jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yassine Mahdi | L Mouhi | N Guemras | Kamel Daoud
Source: Journal of Fundamental and Applied Sciences (article, 2018-03-14)
Field: Chemistry — Analytical Chemistry

Coupling the image analysis and the artificial neural networks to predict a mixing time of a pharmaceutical powder In recent years, different laboratories were interested in predicting the mixing time of a pharmaceutical powder. In fact, a nonhomogeneous mixture may lead to under dose and/or overdose of the active ingredient in the drug product. Our study is aimed toward using a new and revolutionary approach in the field of the processes “The Artificial Neural Networks” (ANN) by using the Neural Networks ToolboxTM derived from Matlab® software. The validation of the neural network was assumed by studying others mixing powder s and then we compared the experimental results to the data obtained by the neural network calculations. Experimental results were obtained from a non-destructive method (Image Analysis) which was used in order to characterize the homogeneity of powder mixture in a V-Blender as well as a Cubic Blender which are most used in the pharmaceutical industry.Keywords: ANN; Image analysis; Homogeneity; Back-propagation algorithm; multi-layer perceptron Artificial neural network ; Homogeneity (statistics) ; MATLAB ; Mixing (physics) ; Multilayer perceptron ; Backpropagation ; Biological system ; Perceptron ; Artificial intelligence ; Image (mathematics) ; Software ; Image processing ; Computer science ; Materials science ; Pattern recognition (psychology) ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals
Authors: Yifan Lu | Honghao Yue | Zongquan Deng | H. S. Tzou
Source: Chinese Journal of Mechanical Engineering (article, 2018-02-26)
Field: Materials Science — Materials Chemistry

Adaptive Shape Control for Thermal Deformation of Membrane Mirror with In-plane PVDF Actuators Optical membrane mirrors are promising key components for future space telescopes. Due to their ultra-thin and high flexible properties, the surfaces of these membrane mirrors are susceptible to temperature variations. Therefore adaptive shape control of the mirror is essential to maintain the surface precision and to ensure its working performance. However, researches on modeling and control of membrane mirrors under thermal loads are sparse in open literatures. A 0.2 m diameter scale model of a polyimide membrane mirror is developed in this study. Three Polyvinylidene fluoride (PVDF) patches are laminated on the non-reflective side of the membrane mirror to serve as in-plane actuators. A new mathematical model of the piezoelectric actuated membrane mirror in multiple fields, (i.e., thermal, mechanical, and electrical field) is established, with which dynamic and static behaviors of the mirror can be analyzed. A closed-loop membrane mirror shape control system is set up and a surface shape control method based on an influence function matrix of the mirror is then investigated. Several experiments including surface displacement tracking and thermal deformation alleviation are performed. The deviations range from 15 μm to 20 μm are eliminated within 0.1 s and the residual deformation is controlled to micron level, which demonstrates the effectiveness of the proposed membrane shape control strategy and shows a satisfactory real-time performance. The proposed research provides a technological support and instruction for shape control of optical membrane mirrors. Deformable mirror ; Actuator ; Materials science ; Membrane ; Polyvinylidene fluoride ; Optics ; Thermal ; Deformation (meteorology) ; Displacement (psychology) ; Tracking (education) ; Computer science ; Composite material ; Polymer ; Physics ; Artificial intelligence ; Chemistry Silicone and Siloxane Chemistry ; Advanced Surface Polishing Techniques ; Adaptive optics and wavefront sensing
Authors: Chuanqi Xie | Yong He
Source: International journal of agricultural and biological engineering (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging This study was carried out to investigate the feasibility of using visible and near infrared hyperspectral imaging for the variety classification of mung beans. Raw hyperspectral images of mung beans were acquired in the wavelengths of 380-1023 nm, and all images were calibrated by the white and dark reference images. The spectral reflectance values were extracted from the region of interest (ROI) of each calibrated hyperspectral image, and then they were treated as the independent variables. The dependent variables of four varieties of mung beans were set as 1, 2, 3 and 4, respectively. The extreme learning machine (ELM) model was established using full spectral wavelengths for classification. Modified gram-schmidt (MGS) method was used to identify effective wavelengths. Based on the selected wavelengths, the ELM and linear discriminant analysis (LDA) models were built. All models performed excellently with the correct classification rates (CCRs) covering 99.17%-99.58% in the training sets and 99.17%-100% in the testing sets. Fifteen wavelengths (432 nm, 455 nm, 468 nm, 560 nm, 705 nm, 736 nm, 760 nm, 841 nm, 861 nm, 921 nm, 930 nm, 937 nm, 938 nm, 959 nm and 965 nm) were recommended by MGS. The results demonstrated that hyperspectral imaging could be used as a non-destructive method to classify mung bean varieties, and MGS was an effective wavelength selection method. Keywords: visible and near-infrared hyperspectral imaging, mung bean, classification, modeling, wavelength selection DOI: 10.25165/j.ijabe.20181101.2655 Citation: Xie C Q, He Y. Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging. Int J Agric & Biol Eng, 2018; 11(1): 187–191. Hyperspectral imaging ; Mung bean ; Wavelength ; Linear discriminant analysis ; Near-infrared spectroscopy ; Mathematics ; Materials science ; Remote sensing ; Optics ; Artificial intelligence ; Computer science ; Physics ; Horticulture ; Statistics ; Biology ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xuan Zeng | Shibin Yin | Yin Guo | Jiarui Lin | Jigui Zhu
Source: Sensors (article, 2018-08-03)
Field: Chemistry — Analytical Chemistry

