jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abbas Dadras | Ali Benvidi | Mansoor Namazian | Saleheh Abbasi | Dehghan Tezerjani | Moharram Roozegari | Reza Tabaraki
Source: Journal of the Serbian Chemical Society (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Determination of the pKa for caffeic acid in mixed solvent using the net analyte signal method and ab initio theory Due to the biological effects of phenolic acid components, polyphenol-rich foods are a significant part of human and animal diets. In this study, the acidity constants of caffeic acid (3,4-dihydroxycinnamic acid) in binary mixtures of ethanol?water were determined spectrophotometrically using the introduced net analyte signal (NAS) algorithm and an ab initio quantum mechanical method. The NAS is an efficient chemometric algorithm for analysis of acid?base equilibrium systems by a spectrophotometric method. At different pH values, the distribution of acid species is obtained from an absorption data matrix and this procedure enabled the pKa of caffeic acid to be obtained alternatively. The results showed that pKa1 (4.02, 4.26, 4.39, 4.57 and 5.11) and pKa2 (8.43, 8.68, 8.79, 9.00 and 9.34) were increased by increasing the percent ethanol in water (0, 10, 20, 30 and 40 vol. %) and these results were in agreement with the results of the Gaussian method. The ab initio calculated Gibbs energy change showed that para-hydroxy group is more acidic than meta-hydroxy group. The red shifts of different species of caffeic acid obtained using the ab initio quantum mechanical method are in good agreement with the results of UV?Vis spectroscopy. Caffeic acid ; Chemistry ; Ab initio ; Ab initio quantum chemistry methods ; Analyte ; Gibbs free energy ; Computational chemistry ; Physical chemistry ; Analytical Chemistry (journal) ; Chromatography ; Molecule ; Organic chemistry ; Thermodynamics Free Radicals and Antioxidants ; Electrochemical sensors and biosensors ; Water Quality Monitoring and Analysis
Authors: Abderrahmane Ayadi | M. Deléglise | Chung Hae Park | Patricia Krawczak
Source: Frontiers in Materials (article, 2019-10-11)
Field: Materials Science — Polymers and Plastics

Analysis of Impregnation Mechanism of Weft-Knitted Commingled Yarn Composites by Staged Consolidation and Laboratory X-Ray Computed Tomography Absorption-based microcomputed tomography (µCT) scans using polychromatic X-ray sources are frequently used to analyse the microstructure of polymer-matrix composites. Assuming the proportionality between linear X-ray attenuation coefficients and grey levels in the reconstructed µCT images, segmentation techniques can be used to conduct qualitative and quantitative analyses. Nevertheless, the reliability of such analyses is limited in partially consolidated composites formed of commingled yarns due to low contrast induced by beam hardening and partial volume effect. This paper aims at investigating the possibility of using low-contrast µCT images to analyse fibre bed deformation and impregnation mechanism by staged consolidation of a weft-knitted fabric of commingled thermoplastic/glass yarns. The experimental work is focused on the effect of the compaction ratio of the consolidation cycle. Absorption-based µCT analyses are conducted for representative samples using a voxel size of 10 µm and a constant energy-level of the X-rays source. Preliminary qualitative analyses of the raw reconstructed images indicate that beam hardening is the most significant in the case of the non-consolidated sample and that partial volume effect makes it difficult to visually distinguish between dry and impregnated zones inside the yarns at a compaction ratio of 63%. Thus, two image-quality descriptors, viz. contrast and signal-to-noise ratio are evaluated based on the K-means clustering method to follow their variation for four consolidation levels. The corresponding volume fractions of glass fibres, thermoplastic matrix and voids are compared with the results of a second weight-controlled thresholding method. Considering that the weight-controlled thresholding method is less sensitive to beam hardening, it is confirmed that obtaining reliable segmentation results based on the K-means method requires a contrast around 0.5 and signal-to-noise ratio lower than 3. The results from the comparison with the classical consolidation scenario of commingled yarns confirm the potential of using µCT images of different qualities to characterize partially consolidated weft-knitted fabrics. Materials science ; Composite material ; Thermoplastic ; Consolidation (business) ; Compaction ; Tomography ; X-ray microtomography ; Optics Textile materials and evaluations ; Mechanical Behavior of Composites ; Optical measurement and interference techniques
Authors: Chenchen Xie | Xi Li | Houpeng Chen | Yang Li | Yuan-Guang Liu | Qian Wang | Kun Ren | Zhitang Song
Source: Micromachines (article, 2019-07-08)
Field: Materials Science — Materials Chemistry

