Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been...Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke.In recent years,the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation.This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer’s disease,Parkinson’s disease,multiple sclerosis,and Huntington’s disease.A comprehensive literature search was conducted using databases such as PubMed and Google Scholar,focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications.The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism,which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases.Although promising results have been observed in selected clinical and preclinical trials,further validations are needed to evaluate their clinical value.When combined with other imaging modalities and advanced analytical methods,chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker,enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases.展开更多
针对无线电能传输WPT(wireless power transmission)系统耦合机构发生偏移时,输出电压波动的问题,提出1种基于恒压输出区间追踪的WPT系统抗偏移方法。首先,建立CLC-S型WPT系统的模型,分析该系统在谐振和非谐振状态下的互感与输出电压增...针对无线电能传输WPT(wireless power transmission)系统耦合机构发生偏移时,输出电压波动的问题,提出1种基于恒压输出区间追踪的WPT系统抗偏移方法。首先,建立CLC-S型WPT系统的模型,分析该系统在谐振和非谐振状态下的互感与输出电压增益之间的关系,由分析可知,系统工作在非谐振状态下的恒压输出区间内抗偏移能力更强;然后,设计电感补偿序列,提出恒压输出区间追踪控制策略,实现WPT系统输出电压恒定控制,提高系统的抗偏移能力;最后,搭建仿真模型和实验平台,仿真及实验结果均表明,采用恒压输出区间追踪控制策略,可以有效减小输出电压的波动,验证了系统在强互感干扰下的鲁棒性。相较于无恒压输出区间追踪的WPT系统,所提系统具有更好的输出电压动态调节能力。展开更多
传统双向E型无线电能传输(wireless power transfer,WPT)拓扑易进入硬开关状态,导致电能传输效率低。针对此,该文提出无线电能传输系统的改进E^(#)型拓扑及其移相控制策略。首先,构建软开关状态负载范围更宽的双向E^(#)型WPT电路拓扑数...传统双向E型无线电能传输(wireless power transfer,WPT)拓扑易进入硬开关状态,导致电能传输效率低。针对此,该文提出无线电能传输系统的改进E^(#)型拓扑及其移相控制策略。首先,构建软开关状态负载范围更宽的双向E^(#)型WPT电路拓扑数学模型,分析并提取电路实现软开关工作状态的关键变量与约束条件,理论上证明所提拓扑的有效性。然后,推导电路中线圈互感和负载阻抗等参数的解析关系式,并基于此提出可保证系统在负载时始终处于最佳工作状态的移相控制策略。该策略通过控制开关管的门极驱动信号相位,使谐振元件内部储存的能量提前或者滞后释放,从而将开关管修正回软开关状态。最后,通过仿真和实验验证所提双向E^(#)型WPT系统的有效性。实验结果表明,所提方法可保证在5~30Ω的负载范围内电路工作在软开关状态,该范围内的电能传输效率峰值达84.3%。展开更多
针对单相矩阵式无线电能传输MC-WPT(matrix converter based wireless power transfer)系统网侧电流谐波含量大的问题,提出1种谐波抑制调制策略,可有效降低网侧电流低次谐波含量及总谐波失真度THD(total harmonic distortion)。分析谐...针对单相矩阵式无线电能传输MC-WPT(matrix converter based wireless power transfer)系统网侧电流谐波含量大的问题,提出1种谐波抑制调制策略,可有效降低网侧电流低次谐波含量及总谐波失真度THD(total harmonic distortion)。分析谐振槽电压电流特性,基于参数归一化方法得到2个基波分量的等效电路,进而推导出MC-WPT的数学模型。在此基础上,以消除低次谐波含量为目标,应用计算法得到接收侧H桥的优化调制波,使网侧电流低频成分仅有工频分量,从而降低网侧电流THD。最后搭建实验平台,验证所提谐波抑制调制策略的可行性与有效性。展开更多
针对立体车库中电动汽车无线充电问题,提出一种基于电场-磁场混合式无线电能传输系统的全双工电能与信号并行传输技术,以提升系统的传输功率和效率。以磁场耦合机构作为电能传输通道,基于LCC-S补偿网络设计电能传输参数,实现恒压输出;...针对立体车库中电动汽车无线充电问题,提出一种基于电场-磁场混合式无线电能传输系统的全双工电能与信号并行传输技术,以提升系统的传输功率和效率。以磁场耦合机构作为电能传输通道,基于LCC-S补偿网络设计电能传输参数,实现恒压输出;以电场耦合金属电极和磁场耦合线圈作为信号传输通道,上下金属载车板和车载金属电极构成四电极层叠式电场耦合机构。在电能传输、信号传输、电能串扰和信号串扰等不同模式下,对交叉耦合电容与耦合线圈电感等参数关系进行分析,对全双工通信过程中的阻波网络参数进行设定,对电能与信号之间的串扰关系进行分析。仿真结果表明,运用此并行传输技术,输出功率可达3 300 W、信号最大传输速率可达200 k B∕s。展开更多
Mitochondria play a crucial role as organelles,managing several physiological processes such as redox balance,cell metabolism,and energy synthesis.Initially,the assumption was that mitochondria primarily resided in th...Mitochondria play a crucial role as organelles,managing several physiological processes such as redox balance,cell metabolism,and energy synthesis.Initially,the assumption was that mitochondria primarily resided in the host cells and could exclusively transmit from oocytes to offspring by a mechanism known as vertical inheritance of mitochondria.Recent scholarly works,however,suggest that certain cell types transmit their mitochondria to other developmental cell types via a mechanism