The separation of ethylene glycol(EG)and 1,2-butanediol(1,2-BDO)azeotrope in the synthesis process of EG via coal and biomass is becoming of increasing commercial and environmental importance.Selective adsorption is d...The separation of ethylene glycol(EG)and 1,2-butanediol(1,2-BDO)azeotrope in the synthesis process of EG via coal and biomass is becoming of increasing commercial and environmental importance.Selective adsorption is deemed as the most promising methods because of energy saving and environment favorable.In this paper,NaY zeolite was used to separate 1,2-BDO from EG,and its adsorption properties was then investigated.The isotherms of EG and 1,2-BDO in vapor and liquid phases from 298 to 328 K indicated that they fitted Langmuir model quite well,and the NaY zeolite absorbent favored EG more than 1,2-BDO.The Grand Canonical Monte Carlo(GCMC)and molecular dynamics(MD)simulation techniques were conducted to investigate the competition adsorption and diffusion characteristics in different adsorption regions.It was observed that EG and 1,2-BDO molecules all have the most probable locations of the center of the 12-membered ring near the Na cations.The diffusivities of EG are lower than those of 1,2-BDO at the same adsorption concentration.At last,the breakthrough curves of the binary mixture regressed from the empirical Dose–Response model in fixed-bed column showed that the adsorption selectivity of EG could reach to as high as 2.43,verified that the NaY zeolite could effectively separate EG from 1,2-BDO.This work is also helpful for further separation of other dihydric alcohol mixtures from coal and biomass fermentation.展开更多
In this study,nanosheet g-C_(3)N_(4)-H_(2) was prepared by thermal exfoliation of bulk g-C_(3)N_(4) under hydrogen.A series of Ru/g-C_(3)N_(4)-H_(2) catalysts with Ru species supported on the nanosheet g-C_(3)N_(4)-H_...In this study,nanosheet g-C_(3)N_(4)-H_(2) was prepared by thermal exfoliation of bulk g-C_(3)N_(4) under hydrogen.A series of Ru/g-C_(3)N_(4)-H_(2) catalysts with Ru species supported on the nanosheet g-C_(3)N_(4)-H_(2) were synthesized via ultrasonic assisted impregnation-deposition method.Ultrafine Ru nanoparticles(<2 nm)were highly dispersed on nanosheet g-C_(3)N_(4)-H_(2).Strong interaction due to Ru-Nx coordination facilitated the uniform distribution of Ru species.Meanwhile,the involvement of surface basicity derived from abundant nitrogen sites was favourable for enhancing the selective hydrogenation performance of bi-benzene ring,i.e.,almost complete 4,40-diaminodiphenylmethane(MDA)conversion and>99%4,40-diaminodicyclohexylmethane selectivity,corresponding to a reaction activity of 35.7 mol_(MDA) mol_(Ru)^(-1) h^(-1).Moreover,the reaction activity of catalyst in the fifth run was 36.5 mol_(MDA) mol_(Ru)^(-1) h^(-1),which was comparable with that of the fresh one.The computational results showed that g-C_(3)N_(4) as support was favorable for adsorption and dissociation of H_(2) molecules.Moreover,the substrate scope can be successfully expanded to a variety of other aromatic diamines.Therefore,this work provides an efficient and green catalyst system for selective hydrogenation of aromatic diamines.展开更多
Tree mortality models play an important role in predicting tree growth and yield,but existing mortality models for Larix gmelinii subsp.principis-rupprechtii,an important species used for regeneration and afforestatio...Tree mortality models play an important role in predicting tree growth and yield,but existing mortality models for Larix gmelinii subsp.principis-rupprechtii,an important species used for regeneration and afforestation in northern China,have overlooked potential regional influences on tree mortality.This study used data acquired from 102 temporary sample plots(TSPs)in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest(n=67)and state-owned Boqiang Forest(n=35)in northern China.To model stand-level tree mortality,we compared seven model forms of county data.Three continuous(dominant height,plot mean diameter,and basal area per hectare)and one dummy variable with two levels(region)were used as fixed effects variables.Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models.Results showed that tree mortality significantly positively correlated with stand basal area and dominant height,but negatively correlated with stand mean diameter.Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements,and Hurdle Poisson mixed-effects model showed the most attractive fit statistics(largest R^(2)and smallest RMSE)when employing leave-one-out cross-validation.These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.展开更多
Transparent electrode based on silver nanowires(Ag NWs) emerges as an outstanding alternative of indium tin oxide film especially for flexible electronics. However, the conductivity of Ag NWs transparent electrode is ...Transparent electrode based on silver nanowires(Ag NWs) emerges as an outstanding alternative of indium tin oxide film especially for flexible electronics. However, the conductivity of Ag NWs transparent electrode is still dramatically limited by the contact resistance between nanowires at high transmittance. Polyvinylpyrrolidone(PVP) layer adsorbed on the nanowire surface acts as an electrically insulating barrier at wire–wire junctions, and some devastating post-treatment methods are proposed to reduce or eliminate PVP layer, which usually limit the application of the substrates susceptible to heat or pressure and burden the fabrication with high-cost, time-consuming, or inefficient processes. In this work, a simple and rapid pre-treatment washing method was proposed to reduce the thickness of PVP layer from 13.19 to0.96 nm and improve the contact between wires. Ag NW electrodes with sheet resistances of 15.6 and 204 X sq-1have been achieved at transmittances of 90 and 97.5 %, respectively. This method avoided any post-treatments and popularized the application of high-performance Ag NW transparent electrode on more substrates. The improved Ag NWs were successfully employed in a capacitive pressure sensor with high transparency, sensitivity, and reproducibility.展开更多
