As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term...As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term uranium supply but is challenged by ultra-low uranium concentrations and ion interference.To overcome these issues,we design three diketopyrrolopyrrole-based covalent organic frameworks(COFs)via a synergisticπ-extended lock and carboxyl-functionalized anchor molecular engineering strategy.Among them,TPy-DPP-COF features a covalently lockedπ-conjugated structure that enhances planarity,optimizes energy alignment,and minimizes exciton binding energy,thereby promoting charge transfer and suppressing recombination.Concurrently,carboxyl groups enable uranyl-specific coordination and create local electric fields to facilitate charge separation.These features contribute to the outstanding performance of TPy-DPP-COF,which achieves a high uranium adsorption capacity of 16.33 mg g−1 in natural seawater under irradiation,with only 29.3%capacity loss after 10 cycles,surpassing industrial benchmarks.Density functional theory(DFT)calculations and experimental studies reveal a synergistic photocatalysis-adsorption pathway,with DPP units acting as active sites for uranium reduction.This work highlights a molecular design strategy for developing efficient COF-based photocatalysts for practical marine uranium recovery.展开更多
Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (...Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.展开更多
In this work, a metal-organic framework derived nanoporous carbon （MOF-5-C） was fabricated and modified with Fe3O4 magnetic nanoparticles. The resulting magnetic MOF-5-derived porous carbon （Fe304@MOF-5-C） was the...In this work, a metal-organic framework derived nanoporous carbon （MOF-5-C） was fabricated and modified with Fe3O4 magnetic nanoparticles. The resulting magnetic MOF-5-derived porous carbon （Fe304@MOF-5-C） was then used for the magnetic solid-phase extraction of chlorophenols （CPs） from mushroom samples prior to high performance liquid chromatography-ultraviolet detection. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and N2 adsorption were used to characterize the adsorbent. After experimental optimization, the amount of the adsorbent was chosen as 8.0 mg, extraction time as 10 min, sample volume as 50 mL, desorption solvent as 0.4 mL （0.2 mL × 2） of alkaline methanol, and sample pH as 6. Under the above optimized conditions, good linearity for the analytes was obtained in the range of 0.8-100.0 ng g 1 with the correlation coefficients between 0.9923 and 0.9963. The limits of detection （SIN= 3） were in the range of 0.25-0.30 ng g-1, and the relative standard deviations were below 6.8%. The result showed that the Fe304@MOF-5-C has an excellent adsorption capacity for the analytes.展开更多
Compared with the traditional liquid–liquid extraction method,solid-phase extraction agents are of great significance for the recovery of indium metal due to their convenience,free of organic solvents,and fully expos...Compared with the traditional liquid–liquid extraction method,solid-phase extraction agents are of great significance for the recovery of indium metal due to their convenience,free of organic solvents,and fully exposed activity.In this study,P_(2)O_(4)(di-2-ethylhexyl phosphoric acid)was chemically modified by using UiO-66 to form the solid-phase extraction agent P_(2)O_(4)-UiO-66-MOFs(di-2-ethylhexyl phosphoric acid-UiO-66-metal-organic frameworks)to adsorb In(Ⅲ).The results show that the Zr of UiO-66 bonds with the P-OH of P_(2)O_(4) to form a composite P_(2)O_(4)-UiO-66-MOF,which was confirmed by X-ray photoelectron spectroscopy(XPS)and Fourier transform infrared spectroscopy(FT-IR).The adsorption process of indium on P_(2)O_(4)-UiO-66-MOFs followed pseudo first-order kinetics,and the adsorption isotherms fit the Langmuir adsorption isotherm model.The adsorption capabilities can reach 192.8 mg/g.After five consecutive cycles of adsorption-desorption-regeneration,the indium adsorption capacity by P_(2)O_(4)-UiO-66-MOFs remained above 99%.The adsorption mechanism analysis showed that the P=O and P-OH of P_(2)O_(4) molecules coated on the surface of P_(2)O_(4)-UiO-66-MOFs participated in the adsorption reaction of indium.In this paper,the extractant P_(2)O_(4) was modified into solid P_(2)O_(4)-UiO-66-MOFs for the first time.This work provides a new idea for the development of solid-phase extractants for the recovery of indium.展开更多
Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on ...Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data.展开更多