Speeding Up the Write Operation for Multi-Level Cell Phase Change Memory with Programmable Ramp-Down Current Pulses Multi-level cell (MLC) phase change memory (PCM) can not only effectively multiply the memory capacity while maintaining the cell area, but also has infinite potential in the application of the artificial neural network. The write and verify scheme is usually adopted to reduce the impact of device-to-device variability at the expense of a greater operation time and more power consumption. This paper proposes a novel write operation for multi-level cell phase change memory: Programmable ramp-down current pulses are utilized to program the RESET initialized memory cells to the expected resistance levels. In addition, a fully differential read circuit with an optional reference current source is employed to complete the readout operation. Eventually, a 2-bit/cell phase change memory chip is presented with a more efficient write operation of a single current pulse and a read access time of 65 ns. Some experiments are implemented to demonstrate the resistance distribution and the drift. Phase-change memory ; Reset (finance) ; Computer science ; Memory cell ; Power (physics) ; Computer hardware ; Current (fluid) ; Power consumption ; Memory refresh ; Chip ; Electronic engineering ; Electrical engineering ; Semiconductor memory ; Computer memory ; Voltage ; Phase change ; Transistor ; Engineering ; Telecommunications Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Semiconductor materials and devices
Authors: Shamima Akter Smriti | Shah Alimuzzaman Belal | Md. Mahbubul Haque | Ismail Hossain | Nawshin Farzana | Abu Naser Md Ahsanul Haque
Source: Tekstilec (article, 2019-09-26)
Field: Materials Science — Polymers and Plastics

Prognosis of Dimensional Stability and Mass per Unit Area of Single Jersey Cotton Knitted Fabric with Fuzzy Inference System Endeavour has been made in this research work using experimental data for constructing a fuzzy inference model based on the Mamdani approach to prognosticate the shrinkage and mass per unit area of a single jersey cotton knitted fabric. To control the dimensional stability of the cotton knitted fabric in advance, an artifi cial intelligent system is required in the knitting industry which simulates all product and process variables and is able to give human-like decisions in advance. The most important controlling parameters of knitted fabric properties such as stitch length, yarn count and overfeed percentage in stenter were considered as input variables, and mass per unit area, lengthwise shrinkage and widthwise shrinkage as output variables. Overall, 35 experiments were conducted to construct the model, varying diff erent parameters. The applicability of the model was validated by comparing the results from 15 newly conducted experiments. The coeffi cient of determination of predicted and actual data for mass per unit area, lengthwise shrinkage and widthwise shrinkage were 0.97, 0.99 and 0.99, respectively which validates the model relatively eff ectively for an industrial application. The proposed model can assist a fabric manufacturer by taking a decision in selecting knitting and fi nishing parameters prior to producing the fabric. Moreover, it can reduce the time and energy required, and waste produced in the process by skipping the sample development step before bulk production. Fuzzy inference system ; Stability (learning theory) ; Unit (ring theory) ; Inference ; Fuzzy logic ; Fuzzy inference ; Computer science ; Mathematics ; Artificial intelligence ; Fuzzy control system ; Machine learning ; Adaptive neuro fuzzy inference system ; Mathematics education Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color perception and design
Authors: Marco Fedrizzi | Francesca Antonucci | Giulio Sperandio | Simone Figorilli | Federico Pallottino | Corrado Costa
Source: Spanish Journal of Agricultural Research (article, 2019-03-08)
Field: Chemistry — Analytical Chemistry

An artificial neural network model to predict the effective work time of different agricultural field shapes The aim of this study was to find a model able to extract the net time per unit of net worked area from different agricultural field basic shapes (square, circle, rectangle and triangle) considering the following variables: field gross area, working speed, number of turnings (these depending on the effective working width), side length parallel and orthogonal to working direction, and working direction type. Being this a non-linear problem, an approach based on artificial neural networks is proposed. The model was trained using an artificial dataset calculated for the various shapes (internal test) and then tested on 47 different agricultural operations extracted by a real field dataset for the estimation of the net time (external test). The net time records obtained from both, the trained model and the external test, were correlated and the performance parameter r was extracted. Both regression coefficients (r), for the training and internal test, appear to be excellent being equal to 0.98 with respect to traditional linear approach (0.13). The variable “number of turnings” scored the highest impact, with a value equal to 44.34% for the net time estimation. Finally, the r correlation parameter for the external test resulted to be very high (0.80). This information is very valuable of the use of information management system for precision agriculture. Artificial neural network ; Rectangle ; Mathematics ; Computer science ; Field (mathematics) ; Statistics ; Net (polyhedron) ; Test data ; Variable (mathematics) ; Artificial intelligence ; Geometry ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Fupeng Ni | Xiaowen Zhu | Gu Fang | Yaohua Hu
Source: Food Science & Nutrition (article, 2019-10-03)
Field: Chemistry — Analytical Chemistry

Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x-loading weights (x-LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of "Fuji" and "Qinguan" apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for "Fuji" and "Qinguan" apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the "Fuji" apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x-LW for the "Qinguan" apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for "Fuji" and "Qinguan" apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both "Fuji" and "Qinguan" apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection. Wavelength ; Materials science ; Nondestructive testing ; Partial least squares regression ; Mathematics ; Optoelectronics ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Horticultural and Viticultural Research
Authors: Igor L. Shabalin
Source: Russian Journal of Non-Ferrous Metals (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Prospects of Nanotechnology and Design of Materials Based on Refractory Compounds The prospects for the development of high-temperature ceramic materials science are considered as a matter of discussion. The substantiation of the development of hetero-modulus ceramic composites as the possibility to implement unique physicochemical features of refractory compounds (carbides, nitrides, borides, etc.) under their application conditions at high and ultrahigh temperatures is presented. The prospects for a nanotechnology-based approach to the fabrication of such materials in engineering are shown. Ceramic ; Materials science ; Refractory (planetary science) ; Nanotechnology ; Fabrication ; Carbide ; Refractory metals ; Nitride ; Metallurgy MXene and MAX Phase Materials ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: John Bradshaw | Matt J. Kusner | Brooks Paige | Marwin Segler | José Miguel Hernández-Lobato
Source: UCL Discovery (University College London) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Generating molecules via chemical reactions Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the difficulty in knowing how to synthesize these molecules. We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules. More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules. Modeling the entire process of constructing a molecule during generation offers a number of advantages. First, we show that such a model has the ability to generate a wide, diverse set of valid and unique molecules due to the useful inductive biases of modeling reactions. Second, modeling synthesis routes rather than final molecules offers practical advantages to chemists who are not only interested in new molecules but also suggestions on stable and safe synthetic routes. Third, we demonstrate the capabilities of our model to also solve one-step retrosynthesis problems, predicting a set of reactants that can produce a target product. Retrosynthetic analysis ; Mirroring ; Computer science ; Generative model ; Set (abstract data type) ; Generative grammar ; Process (computing) ; Molecule ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Peng Liu | Chao Liu | Shiwei Li | Bingguo Liu | Junjie Shi | Libo Zhang | Jinhui Peng
Source: Environmental Progress & Sustainable Energy (article, 2019-03-03)
Field: Materials Science — Materials Chemistry

Desorption kinetic study of mercury species in spent mercury chloride catalyst from polyvinyl chloride production process In order to improve the hazard‐free treatment and comprehensive utilization of the spent chloride mercury catalysts, reaction kinetics, and diffusion process of the thermal desorption of mercury were investigated. For reaction kinetics, activation energy of the reactions in the temperature range 436.15–694.15 K was calculated by five different model‐free methods, among which, FWO (105.47 kJ·mol −1 ) model‐free method with the best fitting precision was applied as the parameter to deduce the reaction models and describe the kinetic mechanism. It was confirmed that three diffusion models (D1, D2, D3) dominated at the beginning/middle of the reactions and one reaction order model (F2) dominated at the later part. According to the deduced mechanism, diffusion enhancing and reaction temperature enhancing strategies were introduced. For diffusion process of mercury volatiles, the laminar state of the protective gas was confirmed when the nitrogen flow rate is 100 L/h; based on the boundary simplifications, the thickness and the pressure difference of the boundary layer were calculated. According to the results of pressure difference, mercury diffusion process has two stages: Higher pressure diffusion stage and Residual mercury removal stage. Reaction kinetics and diffusion calculation give a better understanding of the mercury removal process and the parameters obtained in this work are useful to design the reactor. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019 Mercury (programming language) ; Chemistry ; Desorption ; Catalysis ; Chloride ; Activation energy ; Order of reaction ; Chemical kinetics ; Thermodynamics ; Kinetics ; Chemical engineering ; Physical chemistry ; Reaction rate constant ; Organic chemistry ; Adsorption Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Recycling and Waste Management Techniques
Authors: Junfeng Zhu | Keisuke Nakakubo | Yuya Mikami | Hiroaki Yoshioka | Kinichi Morita | Yuji Oki
Source: Optical Materials Express (article, 2019-02-07)
Field: Materials Science — Surfaces, Coatings and Films