referred to as intercellular or horizontal mitochondrial transfer.This review details the process of which mitochondria are transferred across cells and explains the impact of mitochondrial transfer between cells on the efficacy and functionality of cancer cells in various cancer forms.Specifically,we review the role of mitochondria transfer in regulating cellular metabolism restoration,excess reactive oxygen species(ROS)generation,proliferation,invasion,metastasis,mitophagy activation,mitochondrial DNA(mtDNA)inheritance,immune system modulation and therapeutic resistance in cancer.Additionally,we highlight the possibility of using intercellular mitochondria transfer as a therapeutic approach to treat cancer and enhance the efficacy of cancer treatments.展开更多
Organic nanophotocatalysts are promising candidates for solar fuels production,but they still face the challenge of unfavorable geminate recombination due to the limited exciton diffusion lengths.Here,we introduce a b...Organic nanophotocatalysts are promising candidates for solar fuels production,but they still face the challenge of unfavorable geminate recombination due to the limited exciton diffusion lengths.Here,we introduce a binary nanophotocatalyst fabricated by blending two polymers,PS-PEG5(PS)and PBT-PEG5(PBT),with matched absorption and emission spectra,enabling a Forster resonance energy transfer(FRET)process for enhanced photocatalysis.These heterostructure nanophotocatalysts are processed using a facile and scalable flash nanoprecipitation(FNP)technique with precious kinetic control over binary nanoparticle formation.The resulting nanoparticles exhibit an exceptional photocatalytic hydrogen evolution rate up to 65 mmol g^(-1) h^(-1),2.5 times higher than that single component nanoparticles.Characterizations through fluorescence spectra and transient absorption spectra confirm the hetero-energy transfer within the binary nanoparticles,which prolongs the excited-state lifetime and extends the namely“effective exciton diffusion length”.Our finding opens new avenues for designing efficient organic photocatalysts by improving exciton migration.展开更多
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.展开更多
Catalytic oxidation of organic pollutants is a well-known and effective technique for pollutant abatement.Unfortunately,this method is significantly hindered in practical applications by the lowefficiency and difficul...Catalytic oxidation of organic pollutants is a well-known and effective technique for pollutant abatement.Unfortunately,this method is significantly hindered in practical applications by the lowefficiency and difficult recovery of the catalysts in a powdery form.Herein,a three-dimensional(3D)framework of Fe-incorporated Ni_(3)S_(2)nanosheets in-situ grown on Ni foam(Fe-Ni_(3)S_(2)@NF)was fabricated by a facile two-step hydrothermal process and applied to trigger peroxymonosulfate(PMS)oxidation of organic compounds inwater.A homogeneous growth environment enabled the uniform and scalable growth of Fe-Ni_(3)S_(2)nanosheets on the Ni foam.Fe-Ni_(3)S_(2)@NF possessed outstanding activity and durability in activating PMS,as it effectively facilitated electron transfer from organic pollutants to PMS.Fe-Ni_(3)S_(2)@NF initially supplied electrons to PMS,causing the catalyst to undergo oxidation,and subsequently accepted electrons from organic compounds,returning to its initial state.The introduction of Fe into the Ni_(3)S_(2)lattice enhanced electrical conductivity,promoting mediated electron transfer between PMS and