A Novel Semi-Supervised Feature Extraction Method and Its Application in Automotive Assembly Fault Diagnosis Based on Vision Sensor Data The fault diagnosis of dimensional variation plays an essential role in the production of an automotive body. However, it is difficult to identify faults based on small labeled sample data using traditional supervised learning methods. The present study proposed a novel feature extraction method named, semi-supervised complete kernel Fisher discriminant (SS-CKFDA), and a new fault diagnosis flow for automotive assembly was introduced based on this method. SS-CKFDA is a combination of traditional complete kernel Fisher discriminant (CKFDA) and semi-supervised learning. It adjusts the Fisher criterion with the data global structure extracted from large unlabeled samples. When the number of labeled samples is small, the global structure that exists in the measured data can effectively improve the extraction effects of the projected vector. The experimental results on Tennessee Eastman Process (TEP) data demonstrated that the proposed method can improve diagnostic performance, when compared to other Fisher discriminant algorithms. Finally, the experimental results on the optical coordinate data proves that the method can be applied in the automotive assembly process, and achieve a better performance. Automotive industry ; Artificial intelligence ; Pattern recognition (psychology) ; Fault (geology) ; Kernel Fisher discriminant analysis ; Kernel (algebra) ; Linear discriminant analysis ; Computer science ; Feature extraction ; Support vector machine ; Process (computing) ; Discriminant ; Fisher kernel ; Machine learning ; Data mining ; Engineering ; Kernel method ; Mathematics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Fault Detection and Control Systems
Authors: Kam Hamidieh
Source: arXiv (Cornell University) (preprint, 2018-03-04)
Field: Materials Science — Materials Chemistry

A Data-Driven Statistical Model for Predicting the Critical Temperature of a Superconductor We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor's chemical formula. The statistical model gives reasonable out-of-sample predictions: $\pm 9.5$ K based on root-mean-squared-error. Features extracted based on thermal conductivity, atomic radius, valence, electron affinity, and atomic mass contribute the most to the model's predictive accuracy. It is crucial to note that our model does not predict whether a material is a superconductor or not, it only gives predictions for superconductors. Superconductivity ; Statistical error ; Atomic radius ; RADIUS ; Statistical model ; Thermal conductivity ; Valence (chemistry) ; Statistical analysis ; Statistical physics ; Materials science ; Physics ; Condensed matter physics ; Thermodynamics ; Statistics ; Mathematics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Yulan Li | B. Zeidman | Shenyang Hu | Charles H. Henager | Theodore M. Besmann | Agnès Grandjean
Source: Computational Materials Science (article, 2018-12-10)
Field: Materials Science — Materials Chemistry