The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods ha...The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical applications.In this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational efficiency.With the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’efficiency.Furthermore,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation capability.In the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image.Extensive experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.展开更多
In this work,the nano-g-C_(3)N_(4)/Ui O-66-NH_(2)composite was prepared by one-step solvothermal method.The as-prepared composite was characterized by scanning electron microscopy,Brunner-Emmet-Teller measurement,ener...In this work,the nano-g-C_(3)N_(4)/Ui O-66-NH_(2)composite was prepared by one-step solvothermal method.The as-prepared composite was characterized by scanning electron microscopy,Brunner-Emmet-Teller measurement,energy dispersive spectrometer,X-ray diffraction,and Fourier transform infrared spectroscopy.By using nano-g-C_(3)N_(4)/Ui O-66-NH_(2)composite as sorbent,a dispersive solid-phase extraction coupled with high-performance liquid chromatography was developed to sensitive analysis of food colorants including tartrazine,amaranth,carmine,sunset yellow,allura red and bright blue.The experiment parameters including the amount of sorbent,adsorption time,the p H of adsorption solution,desorption time,desorption solvent,the p H of desorption solution as well as the proportion between desorption solvent and buffer solvent were investigated.Under the optimized conditions,the limits of detection(S/N=3) and limits of quantitation (S/N=10) were determined in the ranges of 0.08-0.8 and 0.2-2.0 ng/m L,respectively.With the developed sample pretreatment method,carmine and brilliant blue were determined from blueberry juice by HPLC-DAD.The contents were calculated as 1.53μg/m L and0.17μg/mL,respectively.展开更多
In this study,a functionalized covalent-organic framework(COF)was first synthesized using porphyrin as the fabrication unit and showed an edge-curled,petal-like and well-ordered structure.The synthesized COF was then ...In this study,a functionalized covalent-organic framework(COF)was first synthesized using porphyrin as the fabrication unit and showed an edge-curled,petal-like and well-ordered structure.The synthesized COF was then introduced to prepare porous organic polymer monolithic materials(POPMs).Two composite POPM/COF monolithic materials with rod shapes,referred to as sorbent A and sorbent B,were prepared in stainless steel tubes using different monomers.Sorbents A and B exhibited relatively uniform porous structures and enhanced specific surface areas of 153.14 m;/g and 80.01 m;/g,respectively.The prepared composite monoliths were used as in-tube solid-phase extraction(SPE)sorbents combined with HPLC for the on-line extraction and quantitative analytical systems.Indole alkaloids(from Catharanthus roseus G.Don and Uncaria rhynchophylla(Miq.)Miq.Ex Havil.)contained in mouse plasma were extracted and quantitatively analyzed using the online system.The two composite multifunctional monoliths showed excellent clean-up ability for complex biological matrices,as well as superior selectivity for target indole alkaloids.Method validation showed that the RSD values of the repeatability(n=6)were≤3.46%,and the accuracy expressed by the spiked recoveries was in the ranges of 99.38%-100.91%and 96.39%-103.50%for vinca alkaloids and Uncaria alkaloids,respectively.Furthermore,sorbents A and B exhibited strong reusability,with RSD values≤5.32%,which were based on the peak area of the corresponding alkaloids with more than 100 injections.These results indicate that the composite POPM/COF rod-shaped monoliths are promising media as SPE sorbents for extracting trace compounds in complex biological samples.展开更多
