A new coordination compound [Mg（L）（H2 O）5·H2 O]（NKU-109, H2 L=5-（4 H-1,2,4-triazol-4-yl）benzene-1,3-dicarboxylic acid） was solvothermally synthesized, featuring a supramolecular hydrogen-bonding network. ...A new coordination compound [Mg（L）（H2 O）5·H2 O]（NKU-109, H2 L=5-（4 H-1,2,4-triazol-4-yl）benzene-1,3-dicarboxylic acid） was solvothermally synthesized, featuring a supramolecular hydrogen-bonding network. A good proton conductivity of 5.87×10^-4S/cm was recorded at 70℃ and a relative humidity of75% in alternating current（AC） impedance experiment, which sheds a new light on the design of proton conduction materials based on coordination compounds.展开更多
To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are anal...To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.展开更多
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
Novel SPPESK/PAA composite proton exchange membranes with semi-interpenetrating polymer network （sIPN） structure have been synthesized through the in-situ polymerization of acrylic acid （AA） in the presence of sul...Novel SPPESK/PAA composite proton exchange membranes with semi-interpenetrating polymer network （sIPN） structure have been synthesized through the in-situ polymerization of acrylic acid （AA） in the presence of sulfonated poly （phthalazinone ether sulfone ketone） （SPPESK）. The composite membranes were identified by FT-IR analysis. Water uptake of the composite membranes was as high as 89.7% at 90℃, nearly one time higher than that of the corresponding SPPESK membrane.展开更多
In the present study the first 20 microatoms of the periodic table are named as bioatoms, since they are needed for biochemical functions and services to life itself. The rationale behind this project is the detailed ...In the present study the first 20 microatoms of the periodic table are named as bioatoms, since they are needed for biochemical functions and services to life itself. The rationale behind this project is the detailed analysis of the ionization energy of the electrons in the inside of the bioatoms and their orderly arrangement at specific positions. Such position of the electrons is harmonized with the succession of their energy values in their logarithmic curves. The compelling arrangement of each electron at a particular place would not be feasible in the absence of an energy offset, which is a proton. The latter would hold electrons on their proper position. These fundamental aspects of our project receive such strong support from the quantum theory, according to which it is the electromagnetic interaction between electrons and protons by the exchange of photons, which hold them together in the atoms. According to our proposed model, the protons of the aforementioned proton—electron twins, are distributed on distinct positions which are the junctions of a primary network, coupled with their electrons, in a secondary network. The geometry and distance of the protons, in the plexus, is constant and is calculated at 8, 42 ?. This was estimated by a mathematical analysis of the proposed model, as discussed further. Our study has shown that electrons in the specific positions are moving in a symmetrical oscillation in the length of a channel, in vacuum, under the Coulomb forces. Moreover, the chemical evidence suggests that electrons, in an atom, have their own separate orbit, and that these orbits are closely interrelated.展开更多
Low methanol permeability of proton exchange membranes (PEMs) is greatly important for direct methanol fuel cells (DMFCs). Here, sulfonated poly (ether ether ketone) (SPEEK) based semiinterpenetrating polymer networks...Low methanol permeability of proton exchange membranes (PEMs) is greatly important for direct methanol fuel cells (DMFCs). Here, sulfonated poly (ether ether ketone) (SPEEK) based semiinterpenetrating polymer networks (semi-IPNs) are successfully prepared by interpenetrating SPEEK into the in-situ synthesized crosslinking networks. The polymeric networks are formed by the covalent bonds between bromobenzyl groups of bro mo methylated poly (phenylene oxide) and amine groups of diamine linkers as well as the ionic bonds between amine species and sulfonated groups. Two linkers without and with sulfonated groups are applied to fabricate the semi-IPNs. The core properties of the membranes, like phase separation, water uptake, proton conductivity and methanol permeability, are systematically studied and compared. The DMFCs assembled by using the semi-IPN membranes display better performance than Nafion 117 in high concentration methanol solutions. The present work provides a facile way to prepare PEMs with enhanced DMFC performance.展开更多
Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work...Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.展开更多
This paper discusses dynamic characteristics of proton exchange membrane fuel cell (PEMFC) under rapid fluctuation of power demand. Wavelet neural network is adopted in the identification of the characteristic curve t...This paper discusses dynamic characteristics of proton exchange membrane fuel cell (PEMFC) under rapid fluctuation of power demand. Wavelet neural network is adopted in the identification of the characteristic curve to predict the voltage. The system control scheme of the voltage and power is introduced. The corresponding schemes for voltage and power control are studied. MATLAB is used to simulate the control system. The results reveal that the adopted control schemes can produce expected effects. Corresponding anti-disturbance and robustness simulation are also carried out. The simulation results show that the implemented control schemes have better robustness and adaptability.展开更多
基金supported by the 973 Program of China (No. 2014CB845600)the National Natural Science Foundation of China (Nos. 21421001 and 21531005)the Natural Science Foundation of Tianjin(No. 15JCZDJC38800)
文摘A new coordination compound [Mg（L）（H2 O）5·H2 O]（NKU-109, H2 L=5-（4 H-1,2,4-triazol-4-yl）benzene-1,3-dicarboxylic acid） was solvothermally synthesized, featuring a supramolecular hydrogen-bonding network. A good proton conductivity of 5.87×10^-4S/cm was recorded at 70℃ and a relative humidity of75% in alternating current（AC） impedance experiment, which sheds a new light on the design of proton conduction materials based on coordination compounds.