A physics-based mesoscale phase-field model for predicting the uptake kinetics of radionuclides in hierarchical nuclear wasteform materials Kinetics ; Materials science ; Diffusion ; Radionuclide ; Particle (ecology) ; Porosity ; Phase (matter) ; Chemical engineering ; Multiscale modeling ; Thermodynamics ; Chemistry ; Nuclear physics ; Composite material ; Physics Nuclear Materials and Properties ; Nuclear materials and radiation effects ; CO2 Sequestration and Geologic Interactions
Authors: Dali Cai | Yu Cui | Jia Zhao | Yao Wang | Fei Wei
Source: Frontiers of Chemical Science and Engineering (article, 2018-01-09)
Field: Chemistry — Inorganic Chemistry

High-precision diffusion measurement of ethane and propane over SAPO-34 zeolites for methanol-to-olefin process Propane ; Diffusion ; Olefin fiber ; Zeolite ; Chemistry ; Propene ; Diffusion process ; Coke ; Methanol ; Chemical engineering ; Analytical Chemistry (journal) ; Organic chemistry ; Catalysis ; Thermodynamics ; Innovation diffusion Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Zhigao Zhu | Jiaxiang Ma | Chenghan Ji | Yan Liu | Wei Wang | Fuyi Cui
Source: RSC Advances (article, 2018-01-01)
Field: Materials Science — Biomaterials

Nitrogen doped hierarchically structured porous carbon fibers with an ultrahigh specific surface area for removal of organic dyes Recently, tremendous efforts have been devoted to creating inexpensive porous carbon materials with a high specific surface area (SSA) as adsorbents or catalysts for the efficient removal of organic pollutants. Here, activated porous carbon fibers with hierarchical structures were designed and constructed by an electrospinning technique, <i>in situ</i> polymerization, and activation and carbonization processes. Benefiting from the precursor fiber design and subsequent activation techniques, the activated porous carbon fibers (APCFs) derived from a benzoxazine/polyacrylonitrile (BA-a/PAN) precursor exhibited an ultrahigh SSA of 2337.16 m<sup>2</sup> g<sup>-1</sup> and a pore volume of 1.24 cm<sup>3</sup> g<sup>-1</sup>, showing excellent adsorption capacity toward methylene blue (MeB, 2020 mg g<sup>-1</sup>). Interestingly, the APCFs after pre-adsorption of MeB also display robust activation of peroxymonosulfate (PMS) with singlet oxygen for the ultrafast removal of MeB. Meanwhile, the synergistic effect of adsorption and a catalytic oxidation reaction using APCFs can realize outstanding total organic carbon (TOC) removal in a comparatively short time. Moreover, a synergistic adsorption-oxidation mechanism for promoting the removal of MeB using APCFs was proposed. This study is useful for the design and development of novel metal-free carbon adsorbents, catalysts or catalyst carriers with an ultrahigh SSA for various applications. Porosity ; Nitrogen ; Carbon fibers ; Chemical engineering ; Materials science ; Doping ; Porous medium ; Specific surface area ; Nanotechnology ; Chemistry ; Organic chemistry ; Optoelectronics ; Composite material ; Catalysis ; Composite number Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Supercapacitor Materials and Fabrication
Authors: Farheen Khan | Rizwan Wahab | Mohamed Hagar | Rua B. Alnoman | Lutfullah | Mohd Rashid
Source: Nanomaterials (article, 2018-02-27)
Field: Materials Science — Materials Chemistry