Metal-organic frameworks(MOFs)received considerable attention to adsorption and removal of various environmental pollutants because of some inherent advantages.However,it is challenging but meaningful to design and fa...Metal-organic frameworks(MOFs)received considerable attention to adsorption and removal of various environmental pollutants because of some inherent advantages.However,it is challenging but meaningful to design and fabricate hierarchical mixed-dimensional MOFs with synergistic effects to enhance the performance for removal and preconcentration of environmental pollutants.Herein,a new hierarchical two-dimensional(2D)-three-dimensional(3D)mixed-dimensional cactus-like MOF@MOF hybrid material(PCN-134@Zr-BTB)was prepared by in-situ growth of 2D MOF nanosheets(Zr-BTB)on the surface of 3D MOF(PCN-134).The PCN-134@Zr-BTB composites combine the advantages of 2D and 3D MOFs with extensive mesoporous structures and large surface area for effective removal and enrichment of bisphenols(BPs).In comparison with pristine PCN-134 and Zr-BTB materials,the PCN-134@Zr-BTB hybrid material presented excellent adsorption performance for BPs.The adsorption isotherms are consistent with the Langmuir model,and the maximum adsorption capacity of four bisphenols(BPs)ranged from 135.1 mg/g to 628.9 mg/g.The adsorption kinetics are in accordance with the pseudo-second-order model.The recoveries ranged from 72.8%to 108%.The limits of detection were calculated at 0.02-0.03 ng/mL.The enrichment factors were calculated in the range of 310-374.According to FT-IR and XPS analysis,the main adsorption mechanisms are hydrogen bonding and π-π stacking.Nevertheless,this work provides a new and convenient strategy for the preparation of new hierarchical mixed-dimensional MOF@MOF(PCN-134@Zr-BTB)hybrid material for extraction and enrichment of BPs from aqueous matrix.展开更多
Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
Residues of tetracycline antibiotics(TCs) in environments may be harmful to human.Due to their high polarities,it is extremely challenging to efficiently enrich TCs with low concentrations in natural waters for analys...Residues of tetracycline antibiotics(TCs) in environments may be harmful to human.Due to their high polarities,it is extremely challenging to efficiently enrich TCs with low concentrations in natural waters for analysis.In this work,a magnetic metal-organic framework Fe_(3)O_(4)@[Cu_(3)(btc)_(2)]was synthesized and applied as a dispersive micro-solid phase extraction adsorbent for TCs enrichment.Effects of dispersive micro-solid phase extraction conditions including extraction time,solution p H,and elution solvent on the extraction efficiencies of TCs were investigated.Results show that TCs could be enriched efficiently by Fe_(3)O_(4)@[Cu_(3)(btc)_(2)],and electrostatic interaction between TCs and Fe_(3)O_(4)@[Cu_(3)(btc)_(2)]dominated this process.Combined with liquid chromatography-tandem mass spectrometry,four TCs residues (oxytetracycline,tetracycline,chlortetracycline,and doxycycline) in natural waters were determined.The detection limits (LOD,S/N=3) of the four antibiotics were 0.01-0.02μg/L,and the limits of quantitation (LOQ,S/N=10)were 0.04-0.07μg/L.The recoveries obtained from river water and aquaculture water spiked with three TCs concentration levels ranged from 70.3%to 96.5%with relative standard deviations of 3.8%-12.8%.Results indicate that the magnetic metal-organic framework based dispersive micro-solid phase extraction is simple,rapid and high-loading for antibiotics enrichment from water,which further expand the practical application of metal-organic frameworks in sample pretreatment for environmental pollutant analysis.展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
Organophosphorus pesticides(OPPs)in foods pose a serious threat to human health,motivating the development of novel analytical methods for their rapid detection and quantification.A magnetic covalent organic framework...Organophosphorus pesticides(OPPs)in foods pose a serious threat to human health,motivating the development of novel analytical methods for their rapid detection and quantification.A magnetic covalent organic framework(M-COF)adsorbent for the magnetic solid-phase extraction(MSPE)of OPPs from foods was reported.M-COF was synthesized by the Schiff base condensation reaction of 1,3,5-tris(4-aminophenyl)benzene and 4,4-biphenyldicarboxaldehyde on the surface of amino-functionalized magnetic nanoparticles.Density functional theory(DFT)calculations showed that adsorption of OPPs onto the surface of M-COF involved hydrophobic effects,van der Waals interactions,π-πinteractions,halogen-N bonding,and hydrogen bonding.Combined with gas chromatography-mass spectrometry(GC-MS)technology,the MSPE method features low limits of detection for OPPs(0.002-0.015μg/L),good reproducibility(1.45%-6.14%),wide linear detection range(0.01-1μg/L,R≥0.9935),and satisfactory recoveries(87.3%-110.4%).The method was successfully applied for the trace analysis of OPPs in spiked fruit juices.展开更多
