Investigation of the behavior of hydrogen‐bonded phenolic compounds and their determination by using poly(vinylferrocenium)–polyaniline composite film

The hydrogen bonding between phenolic compounds (phenol (Ph), catechol (Ct), resorcinol (Rs), and hydroquinone (Hq)) is investigated at pH 4. The oxidation behaviors of total phenolic compounds (TotPh) are different from their individual behaviors due to the existence of intermolecular hydrogen‐bonded oligomeric clusters. Theoretical calculations and voltammetric and spectroscopic evidences support the intermolecular hydrogen bonding. The interaction of the phenolic compounds with polyaniline (PANI) and poly(vinylferrocenium) (PVF⁺) films are also investigated electrochemically and spectroscopically. The phenolic molecules are immobilized in both polymers due to the construction of hydrogen bonds by PANI and the complexation with PVF⁺. In addition, Ct and Hq are catalytically oxidized by PANI. Determinations of Ct and TotPh are performed on PVF⁺–PANI composite ‐ coated Pt electrode using amperometric I–t method. Composite coating exhibits significant electrochemical activity toward Ct and TotPh, with high sensitivity and a wide linearity range. The steady‐state currents versus concentration of Ct and TotPh are found to be linear in the range of 1.35 × 10^?3?50.0 mM and 4.10 × 10^?4?560 mM for two linear regions, respectively. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43596.     

A probabilistic object tracking model based on condensation algorithm

Object tracking, which has many application in our daily life, is an important topic in electronics engineering area. It basically deals with estimation and location of an object in given video frames. In this paper, a novel object tracking algorithm based on particle filtering associate with population balances is proposed. The developed algorithm was used to track objects in synthetic frames and natural video frames. According to results, it has high accuracy level for single and multi-object tracking.     

Stochastic modeling of particle motion along a sliding conveyor

The sliding conveyor consists of a plane surface, known as the track, along which particles are induced to move by vibrating the bed sinusoidal with respect to time. The forces on the particle include gravity, bed reaction force and friction. Because friction coefficients are inherently variable, particle motion along the bed is erratic and unpredictable. A deterministic model of particle motion (where friction is considered to be known and invariant) is selected and its output validated by experiment. Two probabilistic solution techniques are developed and applied to the deterministic model, in order to account for the randomness that is present. The two methods consider particle displacement to be represented by discrete time and continuous time random processes, respectively, and permits analytical solutions for mean and variance in displacement versus time to be found. These are compared with experimental measurements of particle motion. Ultimately this analysis can be employed to calculate residence‐time distributions for such items of process equipment. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Monitoring of hydrogenation with various catalyst ratios

Soybean oil was hydrogenated using two different nickel-based commercial catalysts (Nysosel 222 and SP-10) at various ratios in 4-L reactors under constant conditions (165°C, 2 bar hydrogen pressure, and 500 rpm stirring rate). Trans isomer formation, reaction rates, selectivity (S) ratios, and melting behaviors of the samples were monitored during the reactions. When Nysosel 222 was used at 0.02, 0.03, and 0.04%, iodine values (IV) were reduced from 130.1 to 70.6, 50.9, and 44.7 and total trans isomers increased from 0 to 34.2, 43.3, and 40.5%, respectively, after 100 min of hydrogenation. However, SP-10 reduced IV from 130.1 to 77.2, 75.7, and 71.3 after 100 min when used at 0.1, 0.15, and 0.2%, respectively, whereas total trans isomers were 58.6, 70.4, and 70.7%. Reaction rates increased with catalyst ratio and time but were higher for Nysosel 222 than for SP-10 although 5–10 times less Nyosel 22 was used than SP-10. Linoleate selectivity (S ₃₂) was almost constant for Nysosel 222, whereas it was higher but fell with time for SP-10. Increasing the catalyst ratio decreased the time needed to reach the highest oleate selectivity (S ₂₁) ratios, and the IV values where the highest S ₂₁ were attained were different for the catalysts. Increases in m.p. of SP-10 samples were slower after IV values of 80 were attained, where S ₂₁ ratios reached to higher values. Solid fat contents (SFC) of these samples fell markedly above 21.1°C, and steeped SFC curves were obtained.     