There is an urgent global need for wireless communication utilizing materials that can provide simultaneous flexibility and high conductivity.Avoiding the harmful effects of electromagnetic(EM)radiation from wireless ...There is an urgent global need for wireless communication utilizing materials that can provide simultaneous flexibility and high conductivity.Avoiding the harmful effects of electromagnetic(EM)radiation from wireless communication is a persistent research hot spot.Two-dimensional(2D)materials are the preferred choice as wireless communication and EM attenuation materials as they are lightweight with high aspect ratios and possess distinguished electronic properties.MXenes,as a novel family of 2D materials,have shown excellent properties in various fields,owing to their excellent electrical conductivity,mechanical stability,high flexibility,and ease of processability.To date,research on the utility of MXenes for wireless communication has been actively pursued.Moreover,MXenes have become the leading materials for EM attenuation.Herein,we systematically review the recent advances in MXene-based materials with different structural designs for wireless communication,electromagnetic interference(EMI)shielding,and EM wave absorption.The relationship governing the structural design and the effectiveness for wireless communication,EMI shielding,and EM wave absorption is clearly revealed.Furthermore,our review mainly focuses on future challenges and guidelines for designing MXene-based materials for industrial application and foundational research.展开更多
The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to patients.Low-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises qual...The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to patients.Low-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises quality and decreases diagnostic performance.Researchers have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques,which have achieved impressive results.However,most of these methods are directly performed on reconstructed LDCT images,in which some subtle structures and details are readily lost during the reconstruction procedure,and convolutional neural network(CNN)-based methods for raw LDCT projection data are rarely reported.To address this problem,we adopted an attention residual dense CNN,referred to as AttRDN,for LDCT sinogram denoising.First,it was aided by the attention mechanism,in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT sinograms.Then,the denoised sinogram was restored by subtracting the noise obtained from the input noisy sinogram.Finally,the CT image was reconstructed using filtered back-projection.The experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art methods.Importantly,it can prevent the loss of detailed information and has the potential for clinical application.展开更多
Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under differen...Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.展开更多
Inspired by total variation(TV), this paper represents a new iterative algorithm based on diagonal total variation(DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reco...Inspired by total variation(TV), this paper represents a new iterative algorithm based on diagonal total variation(DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reconstructed image, we used DTV to sparsely represent images when iterative convergence of the reconstructed algorithm with TV-constraint had no effect during the reconstruction process. To investigate our proposed algorithm, the numerical and experimental studies were performed, and rootmean-square error(RMSE) and structure similarity(SSIM)were used to evaluate the reconstructed image quality. The results demonstrated that the proposed method could effectively reduce noise, suppress artifacts, and reconstruct highquality image from incomplete projection data.展开更多
In recent years,online reservation systems of country hotel have become increasingly popular in rural areas.How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved.Aiming...In recent years,online reservation systems of country hotel have become increasingly popular in rural areas.How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved.Aiming at the problem of cold start and data sparseness in recommendation,a Hybrid Recommendation method based on Graph Embedding(HRGE)is proposed.First,three types of network are built,including user-user network based on user tag,househouse network based on house tag,and user-user network based on user behavior.Then,by using the method of graph embedding,three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes.Finally,these characterization vectors are used to make a hybrid recommendation.The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province.The experimental results show that,compared with traditional recommendation algorithms,the comprehensive evaluation index(F1)of the HRGE is improved by 20% and the Mean Average Precision(MAP)is increased by 11%.展开更多
基金the National Natural Science Foundation of China(21576272)“Transformational Technologies for Clean Energy and Demonstration”Strategic Priority Research Program of Chinese Academy of Sciences,Grant No.XDA 21030600,Science and Technology Service Network Initiative,Chinese Academy of Sciences(KFJ-STS-QYZD-138).