The development of novel metal-organic frameworks(MOFs)as solid adsorbents for rapid and efficient extraction of uranium from natural seawater is a long-term pursuit,yet remains challenging.In this work,we have prepar...The development of novel metal-organic frameworks(MOFs)as solid adsorbents for rapid and efficient extraction of uranium from natural seawater is a long-term pursuit,yet remains challenging.In this work,we have prepared four two-dimensional(2D)vinylene-linked cyclic trinuclear units(CTUs)based MOFs.The metal nodes in the skeleton can be regulated,resulting in two Ag-CTU and two Cu-CTU-based MOFs with similar 2D hexagonal structures.These MOFs exhibit not only good stability in acid/base,γ-ray irradiation and natural seawater but also feature excellent anti-biofouling properties against five marine bacteria with inhibition rates as high as~99%.Interestingly,the alteration of Ag(I)to Cu(I)remarkably enhances the uranium sorption capacity due to the reduction of soluble U(VI)to insoluble U(IV)triggered by the chemical and photo-redox reaction of Cu-CTU.Due to the multiple functions,the Cu-CTU-based MOF delivers the best overall performance for uranium extraction from natural seawater with a high adsorption capacity of 7.96 mg g^(-1)and adsorption rate of~1.00 mg g^(-1)day^(-1),which is much higher than most of reported representative solid adsorbents.Our work paves a new way for rationally designing synergistic MOFs with superior performance for extracting uranium from natural seawater.展开更多
基金the Young Elite Scientists Sponsorship Program by JXAST(2024QT11)the National Natural Science Foundation of China(22465001,22309003)the Jiangxi Provincial Natural Science Foundation(20232BAB203042,20242BAB22002).
文摘As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term uranium supply but is challenged by ultra-low uranium concentrations and ion interference.To overcome these issues,we design three diketopyrrolopyrrole-based covalent organic frameworks(COFs)via a synergisticπ-extended lock and carboxyl-functionalized anchor molecular engineering strategy.Among them,TPy-DPP-COF features a covalently lockedπ-conjugated structure that enhances planarity,optimizes energy alignment,and minimizes exciton binding energy,thereby promoting charge transfer and suppressing recombination.Concurrently,carboxyl groups enable uranyl-specific coordination and create local electric fields to facilitate charge separation.These features contribute to the outstanding performance of TPy-DPP-COF,which achieves a high uranium adsorption capacity of 16.33 mg g−1 in natural seawater under irradiation,with only 29.3%capacity loss after 10 cycles,surpassing industrial benchmarks.Density functional theory(DFT)calculations and experimental studies reveal a synergistic photocatalysis-adsorption pathway,with DPP units acting as active sites for uranium reduction.This work highlights a molecular design strategy for developing efficient COF-based photocatalysts for practical marine uranium recovery.
文摘Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.
基金Financial support from the National Natural Science Foundation of China (Nos. 31471643, 31571925)the Innovation Research Program of the Department of Education of Hebei for Hebei Provincial Universities (No. LJRC009)
文摘In this work, a metal-organic framework derived nanoporous carbon （MOF-5-C） was fabricated and modified with Fe3O4 magnetic nanoparticles. The resulting magnetic MOF-5-derived porous carbon （Fe304@MOF-5-C） was then used for the magnetic solid-phase extraction of chlorophenols （CPs） from mushroom samples prior to high performance liquid chromatography-ultraviolet detection. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and N2 adsorption were used to characterize the adsorbent. After experimental optimization, the amount of the adsorbent was chosen as 8.0 mg, extraction time as 10 min, sample volume as 50 mL, desorption solvent as 0.4 mL （0.2 mL × 2） of alkaline methanol, and sample pH as 6. Under the above optimized conditions, good linearity for the analytes was obtained in the range of 0.8-100.0 ng g 1 with the correlation coefficients between 0.9923 and 0.9963. The limits of detection （SIN= 3） were in the range of 0.25-0.30 ng g-1, and the relative standard deviations were below 6.8%. The result showed that the Fe304@MOF-5-C has an excellent adsorption capacity for the analytes.
基金supported by the Strategic Priority Research Program(A)of the Chinese Academy of Sciences(No.XDA23030302)the Key Programs of the Chinese Academy of Sciences(No.KFZD-SW-315)the Start-Up Foundation from Huaqiao University(No.20BS109).
