determine the secondary structure of the peptides while Field Emission Scanning Electron Microscopy (FE-SEM) was employed to study the morphology and dimension of higher order structures at various pH. Turbidity results showed that both FEFEFKFK and FEFEFRFR displayed higher turbidity level when their side chains were neutralized, in which FEFEFRFR showed higher turbidity level when arginine was neutralized. Both peptides exhibited similar self-assembly behavior in solution which mainly adopted antiparallel β-sheets conformation with spherical structures of different micro-metre size when there is a net charge of +2 or -2. FEFEFKFK was also found to form concaved disks and beads-on-a-string arrangement at pH 4. Both the peptide analogues were capable of forming smaller aggregates in a network of spherical structure at nanoscale when the net charge was near zero. This study ultimately provides a better understanding of predicting morphology and size of surface functional modification of self-assembled polypeptides. Potentiometric titration ; Chemistry ; Antiparallel (mathematics) ; Fourier transform infrared spectroscopy ; Self-assembly ; Amphiphile ; Peptide ; Cationic polymerization ; Nanostructure ; Titration ; Crystallography ; Analytical Chemistry (journal) ; Chemical engineering ; Materials science ; Nanotechnology ; Polymer chemistry ; Chromatography ; Organic chemistry ; Ion ; Copolymer ; Biochemistry ; Polymer Supramolecular Self-Assembly in Materials ; Antimicrobial Peptides and Activities ; Advanced biosensing and bioanalysis techniques
Authors: Miten Mistry | Dimitrios Letsios | Ruth Misener | G. Krennrich | Robert M. Lee
Source: Spiral (Imperial College London) (article, 2018-03-02)
Field: Materials Science — Materials Chemistry

Optimization with Gradient-Boosted Trees and Risk Control. Decision trees effectively represent the sparse, high dimensional and noisy nature of chemical data from experiments. Having learned a function from this data, we may want to thereafter optimize the function, e.g., picking the best chemical process catalyst. In this way, we may repurpose legacy predictive models. This work studies a large-scale, industrially-relevant mixed-integer quadratic optimization problem involving: (i) gradient-boosted pre-trained regression trees modeling catalyst behavior, (ii) penalty functions mitigating risk, and (iii) penalties enforcing composition constraints. We develop heuristic methods and an exact, branch-and-bound algorithm leveraging structural properties of gradient-boosted trees and penalty functions. We numerically test our methods on an industrial instance. Mathematical optimization ; Heuristic ; Decision tree ; Function (biology) ; Computer science ; Quadratic equation ; Integer (computer science) ; Scale (ratio) ; Process (computing) ; Mathematics ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Sadia Salsabil Bristy | Mohammad Mostafizar Rahman | Md. Mahbubor Rahman | M. A. Alam | Mohammad Rabiul Karim | Hasan Ahmad
Source: Preprints.org (preprint, 2018-07-11)
Field: Chemistry — Organic Chemistry

Epoxide Functional &amp;gamma;-Al2O3/Fe3O4/SiO2 Ceramic Nanocomposite Particles as Adsorbent for Reactive Azo Dye: Understanding Surface Property In this investigation magnetic &amp;gamma;-Al2O3 ceramic nanocomposite particles bearing epoxide functionality are prepared following a multistep process. The ultimate nanocomposite particles are named as &amp;gamma;-Al2O3/Fe3O4/SiO2/poly(glycidyl methacrylate (PGMA). The surface property is evaluated by carrying out the adsorption study of Remazol navy (RN), a model reactive azo dye, on both &amp;gamma;-Al2O3/Fe3O4/SiO2 and &amp;gamma;-Al2O3/Fe3O4/SiO2/PGMA nanocomposite particles. The adsorption is carried out at the point of zero charge (PZC) to neutralize the effect of particle surface charge. The adsorption rate is very fast, reached equilibrium (qe) value within five min. Due to mesoporous structure of silica layer &amp;gamma;-Al2O3/Fe3O4/SiO2 nanocomposite particles possessed relatively higher specific surface area and magnitude of adsorption is dependent on the total specific surface area. The introduction of epoxide functionality favored high adsorption capacity in mass per unit surface area. The adsorption process strictly followed Langmuir model. Thermodynamic equilibrium parameters implied that irrespective of surface functionality the adsorption process is spontaneous and exothermic. Pseudo-second-order rate kinetic model is more appropriate to explain the adsorption kinetics. Adsorption ; Point of zero charge ; Nanocomposite ; Chemical engineering ; Materials science ; Glycidyl methacrylate ; Specific surface area ; Mesoporous material ; Exothermic reaction ; Surface charge ; Composite material ; Chemistry ; Organic chemistry ; Physical chemistry ; Catalysis Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Gürcan GÜLER | Samim Yaşar
Source: DergiPark (Istanbul University) (article, 2018-08-16)
Field: Materials Science — Polymers and Plastics