Investigation and modeling of UV band-pass-filtering white compound materials for potting or embedding in micro-optical applications A novel white compound material with spectral band pass filtering was proposed and demonstrated in the UV region for wavelengths smaller than 310 nm. This compound material can be obtained by dispersing CaF2 in polydimethylsiloxane (PDMS). Refractive index matching (RIM) of the compound material resulted in transmittance of up to 80% at RIM wavelength. In no-RIM wavelength region, refractive index difference about 0.005 could result in transmittance of down to 30% due to intense Rayleigh isotropic scattering. A good reproducibility of the transparent wavelength can be obtained with the specific CaF2 particle and PDMS matrix in this study. Meanwhile, simple calculation based on Rayleigh-Gans-Debye approximation [J. Am. Ceram. Soc.86(3), 480 (2003)] in the soft diffusion model can explain the CaF2: PDMS compound film transmittance spectrum in the UV region, and the bandwidth narrowing condition. Materials science ; Transmittance ; Polydimethylsiloxane ; Refractive index ; Optics ; Wavelength ; Rayleigh scattering ; Optoelectronics ; Light scattering ; Scattering ; Composite material Optical Coatings and Gratings ; Photonic and Optical Devices ; Semiconductor Lasers and Optical Devices
Authors: Methary Jaipal | Abhijit Chatterjee
Source: Modelling and Simulation in Materials Science and Engineering (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Mesoscale understanding of ionic conduction in yttria stabilized zirconia: the nanoscale percolation network and its effect on O <sup>2−</sup> ion movement* Abstract Yttria-stabilized zirconia (YSZ) is widely used as a fast oxygen ion conductor in solid oxide fuel cells. Over the years several studies have probed the effect of different cation arrangements on the O 2− ion hopping behavior at the atomistic scale. However, an analytical model that can predict the macroscopic ionic conductivity using both the atomic scale hopping behavior as well as the spatial arrangement of cations at the mesoscopic length scale is lacking. A novel mesoscale model is constructed as a step towards addressing this gap. First, using a kinetic Monte Carlo (KMC) model for YSZ we find evidence of a fast ion conducting percolation network being present. The tortuous network, which consists of connected regions spanning the material structure, mainly contributes to the ionic conduction as O 2− ion movement in other regions is relatively slow. The topology, composition and O 2− ion movement in the network are analyzed. Next, the shortest path lengths in the network are identified with the help of the Dijsktra algorithm. Finally, a diffusion model is developed that relates the atomic scale hopping rates and shortest path lengths (a mesoscale feature) to the macroscale ionic conductivity. Estimates for ionic conductivity from the diffusion model are in excellent agreement with the KMC model. Changes within the percolation network with increasing Y 2 O 3 content can describe the maximum observed in ionic conductivity. Materials science ; Percolation (cognitive psychology) ; Nanoscopic scale ; Mesoscale meteorology ; Ion ; Yttria-stabilized zirconia ; Cubic zirconia ; Ionic bonding ; Ionic conductivity ; Thermal conduction ; Chemical physics ; Nanotechnology ; Composite material ; Ceramic ; Physical chemistry ; Meteorology ; Physics ; Electrode Advancements in Solid Oxide Fuel Cells ; Ionic liquids properties and applications ; Catalysis and Oxidation Reactions
Authors: Parisa Shiri | Earl Jon S. Dacanay | Brennan Hagen | Loren G. Kaake
Source: The Journal of Physical Chemistry C (article, 2019-07-23)
Field: Materials Science — Polymers and Plastics

Dynamic Charging Mechanism of Organic Electrochemical Devices Revealed with In Situ Infrared Spectroscopy The steps responsible for device charging in organic electrochemical transistors were investigated using in situ infrared spectroscopy. Metal–electrolyte-dielectric–organic semiconductor capacitor structures were fabricated on infrared waveguides and measured using the total internal reflection sampling method. Upon the application of a voltage, charges were induced in the device, creating polaron absorption features in the mid-infrared region. The dynamics of the device charging were investigated by varying the channel length, dielectric layer thickness, and organic semiconductor layer thickness. Device charging was independent of the channel length but depended strongly on the semiconductor and dielectric layer thicknesses, indicating that the movement of ions is the primary determining factor for device charging kinetics. A quantitative model is developed combining an resistor-capacitor (RC) circuit model for the dielectric layer and a mixed ion–carrier diffusion model for the organic semiconductor layer. The model is evaluated against an RC circuit model for electrochemical charging dynamics, which is used in the popular Bernards model. Thickness-dependent dynamics cannot be adequately explained using the RC circuit model. The model presented here is significantly more appropriate for fundamental studies of ion dynamics in pi-conjugated materials. Materials science ; Capacitor ; Semiconductor ; Optoelectronics ; Organic semiconductor ; Attenuated total reflection ; Dielectric ; Electrolyte ; Infrared ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Electrode ; Chemistry ; Voltage ; Optics ; Electrical engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Electrochemical Analysis and Applications