文摘The separation of ethylene glycol(EG)and 1,2-butanediol(1,2-BDO)azeotrope in the synthesis process of EG via coal and biomass is becoming of increasing commercial and environmental importance.Selective adsorption is deemed as the most promising methods because of energy saving and environment favorable.In this paper,NaY zeolite was used to separate 1,2-BDO from EG,and its adsorption properties was then investigated.The isotherms of EG and 1,2-BDO in vapor and liquid phases from 298 to 328 K indicated that they fitted Langmuir model quite well,and the NaY zeolite absorbent favored EG more than 1,2-BDO.The Grand Canonical Monte Carlo(GCMC)and molecular dynamics(MD)simulation techniques were conducted to investigate the competition adsorption and diffusion characteristics in different adsorption regions.It was observed that EG and 1,2-BDO molecules all have the most probable locations of the center of the 12-membered ring near the Na cations.The diffusivities of EG are lower than those of 1,2-BDO at the same adsorption concentration.At last,the breakthrough curves of the binary mixture regressed from the empirical Dose–Response model in fixed-bed column showed that the adsorption selectivity of EG could reach to as high as 2.43,verified that the NaY zeolite could effectively separate EG from 1,2-BDO.This work is also helpful for further separation of other dihydric alcohol mixtures from coal and biomass fermentation.
基金financially supported by the National Nature Science Foundation of China(21576272)“Transformational Technologies for Clean Energy and Demonstration”,Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA 21030600)Science and Technology Service Network Initiative,Chinese Academy of Sciences(KFJ-STS-QYZD-138).
文摘In this study,nanosheet g-C_(3)N_(4)-H_(2) was prepared by thermal exfoliation of bulk g-C_(3)N_(4) under hydrogen.A series of Ru/g-C_(3)N_(4)-H_(2) catalysts with Ru species supported on the nanosheet g-C_(3)N_(4)-H_(2) were synthesized via ultrasonic assisted impregnation-deposition method.Ultrafine Ru nanoparticles(<2 nm)were highly dispersed on nanosheet g-C_(3)N_(4)-H_(2).Strong interaction due to Ru-Nx coordination facilitated the uniform distribution of Ru species.Meanwhile,the involvement of surface basicity derived from abundant nitrogen sites was favourable for enhancing the selective hydrogenation performance of bi-benzene ring,i.e.,almost complete 4,40-diaminodiphenylmethane(MDA)conversion and>99%4,40-diaminodicyclohexylmethane selectivity,corresponding to a reaction activity of 35.7 mol_(MDA) mol_(Ru)^(-1) h^(-1).Moreover,the reaction activity of catalyst in the fifth run was 36.5 mol_(MDA) mol_(Ru)^(-1) h^(-1),which was comparable with that of the fresh one.The computational results showed that g-C_(3)N_(4) as support was favorable for adsorption and dissociation of H_(2) molecules.Moreover,the substrate scope can be successfully expanded to a variety of other aromatic diamines.Therefore,this work provides an efficient and green catalyst system for selective hydrogenation of aromatic diamines.
基金The work was supported by the National Natural Science Foundations of China(No.31971653).
文摘Tree mortality models play an important role in predicting tree growth and yield,but existing mortality models for Larix gmelinii subsp.principis-rupprechtii,an important species used for regeneration and afforestation in northern China,have overlooked potential regional influences on tree mortality.This study used data acquired from 102 temporary sample plots(TSPs)in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest(n=67)and state-owned Boqiang Forest(n=35)in northern China.To model stand-level tree mortality,we compared seven model forms of county data.Three continuous(dominant height,plot mean diameter,and basal area per hectare)and one dummy variable with two levels(region)were used as fixed effects variables.Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models.Results showed that tree mortality significantly positively correlated with stand basal area and dominant height,but negatively correlated with stand mean diameter.Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements,and Hurdle Poisson mixed-effects model showed the most attractive fit statistics(largest R^(2)and smallest RMSE)when employing leave-one-out cross-validation.These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.
基金partly supported by Showa Denko Co. Ltd, Grant-in-Aid for Scientific Research (Kaken S, 24226017)COI Stream Projectfinancial support from China Scholarship Council
文摘Transparent electrode based on silver nanowires(Ag NWs) emerges as an outstanding alternative of indium tin oxide film especially for flexible electronics. However, the conductivity of Ag NWs transparent electrode is still dramatically limited by the contact resistance between nanowires at high transmittance. Polyvinylpyrrolidone(PVP) layer adsorbed on the nanowire surface acts as an electrically insulating barrier at wire–wire junctions, and some devastating post-treatment methods are proposed to reduce or eliminate PVP layer, which usually limit the application of the substrates susceptible to heat or pressure and burden the fabrication with high-cost, time-consuming, or inefficient processes. In this work, a simple and rapid pre-treatment washing method was proposed to reduce the thickness of PVP layer from 13.19 to0.96 nm and improve the contact between wires. Ag NW electrodes with sheet resistances of 15.6 and 204 X sq-1have been achieved at transmittances of 90 and 97.5 %, respectively. This method avoided any post-treatments and popularized the application of high-performance Ag NW transparent electrode on more substrates. The improved Ag NWs were successfully employed in a capacitive pressure sensor with high transparency, sensitivity, and reproducibility.