Comparison of two palladium catalysts on different supports during hydrogenation

Soybean oil was hydrogenated using two different palladium-based catalysts, 5% palladium on carbon (Pd/C) and 10% palladium on alumina (Pd/A), at various ratios in a 4-L reactor under constant conditions (165°C, 2 bar H₂, and 500 rpm stirring rate). Reaction rate, trans isomer formation, selectivity ratios, and melting behaviors of the samples were monitored. Activity of Pd/C was about 10 times higher than that of Pd/A, and the reaction rate showed a strong dependency on the support material. Increases in the concentrations of both Pd catalysts did not have considerable effect on trans formation, which is slightly dependent on support material. Oleate selectivity (S ₂₁) for all runs varied between 2.48 and 30.34, and type of support material did not have an effect on selectivity. Melting behaviors of the samples were mainly dependent on reaction rates.     

低温冷却对AISI 304不锈钢铣削加工的影响

研究低温冷却对AISI304不锈钢铣削加工的影响。用内径为1mm的管子来喷淋液氮到铣刀、削口和材料界面起到低温冷却的作用。液氮的流量为5.2L/min;采用顺铣和逆铣2个铣削方向、干铣和低温冷却2种方式和80、120、160和200m/min 4种铣削速度。结果表明：低温冷却和铣削速度对切削力有影响;低温冷却过程中的切削力和扭矩比干铣过程中的高;切削力随着铣削速度的加快而增大。进入铣刀半径范围内的削屑影响低速顺铣的效果。     

The experimental investigation of the effects of uncoated,PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

In this study the machining of AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated cemented carbide insert with different feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45 mm/rev with the cutting speeds of 100, 200 and 300 m/min by keeping depth of cuts constant (i.e. 2 mm), without using cooling liquids has been accomplished. The surface roughness effects of coating method, coating material, cutting speed and feed rate on the workpiece have been investigated. Among the cutting tools—with 200 mm/min cutting speed and 0.25 mm/rev feed rate—the TiN coated with PVD method has provided 2.16 μm, TiAlN coated with PVD method has provided 2.3 μm, AlTiN coated with PVD method has provided 2.46 μm surface roughness values, respectively. While the uncoated cutting tool with the cutting speed of 100 m/min and 0.25 mm/rev feed rate has yielded the surface roughness value of 2.45 μm. Afterwards, these experimental studies were executed on artificial neural networks (ANN). The training and test data of the ANNs have been prepared using experimental patterns for the surface roughness. In the input layer of the ANNs, the coating tools, feed rate (f) and cutting speed (V) values are used while at the output layer the surface roughness values are used. They are used to train and test multilayered, hierarchically connected and directed networks with varying numbers of the hidden layers using back-propagation scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) algorithms with the logistic sigmoid transfer function. The experimental values and ANN predictions are compared by statistical error analyzing methods. It is shown that the SCG model with nine neurons in the hidden layer has produced absolute fraction of variance (R²) values about 0.99985 for the training data, and 0.99983 for the test data; root mean square error (RMSE) values are smaller than 0.00265; and mean error percentage (MEP) are about 1.13458 and 1.88698 for the training and test data, respectively. Therefore, the surface roughness value has been determined by the ANN with an acceptable accuracy.     

Effects of hydrogenation parameters on trans isomer formation,selectivity and melting properties of fat

Effects of hydrogenation conditions (temperature, hydrogen pressure, stirring rate) on trans fatty acid formation, selectivity and melting behavior of fat were investigated. To this aim, soybean oil was hydrogenated under various conditions and fatty acid composition, trans isomer formation, slip melting point (SMP), solid fat content (SFC) and iodine number (IV) of the samples withdrawn at certain intervals of the reactions were monitored. A constant ratio (0.03%) of Nysosel 222 was used in the various combinations of temperature (150, 165 and 180 °C), stirring speed (500, 750 and 1000 rpm) and hydrogen pressure (1, 2 and 3 bar). Raising the temperature increased the formation of fatty acid isomers, whereas higher stirring rates decreased this formation, while changes in hydrogen pressure had no effect or slightly reduced it, depending on other parameters. Results also indicated that the trans fatty acid ratio increased with IV reduction, reached the highest value when the IV was about 70 and decreased at IV < 70 due to saturation. Selectivity values (S₂₁) at that point ranged between 5.78 and 11.59. Lower temperatures and higher stirring rates decreased not only the trans isomer content but also the S₂₁ values at significant levels. However, same effects were not observed with the changes in hydrogen pressure. It was determined that a high SMP does not necessarily mean a high SFC. Selective conditions produced samples with higher SFC but lower SMP, which is possibly because of higher trans isomer formation as well as lower saturation.     

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.     

TENET: a new hybrid network architecture for adversarial defense

International Journal of Information Security - Deep neural network (DNN) models are widely renowned for their resistance to random perturbations. However, researchers have found out that these...