文摘Compared with the traditional liquid–liquid extraction method,solid-phase extraction agents are of great significance for the recovery of indium metal due to their convenience,free of organic solvents,and fully exposed activity.In this study,P_(2)O_(4)(di-2-ethylhexyl phosphoric acid)was chemically modified by using UiO-66 to form the solid-phase extraction agent P_(2)O_(4)-UiO-66-MOFs(di-2-ethylhexyl phosphoric acid-UiO-66-metal-organic frameworks)to adsorb In(Ⅲ).The results show that the Zr of UiO-66 bonds with the P-OH of P_(2)O_(4) to form a composite P_(2)O_(4)-UiO-66-MOF,which was confirmed by X-ray photoelectron spectroscopy(XPS)and Fourier transform infrared spectroscopy(FT-IR).The adsorption process of indium on P_(2)O_(4)-UiO-66-MOFs followed pseudo first-order kinetics,and the adsorption isotherms fit the Langmuir adsorption isotherm model.The adsorption capabilities can reach 192.8 mg/g.After five consecutive cycles of adsorption-desorption-regeneration,the indium adsorption capacity by P_(2)O_(4)-UiO-66-MOFs remained above 99%.The adsorption mechanism analysis showed that the P=O and P-OH of P_(2)O_(4) molecules coated on the surface of P_(2)O_(4)-UiO-66-MOFs participated in the adsorption reaction of indium.In this paper,the extractant P_(2)O_(4) was modified into solid P_(2)O_(4)-UiO-66-MOFs for the first time.This work provides a new idea for the development of solid-phase extractants for the recovery of indium.
基金The National Natural Science Foundation of China(No.51675098)
文摘Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data.
基金the National Natural Science Foundation of China(61772149,61866009,61762028,U1701267,61702169)Guangxi Science and Technology Project(2019GXNSFFA245014,ZY20198016,AD18281079,AD18216004)+1 种基金the Natural Science Foundation of Hunan Province(2020JJ3014)Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics(GIIP202001).
文摘The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical applications.In this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational efficiency.With the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’efficiency.Furthermore,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation capability.In the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image.Extensive experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.
基金sponsored by the National Nature Science Foundation of China (No.22076038)Natural Science Foundation of Henan (No.202300410044)。
文摘In this work,the nano-g-C_(3)N_(4)/Ui O-66-NH_(2)composite was prepared by one-step solvothermal method.The as-prepared composite was characterized by scanning electron microscopy,Brunner-Emmet-Teller measurement,energy dispersive spectrometer,X-ray diffraction,and Fourier transform infrared spectroscopy.By using nano-g-C_(3)N_(4)/Ui O-66-NH_(2)composite as sorbent,a dispersive solid-phase extraction coupled with high-performance liquid chromatography was developed to sensitive analysis of food colorants including tartrazine,amaranth,carmine,sunset yellow,allura red and bright blue.The experiment parameters including the amount of sorbent,adsorption time,the p H of adsorption solution,desorption time,desorption solvent,the p H of desorption solution as well as the proportion between desorption solvent and buffer solvent were investigated.Under the optimized conditions,the limits of detection(S/N=3) and limits of quantitation (S/N=10) were determined in the ranges of 0.08-0.8 and 0.2-2.0 ng/m L,respectively.With the developed sample pretreatment method,carmine and brilliant blue were determined from blueberry juice by HPLC-DAD.The contents were calculated as 1.53μg/m L and0.17μg/mL,respectively.
基金supported by the Natural Science Foundation of Hebei Province (Grant No.: B2020201002)the National Natural Science Foundation of China (Grant Nos.: 21974034 and 21505030)the Interdisciplinary Research Project of Natural Science of Hebei University (Grant No.: DXK201912)
文摘In this study,a functionalized covalent-organic framework(COF)was first synthesized using porphyrin as the fabrication unit and showed an edge-curled,petal-like and well-ordered structure.The synthesized COF was then introduced to prepare porous organic polymer monolithic materials(POPMs).Two composite POPM/COF monolithic materials with rod shapes,referred to as sorbent A and sorbent B,were prepared in stainless steel tubes using different monomers.Sorbents A and B exhibited relatively uniform porous structures and enhanced specific surface areas of 153.14 m;/g and 80.01 m;/g,respectively.The prepared composite monoliths were used as in-tube solid-phase extraction(SPE)sorbents combined with HPLC for the on-line extraction and quantitative analytical systems.Indole alkaloids(from Catharanthus roseus G.Don and Uncaria rhynchophylla(Miq.)Miq.Ex Havil.)contained in mouse plasma were extracted and quantitatively analyzed using the online system.The two composite multifunctional monoliths showed excellent clean-up ability for complex biological matrices,as well as superior selectivity for target indole alkaloids.Method validation showed that the RSD values of the repeatability(n=6)were≤3.46%,and the accuracy expressed by the spiked recoveries was in the ranges of 99.38%-100.91%and 96.39%-103.50%for vinca alkaloids and Uncaria alkaloids,respectively.Furthermore,sorbents A and B exhibited strong reusability,with RSD values≤5.32%,which were based on the peak area of the corresponding alkaloids with more than 100 injections.These results indicate that the composite POPM/COF rod-shaped monoliths are promising media as SPE sorbents for extracting trace compounds in complex biological samples.
