The semi-interpenetrating network anion exchange membranes(AEMs)based on quaternized polyvinyl alcohol(QPVA)and poly(-diallyldimethylammonium chloride)(PDDA)are synthesized.The chemical cross-linking structure is form...The semi-interpenetrating network anion exchange membranes(AEMs)based on quaternized polyvinyl alcohol(QPVA)and poly(-diallyldimethylammonium chloride)(PDDA)are synthesized.The chemical cross-linking structure is formed between hydroxyl groups of QPVA and aldehyde groups of glutaraldehyde(GA),which makes PDDA more stable embed in the QPVA matrix and also improves the mechanical properties and dimensional stability of AEMs.Due to the phase separation phenomenon of AEMs swelling in water,a microporous structure may be formed in the membrane,which reduces the transmission resistance of hydroxide ions and provides a larger space for the transfer of hydroxide ions,thus improving the conductivity.The ring structure of PDDA is introduced as a cationic group to transfer hydroxide ions,and shields the nucleophilic attack of the hydroxide ions through the steric hindrance effect,which improves alkaline stability.The hydroxide conductivity of semi-interpenetrating network membrane(QPVA/PDDA0.5-GA)is 36.5 mS cm^(-1) at 60℃.And the membrane of QPVA/PDDA0.5-GA exhibits excellent mechanical property with maximum tensile strength of 19.6 MPa.After immersing into hot 3 mol L^(-1) NaOH solutions at 60℃ for 300 h,the OHconductivity remains 78%of its initial value.The semi-interpenetrating network AEMs with microporous structure exhibit good ionic conductivity,mechanical strength and alkaline durability.展开更多
A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as...A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as they are variable and varied nonlinearly in response to varied voltage and power. In this paper the equivalent electrical parameters of DBD are predicted using a neural network, which is beneficial for the design of power supply and helps to investigate how the electrical parameters influence the equivalent load parameters. The electrical parameters includ- ing voltage and power are determined to be the inputs of the neural network model, as these two parameters greatly influence the discharge type and the equivalent DBD load parameters which are the outputs of the model. The voltage and power are decoupled with pulse density modula- tion （PDM） and hence the impact of the two electrical parameters is discussed individually. The neural network model is trained with the back-propagation （BP） algorithm. The obtained neural network model is evaluated by the relative error, and the prediction has a good agreement with the practical values obtained in experiments.展开更多
基金The authors gratefully acknowledge the financial support of this work by Natural Science Foundation of China(grant no.s 51673030,51603017 and 51803011)Jilin Provincial Science&Technology Department(grant no.s 20200801011GH,20180101209JC,20160520138JH,20160519020JH)+1 种基金Jilin Province Development and Reform Commission(Grant nos:2019C042-5)ChangBai Mountain Scholars Program of Jilin Province.
文摘The semi-interpenetrating network anion exchange membranes(AEMs)based on quaternized polyvinyl alcohol(QPVA)and poly(-diallyldimethylammonium chloride)(PDDA)are synthesized.The chemical cross-linking structure is formed between hydroxyl groups of QPVA and aldehyde groups of glutaraldehyde(GA),which makes PDDA more stable embed in the QPVA matrix and also improves the mechanical properties and dimensional stability of AEMs.Due to the phase separation phenomenon of AEMs swelling in water,a microporous structure may be formed in the membrane,which reduces the transmission resistance of hydroxide ions and provides a larger space for the transfer of hydroxide ions,thus improving the conductivity.The ring structure of PDDA is introduced as a cationic group to transfer hydroxide ions,and shields the nucleophilic attack of the hydroxide ions through the steric hindrance effect,which improves alkaline stability.The hydroxide conductivity of semi-interpenetrating network membrane(QPVA/PDDA0.5-GA)is 36.5 mS cm^(-1) at 60℃.And the membrane of QPVA/PDDA0.5-GA exhibits excellent mechanical property with maximum tensile strength of 19.6 MPa.After immersing into hot 3 mol L^(-1) NaOH solutions at 60℃ for 300 h,the OHconductivity remains 78%of its initial value.The semi-interpenetrating network AEMs with microporous structure exhibit good ionic conductivity,mechanical strength and alkaline durability.
基金supported by National Natural Science Foundation of China(Nos.51107115,11347125,51407156)China Postdoctoral Science Foundation(Nos.20110491766,2014M551735)
文摘A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as they are variable and varied nonlinearly in response to varied voltage and power. In this paper the equivalent electrical parameters of DBD are predicted using a neural network, which is beneficial for the design of power supply and helps to investigate how the electrical parameters influence the equivalent load parameters. The electrical parameters includ- ing voltage and power are determined to be the inputs of the neural network model, as these two parameters greatly influence the discharge type and the equivalent DBD load parameters which are the outputs of the model. The voltage and power are decoupled with pulse density modula- tion （PDM） and hence the impact of the two electrical parameters is discussed individually. The neural network model is trained with the back-propagation （BP） algorithm. The obtained neural network model is evaluated by the relative error, and the prediction has a good agreement with the practical values obtained in experiments.