organic compounds.The 3D conductive Ni foam provided an ideal platform for the nucleation and growth of Fe-Ni_(3)S_(2),accelerating pollutant abatement due to its porous structure and high conductivity.Furthermore,its monolithic nature simplified the catalyst recycling process.A continuous flow packed-bed reactor by encapsulating Fe-Ni_(3)S_(2)@NF catalyst achieved complete pollutant abatement with continuous operation for 240 h,highlighting its immense potential for practical environmental remediation.This study presents a facile synthesis method for creating a novel type of monolithic catalyst with high activity and durability for decontamination through Fenton-like processes.展开更多
Layered double hydroxide(LDH)based heterogonous peroxymonosulfate(PMS)activation degradation of pollutants has attracted extensive attention.The challenge is to selectively regulate the traditional free radical domina...Layered double hydroxide(LDH)based heterogonous peroxymonosulfate(PMS)activation degradation of pollutants has attracted extensive attention.The challenge is to selectively regulate the traditional free radical dominant degradation pathway into a nonradical degradation pathway.Herein,an interface ar-chitecture of Ti_(3) C_(2) T_(x)-MXene(MXene)loading on the Fe-Al LDH scaffold was developed,which showed excellent stability and robust resistance against harsh conditions.Significantly,the rate constant for tetra-cycline hydrochloride(TC)degradation in the MXene-LDH/PMS process was 0.421 min^(-1),which was ten times faster than the rate constant for pure Fe-Al LDH(0.042 min^(-1)).Specifically,more reactive Fe with the closer d-band center to the Fermi level results in higher electron transfer efficiency.The occupa-tions of Fe-3d orbitals in Mxene/Fe-Al LDH are pushed above the Fermi level to generate,which results in higher PMS adsorption and inhibition of the release of oxygen-containing active species intermedi-ates,leading to the enhanced^(1)O_(2) generation.Additionally,the built-in electric field in the heterojunc-tion was driven by the charge redistribution between MXene and Fe-Al LDH,resulting in a mediated-electron transfer mechanism,differentiating it from the Fe-Al LDH/PMS system.It was fascinating that MXene/Fe-Al LDH achieved satisfactory treatment efficiency in continuous column reactor and real landfill leachate.展开更多
The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To addres...The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.展开更多
基金supported by The University of Hong Kong,China(109000487,109001694,204610401,and 204610519)National Natural Science Foundation of China(82402225)(to JH).
文摘Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke.In recent years,the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation.This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer’s disease,Parkinson’s disease,multiple sclerosis,and Huntington’s disease.A comprehensive literature search was conducted using databases such as PubMed and Google Scholar,focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications.The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism,which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases.Although promising results have been observed in selected clinical and preclinical trials,further validations are needed to evaluate their clinical value.When combined with other imaging modalities and advanced analytical methods,chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker,enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases.
