Synthesis and evaluation of the Cr(VI) extraction ability of amino/nitrile calix[4]arenes immobilized onto a polymeric backbone

2006 ◽  
Vol 66 (11) ◽  
pp. 1342-1349 ◽  
Author(s):  
Shahabuddin Memon ◽  
Mustafa Tabakci ◽  
D. Max Roundhill ◽  
Mustafa Yilmaz
Keyword(s):  
Extraction Ability ◽  
Amino Nitrile

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

The Quest for Carbenic Nitrile Imines: Experimental and Computational Characterization ofC-Amino Nitrile Imine

2015 ◽  
Vol 2015 (34) ◽  
pp. 7484-7493 ◽  
Author(s):  
Cláudio M. Nunes ◽  
Igor Reva ◽  
Mário T. S. Rosado ◽  
Rui Fausto
Keyword(s):  
Nitrile Imine ◽  
Nitrile Imines ◽  
Amino Nitrile

Self-Assembled Monolayers of 12-Mercaptododecylphosphonic Acid on Titania Particles; Application to the Extraction of Heavy Metals

2004 ◽  
Vol 847 ◽  
Author(s):  
Sébastien Anthérieu ◽  
Florence Brodard-Séverac ◽  
Gilles Guerrero ◽  
P. Hubert Mutin
Keyword(s):  
Heavy Metals ◽  
Chemical Analysis ◽  
Phosphonic Acid ◽  
Metal Extraction ◽  
Self Assembled Monolayers ◽  
Extraction Ability ◽  
Self Assembled Monolayer ◽  
Inorganic Hybrid ◽  
Assembled Monolayers ◽  
Self Assembled

ABSTRACTTitania particles (P25 DEGUSSA AG) were treated by a solution of 12-mercaptododecyl-phosphonic acid (MDPA) in toluene to obtain an organic-inorganic hybrid material with thiol functions at the surface. This material was characterized by chemical analysis, solid state 31P MAS NMR and FTIR spectroscopies, and XPS. Reaction of the phosphonic acid end of MDPA with the TiO2 surface led to the formation of a relatively dense and well-ordered self-assembled monolayer (SAM), with a density of about 3.9 SH functions per nm2. This material was tested for the extraction of mercury in water at pH 7, 9, and 11. Chemical analysis indicated no loss of SH functionality, even at pH 11. The best extraction results were obtained at pH 9 and 11, with a metal extraction ability between 85 and 90%.

scholarly journals Improving Electron Extraction Ability and Device Stability of Perovskite Solar Cells Using a Compatible PCBM/AZO Electron Transporting Bilayer

Nanomaterials ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 720 ◽  
Author(s):  
Hang Dong ◽  
Shangzheng Pang ◽  
Yi Zhang ◽  
Dazheng Chen ◽  
Weidong Zhu ◽  
...  
Keyword(s):  
Solar Cells ◽  
Perovskite Solar Cells ◽  
Metal Electrode ◽  
Extraction Ability ◽  
Time Resolved ◽  
Recombination Lifetime ◽  
Transient Photovoltage ◽  
Device Stability ◽  
Hole Transporting ◽  
Electron Extraction

Due to the low temperature fabrication process and reduced hysteresis effect, inverted p-i-n structured perovskite solar cells (PSCs) with the PEDOT:PSS as the hole transporting layer and PCBM as the electron transporting layer have attracted considerable attention. However, the energy barrier at the interface between the PCBM layer and the metal electrode, which is due to an energy level mismatch, limits the electron extraction ability. In this work, an inorganic aluminum-doped zinc oxide (AZO) interlayer is inserted between the PCBM layer and the metal electrode so that electrons can be collected efficiently by the electrode. It is shown that with the help of the PCBM/AZO bilayer, the power conversion efficiency of PSCs is significantly improved, with negligible hysteresis and improved device stability. The UPS measurement shows that the AZO interlayer can effectively decrease the energy offset between PCBM and the metal electrode. The steady state photoluminescence, time-resolved photoluminescence, transient photocurrent, and transient photovoltage measurements show that the PSCs with the AZO interlayer have a longer radiative carrier recombination lifetime and more efficient charge extraction efficiency. Moreover, the introduction of the AZO interlayer could protect the underlying perovskite, and thus, greatly improve device stability.

scholarly journals Experimental investigation and modeling of thermophysical and extraction properties of choline chloride + DL-malic acid based deep eutectic solvent