Nanotransition Materials (NTMs): Photocatalysis, Validated High Effective Sorbent Models Study for Organic Dye Degradation and Precise Mathematical Data’s at Standardized Level The present work describes the synthesis of copper oxide nanoparticles (CuONPs) via a solution process with the aim of applying the nano-adsorbent for the reduction of methylene blue (MB) dye in alkaline media. These NPs were characterized via Field emission scanning electron microscopy (FE-SEM), X-ray diffraction, high-resolution Transmission electron microscopy (TEM), and ultra violet UV-visible spectroscopy to confirm their morphology and crystalline and optical properties in order to design an adsorption-degradation process. The photocatalytic CuONPs exhibited dynamic properties, great adsorption affinity during the chemisorption process, and operated at various modes with a strong interaction between the adsorbent and the adsorptive species, and equilibrium isotherm, kinetic isotherm, and thermodynamic activities in the presence of UV light. All basic quantities, such as concentration, pH, adsorbent dose, time, and temperature, were determined by an optimization process. The best-fitted adsorption Langmuir model (<i>R</i>² = 0.9988) and performance, including adsorption capacity (350.87 mg/g), photocatalytic efficiency (90.74%), and degradation rate constant (<i>K</i>s = 2.23 ×10<sup>-2</sup> min<sup>-1</sup>), illustrate good feasibility with respect to sorption-reduction reactions but followed a pseudo-second-order kinetic on the adsorbent surface, reaching an equilibrium point in 80 min. The thermodynamic analysis suggests that the adsorption reaction is spontaneous and endothermic in nature. The thermodynamic parameters such as enthalpy (∆H°), entropy (∆S°), and Gibbs free energy (∆G°) give effective results to support a chemical reduction reaction at 303 K temperature. The equilibrium isotherm and kinetic and thermodynamic models with error function analysis explore the potential, acceptability, accuracy, access to adsorbents, and novelty of an unrivaled-sorption system. Adsorption ; Endothermic process ; Gibbs free energy ; Enthalpy ; Langmuir adsorption model ; Sorption ; Photocatalysis ; Chemistry ; Chemisorption ; Reaction rate constant ; Materials science ; Chemical engineering ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Kinetics ; Catalysis Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Aditi Singhal | Mary Laura Lind
Source: International Journal of Nanomedicine (article, 2018-03-01)
Field: Materials Science — Materials Chemistry

Removal of pesticide toxicity by cysteine-capped Ag nanoparticles and study of their adsorption kinetics A new method has been developed for one-step synthesis of cysteine-capped Ag nanoparticles. The particles have been characterized by several techniques, including ultraviolet-visible spectroscopy, infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The interaction of these nanoparticles has been seen with two pesticides, namely, chlorpyrifos and malathion, which are major water pollutants. Adsorption ; Transmission electron microscopy ; Nanoparticle ; Scanning electron microscope ; Malathion ; Kinetics ; Materials science ; Chlorpyrifos ; Infrared spectroscopy ; Nuclear chemistry ; Fourier transform infrared spectroscopy ; Spectroscopy ; Chemical engineering ; Chemistry ; Pesticide ; Nanotechnology ; Organic chemistry Nanoparticles: synthesis and applications ; Pesticide Exposure and Toxicity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Dorothee Childs | Karsten Bach | Holger Franken | Simon Anders | Nils Kurzawa | Marcus Bantscheff | Mikhail M. Savitski | Wolfgang Huber
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-07-22)
Field: Chemistry — Spectroscopy

Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins Abstract Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present non-parametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP datasets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. To facilitate access to a wide range of users, a freely available software implementation of NPARC is provided. Proteome ; Parametric statistics ; Computational biology ; Replication (statistics) ; Drug discovery ; False discovery rate ; Computer science ; Data mining ; Biological system ; Bioinformatics ; Biology ; Statistics ; Mathematics ; Biochemistry Advanced Proteomics Techniques and Applications ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Bo Chen | Shenxu Bao | Yimin Zhang | Ruwei Zheng
Source: Minerals (article, 2018-08-19)
Field: Chemistry — Inorganic Chemistry

