Growth process of the NaY zeolite membranes was investigated by fluoride-containing precursor synthesis gel.Compared with the fluoride-free precursor synthesis gel,the irregular NaY zeolite crystals were dissolved int...Growth process of the NaY zeolite membranes was investigated by fluoride-containing precursor synthesis gel.Compared with the fluoride-free precursor synthesis gel,the irregular NaY zeolite crystals were dissolved into amorphous by the fluoride-containing precursor synthesis gel initially,the amorphous contained the Y-type zeolite characteristic bands by the IR characterization.The fine square NaY zeolite crystals arose from the amorphous,which were accumulated and gradually grew into a dense NaY zeolite layer on the support surface after 6.5 h.Because the excessive NaY zeolites were dissolved by the strong alkaline and fluoride-containing precursor synthesis gel,there was plenty of amorphous on NaY zeolites layer for prolonging the crystallization time.The assynthesized NaY zeolite membranes had a good separation performance and repeatability for separation of 10 wt%methanol(MeOH)/methyl methacrylate(MMA) mixture by pervaporation,the flux and separation factor were(1.27 ± 0.07) kg·M^(-2)·h^(-1) and(4900 ± 1500) at 323 K,respectively.Besides,the NaY zeolite membranes were applied to separate the other short chain alcohol from the various alcohol/organic ester and alcohol/organic ether mixtures,the NaY zeolite membranes showed high short chain alcohol perm-selectivity.展开更多
By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral...By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)image.With previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth data.Thus,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in reality.The learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse environments.To handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR problem.The authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS version.However,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction performance.To solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlabelled training samples.Specifically,the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for measuring the network modelling performance.By consolidating the DIL and DSL into a unified deep framework,the authors construct a more robust HSI SR method without any prior training and have great potential of flexible adaptation to different settings per obser-vation.To verify the effectiveness of the proposed approach,extensive experiments have been conducted on two benchmark HS datasets,including the CAVE and Harvard datasets,and demonstrate the great performance gain of the proposed method over the state-of-the-art methods.展开更多
We propose a convenient way of evaluating the mixing performance of static mixers used for round pipe by conducting flow visualization experiments under the turbulent region and using water as the main stream. A fluor...We propose a convenient way of evaluating the mixing performance of static mixers used for round pipe by conducting flow visualization experiments under the turbulent region and using water as the main stream. A fluorescent pigment, glycerin, two carboxymethyl cellulose solutions, and rapeseed oil were each injected upstream of the mixer. Three static mixer conditions were tested: 1) no static mixer;2) a Kenics-type static mixer;and 3) a multi-stacked elements (MSE) static mixer. The mixing trend downstream of the mixer in each condition and with each injection fluid was monitored using a laser and high-speed video camera system to obtain cross-sectional images. We propose suitable indexes based on the images obtained for quantitative evaluations of the mixing characteristics of static mixers.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 21868012 and 21968009)Jiangxi Provincial Department of Science and Technology (20171BCB24005, 20181ACH80003, 20192ACB80003 and 20192BBH80024)。
文摘Growth process of the NaY zeolite membranes was investigated by fluoride-containing precursor synthesis gel.Compared with the fluoride-free precursor synthesis gel,the irregular NaY zeolite crystals were dissolved into amorphous by the fluoride-containing precursor synthesis gel initially,the amorphous contained the Y-type zeolite characteristic bands by the IR characterization.The fine square NaY zeolite crystals arose from the amorphous,which were accumulated and gradually grew into a dense NaY zeolite layer on the support surface after 6.5 h.Because the excessive NaY zeolites were dissolved by the strong alkaline and fluoride-containing precursor synthesis gel,there was plenty of amorphous on NaY zeolites layer for prolonging the crystallization time.The assynthesized NaY zeolite membranes had a good separation performance and repeatability for separation of 10 wt%methanol(MeOH)/methyl methacrylate(MMA) mixture by pervaporation,the flux and separation factor were(1.27 ± 0.07) kg·M^(-2)·h^(-1) and(4900 ± 1500) at 323 K,respectively.Besides,the NaY zeolite membranes were applied to separate the other short chain alcohol from the various alcohol/organic ester and alcohol/organic ether mixtures,the NaY zeolite membranes showed high short chain alcohol perm-selectivity.
基金Ministry of Education,Culture,Sports,Science and Technology,Grant/Award Number:20K11867。
文摘By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)image.With previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth data.Thus,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in reality.The learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse environments.To handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR problem.The authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS version.However,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction performance.To solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlabelled training samples.Specifically,the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for measuring the network modelling performance.By consolidating the DIL and DSL into a unified deep framework,the authors construct a more robust HSI SR method without any prior training and have great potential of flexible adaptation to different settings per obser-vation.To verify the effectiveness of the proposed approach,extensive experiments have been conducted on two benchmark HS datasets,including the CAVE and Harvard datasets,and demonstrate the great performance gain of the proposed method over the state-of-the-art methods.
文摘We propose a convenient way of evaluating the mixing performance of static mixers used for round pipe by conducting flow visualization experiments under the turbulent region and using water as the main stream. A fluorescent pigment, glycerin, two carboxymethyl cellulose solutions, and rapeseed oil were each injected upstream of the mixer. Three static mixer conditions were tested: 1) no static mixer;2) a Kenics-type static mixer;and 3) a multi-stacked elements (MSE) static mixer. The mixing trend downstream of the mixer in each condition and with each injection fluid was monitored using a laser and high-speed video camera system to obtain cross-sectional images. We propose suitable indexes based on the images obtained for quantitative evaluations of the mixing characteristics of static mixers.