文摘To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
基金The authors gratefully acknowledge the financial supports of the National Natural Science Foundation of China （No. 50273005）.
文摘Novel SPPESK/PAA composite proton exchange membranes with semi-interpenetrating polymer network （sIPN） structure have been synthesized through the in-situ polymerization of acrylic acid （AA） in the presence of sulfonated poly （phthalazinone ether sulfone ketone） （SPPESK）. The composite membranes were identified by FT-IR analysis. Water uptake of the composite membranes was as high as 89.7% at 90℃, nearly one time higher than that of the corresponding SPPESK membrane.
文摘In the present study the first 20 microatoms of the periodic table are named as bioatoms, since they are needed for biochemical functions and services to life itself. The rationale behind this project is the detailed analysis of the ionization energy of the electrons in the inside of the bioatoms and their orderly arrangement at specific positions. Such position of the electrons is harmonized with the succession of their energy values in their logarithmic curves. The compelling arrangement of each electron at a particular place would not be feasible in the absence of an energy offset, which is a proton. The latter would hold electrons on their proper position. These fundamental aspects of our project receive such strong support from the quantum theory, according to which it is the electromagnetic interaction between electrons and protons by the exchange of photons, which hold them together in the atoms. According to our proposed model, the protons of the aforementioned proton—electron twins, are distributed on distinct positions which are the junctions of a primary network, coupled with their electrons, in a secondary network. The geometry and distance of the protons, in the plexus, is constant and is calculated at 8, 42 ?. This was estimated by a mathematical analysis of the proposed model, as discussed further. Our study has shown that electrons in the specific positions are moving in a symmetrical oscillation in the length of a channel, in vacuum, under the Coulomb forces. Moreover, the chemical evidence suggests that electrons, in an atom, have their own separate orbit, and that these orbits are closely interrelated.
基金support of the National Natural Science Foundation of China(Nos. 21603197, 21703212,21233006 and 21473164)Natural Science Foundation of Hubei Province of China(No.2016CFB181)+1 种基金Fundamental Research Funds for the Central University, China University of Geosciences (Wuhan)(No. CUGL180403)China University of Geosciences (Wuhan) for the program of Center for Advanced Energy Research and Technologies
文摘Low methanol permeability of proton exchange membranes (PEMs) is greatly important for direct methanol fuel cells (DMFCs). Here, sulfonated poly (ether ether ketone) (SPEEK) based semiinterpenetrating polymer networks (semi-IPNs) are successfully prepared by interpenetrating SPEEK into the in-situ synthesized crosslinking networks. The polymeric networks are formed by the covalent bonds between bromobenzyl groups of bro mo methylated poly (phenylene oxide) and amine groups of diamine linkers as well as the ionic bonds between amine species and sulfonated groups. Two linkers without and with sulfonated groups are applied to fabricate the semi-IPNs. The core properties of the membranes, like phase separation, water uptake, proton conductivity and methanol permeability, are systematically studied and compared. The DMFCs assembled by using the semi-IPN membranes display better performance than Nafion 117 in high concentration methanol solutions. The present work provides a facile way to prepare PEMs with enhanced DMFC performance.
文摘Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.
文摘This paper discusses dynamic characteristics of proton exchange membrane fuel cell (PEMFC) under rapid fluctuation of power demand. Wavelet neural network is adopted in the identification of the characteristic curve to predict the voltage. The system control scheme of the voltage and power is introduced. The corresponding schemes for voltage and power control are studied. MATLAB is used to simulate the control system. The results reveal that the adopted control schemes can produce expected effects. Corresponding anti-disturbance and robustness simulation are also carried out. The simulation results show that the implemented control schemes have better robustness and adaptability.