文摘针对无线电能传输WPT(wireless power transmission)系统耦合机构发生偏移时,输出电压波动的问题,提出1种基于恒压输出区间追踪的WPT系统抗偏移方法。首先,建立CLC-S型WPT系统的模型,分析该系统在谐振和非谐振状态下的互感与输出电压增益之间的关系,由分析可知,系统工作在非谐振状态下的恒压输出区间内抗偏移能力更强;然后,设计电感补偿序列,提出恒压输出区间追踪控制策略,实现WPT系统输出电压恒定控制,提高系统的抗偏移能力;最后,搭建仿真模型和实验平台,仿真及实验结果均表明,采用恒压输出区间追踪控制策略,可以有效减小输出电压的波动,验证了系统在强互感干扰下的鲁棒性。相较于无恒压输出区间追踪的WPT系统,所提系统具有更好的输出电压动态调节能力。
文摘传统双向E型无线电能传输(wireless power transfer,WPT)拓扑易进入硬开关状态,导致电能传输效率低。针对此,该文提出无线电能传输系统的改进E^(#)型拓扑及其移相控制策略。首先,构建软开关状态负载范围更宽的双向E^(#)型WPT电路拓扑数学模型,分析并提取电路实现软开关工作状态的关键变量与约束条件,理论上证明所提拓扑的有效性。然后,推导电路中线圈互感和负载阻抗等参数的解析关系式,并基于此提出可保证系统在负载时始终处于最佳工作状态的移相控制策略。该策略通过控制开关管的门极驱动信号相位,使谐振元件内部储存的能量提前或者滞后释放,从而将开关管修正回软开关状态。最后,通过仿真和实验验证所提双向E^(#)型WPT系统的有效性。实验结果表明,所提方法可保证在5~30Ω的负载范围内电路工作在软开关状态,该范围内的电能传输效率峰值达84.3%。
文摘针对单相矩阵式无线电能传输MC-WPT(matrix converter based wireless power transfer)系统网侧电流谐波含量大的问题,提出1种谐波抑制调制策略,可有效降低网侧电流低次谐波含量及总谐波失真度THD(total harmonic distortion)。分析谐振槽电压电流特性,基于参数归一化方法得到2个基波分量的等效电路,进而推导出MC-WPT的数学模型。在此基础上,以消除低次谐波含量为目标,应用计算法得到接收侧H桥的优化调制波,使网侧电流低频成分仅有工频分量,从而降低网侧电流THD。最后搭建实验平台,验证所提谐波抑制调制策略的可行性与有效性。
文摘针对立体车库中电动汽车无线充电问题,提出一种基于电场-磁场混合式无线电能传输系统的全双工电能与信号并行传输技术,以提升系统的传输功率和效率。以磁场耦合机构作为电能传输通道,基于LCC-S补偿网络设计电能传输参数,实现恒压输出;以电场耦合金属电极和磁场耦合线圈作为信号传输通道,上下金属载车板和车载金属电极构成四电极层叠式电场耦合机构。在电能传输、信号传输、电能串扰和信号串扰等不同模式下,对交叉耦合电容与耦合线圈电感等参数关系进行分析,对全双工通信过程中的阻波网络参数进行设定,对电能与信号之间的串扰关系进行分析。仿真结果表明,运用此并行传输技术,输出功率可达3 300 W、信号最大传输速率可达200 k B∕s。
基金supported by the National Natural Science Foundation of China(Grant No.:82272749)the Natural Science Foundation of Liaoning Province,China(Grant No.:2022-MS-190).
文摘Mitochondria play a crucial role as organelles,managing several physiological processes such as redox balance,cell metabolism,and energy synthesis.Initially,the assumption was that mitochondria primarily resided in the host cells and could exclusively transmit from oocytes to offspring by a mechanism known as vertical inheritance of mitochondria.Recent scholarly works,however,suggest that certain cell types transmit their mitochondria to other developmental cell types via a mechanism referred to as intercellular or horizontal mitochondrial transfer.This review details the process of which mitochondria are transferred across cells and explains the impact of mitochondrial transfer between cells on the efficacy and functionality of cancer cells in various cancer forms.Specifically,we review the role of mitochondria transfer in regulating cellular metabolism restoration,excess reactive oxygen species(ROS)generation,proliferation,invasion,metastasis,mitophagy activation,mitochondrial DNA(mtDNA)inheritance,immune system modulation and therapeutic resistance in cancer.Additionally,we highlight the possibility of using intercellular mitochondria transfer as a therapeutic approach to treat cancer and enhance the efficacy of cancer treatments.
基金supported by National Natural Science Foundation of China(NSFC,22338006,92356301,9235630033 and 22375062)Shanghai Municipal Science and Technology Major Project(21JC1401700)+4 种基金Shanghai Pilot Program for Basic Research(22TQ1400100-10)Fundamental Research Funds for the Central UniversitiesShanghai Pujiang Program(22PJ1402400)“Chenguang Program”supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission(22CGA32)the Young Elite Scientists Sponsorship Program by CAST(2023QNRC001).