基金National Natural Science Foundation of China(Nos.11774027,51132002,51977009 and 51372282).
文摘There is an urgent global need for wireless communication utilizing materials that can provide simultaneous flexibility and high conductivity.Avoiding the harmful effects of electromagnetic(EM)radiation from wireless communication is a persistent research hot spot.Two-dimensional(2D)materials are the preferred choice as wireless communication and EM attenuation materials as they are lightweight with high aspect ratios and possess distinguished electronic properties.MXenes,as a novel family of 2D materials,have shown excellent properties in various fields,owing to their excellent electrical conductivity,mechanical stability,high flexibility,and ease of processability.To date,research on the utility of MXenes for wireless communication has been actively pursued.Moreover,MXenes have become the leading materials for EM attenuation.Herein,we systematically review the recent advances in MXene-based materials with different structural designs for wireless communication,electromagnetic interference(EMI)shielding,and EM wave absorption.The relationship governing the structural design and the effectiveness for wireless communication,EMI shielding,and EM wave absorption is clearly revealed.Furthermore,our review mainly focuses on future challenges and guidelines for designing MXene-based materials for industrial application and foundational research.
基金This work was supported in part by the National Key R&D Program of China(Nos.2016YFC0104609 and 2019YFC0605203)The Fundamental Research Funds for the Central Universities(Nos.2019CDYGYB019 and 2020CDJ-LHZZ-075)。
文摘The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to patients.Low-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises quality and decreases diagnostic performance.Researchers have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques,which have achieved impressive results.However,most of these methods are directly performed on reconstructed LDCT images,in which some subtle structures and details are readily lost during the reconstruction procedure,and convolutional neural network(CNN)-based methods for raw LDCT projection data are rarely reported.To address this problem,we adopted an attention residual dense CNN,referred to as AttRDN,for LDCT sinogram denoising.First,it was aided by the attention mechanism,in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT sinograms.Then,the denoised sinogram was restored by subtracting the noise obtained from the input noisy sinogram.Finally,the CT image was reconstructed using filtered back-projection.The experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art methods.Importantly,it can prevent the loss of detailed information and has the potential for clinical application.
基金supported by National Natural Science Foundation of China (No.62101136)Shanghai Sailing Program (No.21YF1402800)+3 种基金Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01)ZJLab,Shanghai Municipal of Science and Technology Project (No.20JC1419500)Natural Science Foundation of Chongqing (No.CSTB2022NSCQ-MSX0360)Shanghai Center for Brain Science and Brain-inspired Technology.
文摘Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.
基金supported in part by the National Natural Science Foundation of China(No.61401049)the Chongqing Foundation and Frontier Research Project(Nos.cstc2016jcyjA0473,cstc2013jcyjA0763)+3 种基金the Graduate Scientific Research and Innovation Foundation of Chongqing,China(No.CYB16044)the Strategic Industry Key Generic Technology Innovation Project of Chongqing(No.cstc2015zdcy-ztzxX0002)China Scholarship Councilthe Fundamental Research Funds for the Central Universities Nos.CDJZR14125501,106112016CDJXY120003,10611CDJXZ238826
文摘Inspired by total variation(TV), this paper represents a new iterative algorithm based on diagonal total variation(DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reconstructed image, we used DTV to sparsely represent images when iterative convergence of the reconstructed algorithm with TV-constraint had no effect during the reconstruction process. To investigate our proposed algorithm, the numerical and experimental studies were performed, and rootmean-square error(RMSE) and structure similarity(SSIM)were used to evaluate the reconstructed image quality. The results demonstrated that the proposed method could effectively reduce noise, suppress artifacts, and reconstruct highquality image from incomplete projection data.
文摘In recent years,online reservation systems of country hotel have become increasingly popular in rural areas.How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved.Aiming at the problem of cold start and data sparseness in recommendation,a Hybrid Recommendation method based on Graph Embedding(HRGE)is proposed.First,three types of network are built,including user-user network based on user tag,househouse network based on house tag,and user-user network based on user behavior.Then,by using the method of graph embedding,three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes.Finally,these characterization vectors are used to make a hybrid recommendation.The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province.The experimental results show that,compared with traditional recommendation algorithms,the comprehensive evaluation index(F1)of the HRGE is improved by 20% and the Mean Average Precision(MAP)is increased by 11%.