2017 ◽  
Vol 82 (11) ◽  
pp. 1287-1302 ◽  
Author(s):  
Jelena Vuksanovic ◽  
Nina Todorovic ◽  
Mirjana Kijevcanin ◽  
Slobodan Serbanovic ◽  
Ivona Radovic
Keyword(s):  
Malic Acid ◽  
Atmospheric Pressure ◽  
Choline Chloride ◽  
Deep Eutectic Solvent ◽  
Ternary Mixtures ◽  
Extraction Ability ◽  
Experimental Phase ◽  
Liquid Liquid Extraction ◽  
Equilibrium Data ◽  
Extraction Properties

The ability of non-toxic and biodegradable deep eutectic solvent (DES) choline chloride + DL-malic acid in mole ratio 1:1, for the breaking of the azeotropes heptane + methanol and toluene + methanol by means of liquid? ?liquid extraction was evaluated. Ternary liquid?liquid equilibrium experiments were performed at 298.15 K and at atmospheric pressure. Densities, viscosities and refractive indices of DES + methanol and water + DES systems were experimentally determined over a wide temperature range and at atmospheric pressure. Additionally, the viscosities of DES + glycerol mixture were - determined at temperatures up to 363.15 K to check how much the addition of glycerol decreases high viscosities of DES. The results indicate that the addition of small amounts of water or glycerol as a third component significantly decreases the viscosity of the investigated deep eutectic solvent. Based on the selectivity and distribution ratio values, the extraction ability of the investigated deep eutectic solvent, in comparison with the conventionally used solvents, yields promising results. Non-random two-liquid (NRTL) and universal quasichemical (UNIQUAC) models were satisfactorily applied for correlation of experimental phase equilibrium data for two ternary mixtures.

scholarly journals Extraction Ability of 4-Benzoyl-3-phenyl-5-isoxazolone towards 4f-Ions into Ionic and Molecular Media

2018 ◽  
Vol 34 (8) ◽  
pp. 973-978 ◽  
Author(s):  
Maria ATANASSOVA ◽  
Hiroyuki OKAMURA ◽  
Ayano EGUCHI ◽  
Yuki UEDA ◽  
Tsuyoshi SUGITA ◽  
...  
Keyword(s):  
Extraction Ability

scholarly journals A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1841
Author(s):  
Linjie Li ◽  
Mian Zhang ◽  
Kesheng Wang
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Extraction Ability ◽  
Position Information ◽  
Vibration Signals ◽  
Test Dataset ◽  
Horizontal Vibration ◽  
Automatic Feature Extraction ◽  
Real Practice

Deep learning-based intelligent fault diagnosis methods have attracted increasing attention for their automatic feature extraction ability. However, existing works are usually under the assumption that the training and test dataset share similar distributions, which unfortunately always violates real practice due to the variety of working conditions. In this paper, an end-to-end scheme of joint use of two-direction signals and capsule network (CN) is proposed for fault diagnosis of rolling bearing. With the help of the superior ability of CN in capturing the spatial position information between features, more valuable information can be mined. Aiming to eliminate the influence of different rotational speeds, vertical and horizontal vibration signals are fused as the input to CN, so that invariant features can be extracted automatically from the raw signals. The effectiveness of the proposed method is verified by experimental data of rolling bearing under different rotational speeds and compared with a deep convolutional neural network (DCNN). The results demonstrate that the proposed scheme is able to recognize the fault types of rolling bearing under scenarios of different rotational speeds.

scholarly journals The Optimized Multi-Scale Permutation Entropy and Its Application in Compound Fault Diagnosis of Rotating Machinery

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 170 ◽  
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yu Wei ◽  
Yongbo Li
Keyword(s):  
Time Delay ◽  
Parameter Selection ◽  
Permutation Entropy ◽  
Embedding Dimension ◽  
Optimization Strategy ◽  
Extraction Ability ◽  
Dynamic Changes ◽  
Multi Scale ◽  
Simulated Signal ◽  
Selection Of

Multi-scale permutation entropy (MPE) is a statistic indicator to detect nonlinear dynamic changes in time series, which has merits of high calculation efficiency, good robust ability, and independence from prior knowledge, etc. However, the performance of MPE is dependent on the parameter selection of embedding dimension and time delay. To complete the automatic parameter selection of MPE, a novel parameter optimization strategy of MPE is proposed, namely optimized multi-scale permutation entropy (OMPE). In the OMPE method, an improved Cao method is proposed to adaptively select the embedding dimension. Meanwhile, the time delay is determined based on mutual information. To verify the effectiveness of OMPE method, a simulated signal and two experimental signals are used for validation. Results demonstrate that the proposed OMPE method has a better feature extraction ability comparing with existing MPE methods.

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