文摘Organic nanophotocatalysts are promising candidates for solar fuels production,but they still face the challenge of unfavorable geminate recombination due to the limited exciton diffusion lengths.Here,we introduce a binary nanophotocatalyst fabricated by blending two polymers,PS-PEG5(PS)and PBT-PEG5(PBT),with matched absorption and emission spectra,enabling a Forster resonance energy transfer(FRET)process for enhanced photocatalysis.These heterostructure nanophotocatalysts are processed using a facile and scalable flash nanoprecipitation(FNP)technique with precious kinetic control over binary nanoparticle formation.The resulting nanoparticles exhibit an exceptional photocatalytic hydrogen evolution rate up to 65 mmol g^(-1) h^(-1),2.5 times higher than that single component nanoparticles.Characterizations through fluorescence spectra and transient absorption spectra confirm the hetero-energy transfer within the binary nanoparticles,which prolongs the excited-state lifetime and extends the namely“effective exciton diffusion length”.Our finding opens new avenues for designing efficient organic photocatalysts by improving exciton migration.
基金supported by the National Defense Fundamental Research Project(No.JCKY2022404C005)the Nuclear Energy Development Project(No.23ZG6106)+1 种基金the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026)the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。
文摘In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
基金supported by the National Natural Science Foundation of China(No.21876039)Y.Yao acknowledges the scholarship support from the China Scholarship Council(No.202106695010)Partial support from the Australian Research Council for DP230102406 is also acknowledged.
文摘Catalytic oxidation of organic pollutants is a well-known and effective technique for pollutant abatement.Unfortunately,this method is significantly hindered in practical applications by the lowefficiency and difficult recovery of the catalysts in a powdery form.Herein,a three-dimensional(3D)framework of Fe-incorporated Ni_(3)S_(2)nanosheets in-situ grown on Ni foam(Fe-Ni_(3)S_(2)@NF)was fabricated by a facile two-step hydrothermal process and applied to trigger peroxymonosulfate(PMS)oxidation of organic compounds inwater.A homogeneous growth environment enabled the uniform and scalable growth of Fe-Ni_(3)S_(2)nanosheets on the Ni foam.Fe-Ni_(3)S_(2)@NF possessed outstanding activity and durability in activating PMS,as it effectively facilitated electron transfer from organic pollutants to PMS.Fe-Ni_(3)S_(2)@NF initially supplied electrons to PMS,causing the catalyst to undergo oxidation,and subsequently accepted electrons from organic compounds,returning to its initial state.The introduction of Fe into the Ni_(3)S_(2)lattice enhanced electrical conductivity,promoting mediated electron transfer between PMS and organic compounds.The 3D conductive Ni foam provided an ideal platform for the nucleation and growth of Fe-Ni_(3)S_(2),accelerating pollutant abatement due to its porous structure and high conductivity.Furthermore,its monolithic nature simplified the catalyst recycling process.A continuous flow packed-bed reactor by encapsulating Fe-Ni_(3)S_(2)@NF catalyst achieved complete pollutant abatement with continuous operation for 240 h,highlighting its immense potential for practical environmental remediation.This study presents a facile synthesis method for creating a novel type of monolithic catalyst with high activity and durability for decontamination through Fenton-like processes.
基金financially supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK1003)the Science and Technology Innovation Pro-gram of Hunan Province(No.2022RC1122)。
文摘Layered double hydroxide(LDH)based heterogonous peroxymonosulfate(PMS)activation degradation of pollutants has attracted extensive attention.The challenge is to selectively regulate the traditional free radical dominant degradation pathway into a nonradical degradation pathway.Herein,an interface ar-chitecture of Ti_(3) C_(2) T_(x)-MXene(MXene)loading on the Fe-Al LDH scaffold was developed,which showed excellent stability and robust resistance against harsh conditions.Significantly,the rate constant for tetra-cycline hydrochloride(TC)degradation in the MXene-LDH/PMS process was 0.421 min^(-1),which was ten times faster than the rate constant for pure Fe-Al LDH(0.042 min^(-1)).Specifically,more reactive Fe with the closer d-band center to the Fermi level results in higher electron transfer efficiency.The occupa-tions of Fe-3d orbitals in Mxene/Fe-Al LDH are pushed above the Fermi level to generate,which results in higher PMS adsorption and inhibition of the release of oxygen-containing active species intermedi-ates,leading to the enhanced^(1)O_(2) generation.Additionally,the built-in electric field in the heterojunc-tion was driven by the charge redistribution between MXene and Fe-Al LDH,resulting in a mediated-electron transfer mechanism,differentiating it from the Fe-Al LDH/PMS system.It was fascinating that MXene/Fe-Al LDH achieved satisfactory treatment efficiency in continuous column reactor and real landfill leachate.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.42307218 and U2239251).
文摘The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.