A High-Efficiency Approach for the Synthesis of N235-Impregnated Resins and the Application in Enhanced Adsorption and Separation of Vanadium(V) Trialkylamine (N235)-tributyl phosphate (TBP) impregnated resins (N-TIRs) were prepared, so as to evaluate the effects of the addition of TBP on the preparation and adsorption performance of N235-impregnated resins (NIRs). The results show that TBP can obviously increase the impregnation ratio and shorten the impregnation equilibrium time of the N-TIRs when compared to that of the NIRs (57.73% versus 36.95% and 5 min versus 240 min). It is confirmed that TBP can interact with N235 during the impregnation process, which shorten the adsorption equilibrium time and increases the adsorption capacity of the N-TIRs for V(V) when compared to that of the NIRs (6 h versus 10 h and 50.95 mg·g−1 versus 46.73 mg·g−1). The kinetics fitting results demonstrate that the adsorption of V(V) onto N-TIRs and NIRs all conform to pseudo-second order kinetic model and chemical reaction is the rate-limiting step of the whole adsorption process. In the meanwhile, the reaction constant (Ks) implies that the chemical reaction rate of V(V) with the impregnated extractants in N-TIRs is faster than that in NIRs. The N-TIRs present higher stability and selectivity than NIRs. This study manifests that the addition of a secondary reagent may be a potential and novel technique on the preparation of SIRs and the enhancement of adsorption and separation for ions. Adsorption ; Reagent ; Tributyl phosphate ; Chemistry ; Selectivity ; Vanadium ; Reaction rate constant ; Phosphate ; Kinetics ; Chemical stability ; Nuclear chemistry ; Inorganic chemistry ; Chromatography ; Catalysis ; Organic chemistry ; Extraction (chemistry) Radioactive element chemistry and processing ; Extraction and Separation Processes ; Metal Extraction and Bioleaching
Authors: L. Arnoldi | M. Spies | Jonathan Houard | Ivan Blum | Auriane Etienne | Р. Р. Исмагилов | A. N. Obraztsov | Angela Vella
Source: Applied Physics Letters (article, 2018-04-02)
Field: Materials Science — Materials Chemistry

Thermal diffusivity of diamond nanowires studied by laser assisted atom probe tomography The thermal properties of single-crystal diamond nanowires (NWs) have been calculated from first principles but have never been measured experimentally. Taking advantage of the sharp geometry of samples analyzed in a laser assisted atom probe, this technique is used to measure the thermal diffusivity of a single NW at low temperature (&amp;lt;300 K). The obtained value is in good agreement with the ab-initio calculations and confirms that thermal diffusivity in nanoscale samples is lower than in bulk samples. The results impact the design and integration of diamond NWs and nanoneedles in nanoscale devices for heat dissipation. Thermal diffusivity ; Nanowire ; Diamond ; Materials science ; Atom probe ; Nanolithography ; Nanoscopic scale ; Laser flash analysis ; Atom (system on chip) ; Ab initio ; Laser ; Ab initio quantum chemistry methods ; Nanotechnology ; Condensed matter physics ; Thermal conductivity ; Thermodynamics ; Optics ; Chemistry ; Composite material ; Fabrication ; Molecule Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques ; Force Microscopy Techniques and Applications
Authors: N. A. Poklonski | С. А. Вырко | А. И. Ковалев | A. N. Dzeraviaha
Source: Journal of Physics Communications (article, 2018-01-16)
Field: Materials Science — Materials Chemistry

Drift-diffusion model of hole migration in diamond crystals via states of valence and acceptor bands Ionization equilibrium and dc electrical conductivity of crystalline diamond are considered, for the temperature T j in the vicinity of which valence band (v-band) conductivity is approximately equal to hopping conductivity via acceptors. For the first time, we find explicitly (in the form of definite integrals) the fundamental ratio of diffusion coefficient to drift mobility for both v-band holes and holes hopping via hydrogen-like acceptors for the temperature T j . The known ratios follow from the obtained ones as particular cases. The densities of the spatial distributions of acceptors and hydrogenlike donors as well as of holes are considered to be Poissonian and the fluctuations of electrostatic potential energy are considered to be Gaussian. The dependence of exchange energy of v-band holes on temperature is taken into account. The thermal activation energy of hopping conduction as a function of the concentration of boron atoms (as acceptors) is calculated for temperature T T 2 j 3 . Without the use of any adjustable parameters, the results of calculations quantitatively agree with data obtained from the measurements of hopping conductivity of diamond with boron concentration from 3 10 17 to 3 10 20 cm -3 , i.e. on the insulating side of the Mott phase transition. Acceptor ; Diamond ; Condensed matter physics ; Conductivity ; Thermal conduction ; Boron ; Ionization ; Activation energy ; Diffusion ; Materials science ; Hydrogen ; Band gap ; Valence (chemistry) ; Electrical resistivity and conductivity ; Chemistry ; Atomic physics ; Molecular physics ; Ion ; Thermodynamics ; Physics ; Physical chemistry Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Thermal properties of materials