Kermes Meşesi (Quercus coccifera L.) Odununun Bazı Kimyasal Özelliklerinin İncelenmesi ve Yongalevha Üretiminde Değerlendirilmesi Bu çalışmada, endüstriyel anlamda hammadde olabilecek nitelikteki kermes\nmeşesi (Quercus coccifera L.)\nyongalarının levha üretiminde kullanım olanakları araştırılmıştır. Çalışmada\nüretilen levhaların deneysel tasarımında, kermes meşesi ile kızılçam (Pinus brutia Ten.) yongalarının %0, 25,\n50, 75 ve 100 oranındaki karışımları kullanılmıştır. Çalışmada kullanılan\ntürlerin kimyasal ve termal özellikleri belirlenmiş ve bu özelliklerin üretilen\nlevhaların fiziksel ve mekanik özellikleri üzerine etkileri\ndeğerlendirilmiştir. Kermes meşesi yongarında, kızılçam yongalarına göre daha\nyüksek miktarlarda α-selüloz ve hemiselüloz, daha düşük miktarlarda ekstraktif\nmadde ve lignin tespit edilmiştir. Ana kimyasal bileşen analizleri FTIR\nanalizleriyle desteklenmiştir. Kermes\nmeşesi yongalarının kızılçama göre daha asidik olduğu görülmüştür. Monosakkarit\nbileşimi kermes meşesi yongalarında ramnoz (%0.34), arabinoz (%0.84), ksiloz\n(%21.24), mannoz (%2.04), galaktoz (%1.01) ve glukoz (%50.44), kızılçam\nyongalarında ise arabinoz (%1.72), ksiloz (%7.89), mannoz (%10.65), galaktoz\n(%2.11) ve glukoz (%46.50) olarak belirlenmiştir. Kermes meşesi yongalarının\nkızılçama göre termal dayanıklılığının daha yüksek olduğu görülmüştür. Üretilen\nlevhaların su alma ve kalınlığına şişme değerlerinin TS EN 312 standardının üstünde\nolduğu saptanmıştır. %25 ve %50 oranındaki kermes meşesi yongalarıyla üretilen\nlevhaların kuru şartlarda genel amaçlı kullanılmaları yönünde elastikiyet\nmodülü, eğilme direnci ve yüzeye dik çekme direnci değerlerinin TS EN 312 standardına\nuygun olduğu belirlenmiştir. Traditional medicine ; Chemistry ; Medicine Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Lignin and Wood Chemistry
Authors: Andrea Cossettini | Paolo Scarbolo | José A. Morales Escalante | Stadlbauer Benjamin | Muhammad Moazzam Naseer | Leila Taghizadeh | Clemens Heitzinger | L. Selmi
Source: Institutional Research Information System (University of Udine) (article, 2018-01-01)
Field: Chemistry — Electrochemistry

Calibration, Compensation, Parameter Estimation, and Uncertainty Quantification for Nanoelectrode Array Biosensors This paper presents the use of a statistical approach to estimate physical/electrochemical parameters of impedance spectroscopy experiments performed with a realistic nanoelectrodes array biosensor platform. The Bayesian estimation methodology is based on the combination of nanobiosensor simulations, performed with the ENBIOS tool, with Markov-Chain Monte Carlo (MCMC) analyses. A simple 1D electrode-electrolyte geometry is first considered as a validation test case, allowing the accurate estimation of Stern layer permittivity and salt concentration, as set by a reference analytical model. Then, full 3D analyses of the nanoelectrodes’ array system are performed in order to estimate a number of relevant parameters for measurements in electrolyte. Furthermore, moving to more challenging test cases, size/permittivity of microparticles suspended in electrolyte will also be discussed. This methodology allows for the determination of impedance spectroscopy data parameters, and quantification of parameter uncertainties in these multi-variable detection problems. It is thus a very promising approach in order to improve the precision of biosensor measurement predictions, which are intrinsically affected by many parameters. Calibration ; Compensation (psychology) ; Biosensor ; Uncertainty quantification ; Biological system ; Computer science ; Nanotechnology ; Materials science ; Mathematics ; Statistics ; Biology ; Machine learning Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Sensor Technology and Measurement Systems