基金sponsored by National Natural Science Foundation of China(No.22076038)Natural Science Foundation of Henan Province,China(No.202300410044)Henan Key Scientific Research Programs to Universities and Colleges(No.22zx003).
文摘Metal-organic frameworks(MOFs)received considerable attention to adsorption and removal of various environmental pollutants because of some inherent advantages.However,it is challenging but meaningful to design and fabricate hierarchical mixed-dimensional MOFs with synergistic effects to enhance the performance for removal and preconcentration of environmental pollutants.Herein,a new hierarchical two-dimensional(2D)-three-dimensional(3D)mixed-dimensional cactus-like MOF@MOF hybrid material(PCN-134@Zr-BTB)was prepared by in-situ growth of 2D MOF nanosheets(Zr-BTB)on the surface of 3D MOF(PCN-134).The PCN-134@Zr-BTB composites combine the advantages of 2D and 3D MOFs with extensive mesoporous structures and large surface area for effective removal and enrichment of bisphenols(BPs).In comparison with pristine PCN-134 and Zr-BTB materials,the PCN-134@Zr-BTB hybrid material presented excellent adsorption performance for BPs.The adsorption isotherms are consistent with the Langmuir model,and the maximum adsorption capacity of four bisphenols(BPs)ranged from 135.1 mg/g to 628.9 mg/g.The adsorption kinetics are in accordance with the pseudo-second-order model.The recoveries ranged from 72.8%to 108%.The limits of detection were calculated at 0.02-0.03 ng/mL.The enrichment factors were calculated in the range of 310-374.According to FT-IR and XPS analysis,the main adsorption mechanisms are hydrogen bonding and π-π stacking.Nevertheless,this work provides a new and convenient strategy for the preparation of new hierarchical mixed-dimensional MOF@MOF(PCN-134@Zr-BTB)hybrid material for extraction and enrichment of BPs from aqueous matrix.
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
基金supported by the National Natural Science Foundation of China(No.21607003).
文摘Residues of tetracycline antibiotics(TCs) in environments may be harmful to human.Due to their high polarities,it is extremely challenging to efficiently enrich TCs with low concentrations in natural waters for analysis.In this work,a magnetic metal-organic framework Fe_(3)O_(4)@[Cu_(3)(btc)_(2)]was synthesized and applied as a dispersive micro-solid phase extraction adsorbent for TCs enrichment.Effects of dispersive micro-solid phase extraction conditions including extraction time,solution p H,and elution solvent on the extraction efficiencies of TCs were investigated.Results show that TCs could be enriched efficiently by Fe_(3)O_(4)@[Cu_(3)(btc)_(2)],and electrostatic interaction between TCs and Fe_(3)O_(4)@[Cu_(3)(btc)_(2)]dominated this process.Combined with liquid chromatography-tandem mass spectrometry,four TCs residues (oxytetracycline,tetracycline,chlortetracycline,and doxycycline) in natural waters were determined.The detection limits (LOD,S/N=3) of the four antibiotics were 0.01-0.02μg/L,and the limits of quantitation (LOQ,S/N=10)were 0.04-0.07μg/L.The recoveries obtained from river water and aquaculture water spiked with three TCs concentration levels ranged from 70.3%to 96.5%with relative standard deviations of 3.8%-12.8%.Results indicate that the magnetic metal-organic framework based dispersive micro-solid phase extraction is simple,rapid and high-loading for antibiotics enrichment from water,which further expand the practical application of metal-organic frameworks in sample pretreatment for environmental pollutant analysis.
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
基金supported by Key Research and Development Project of Shandong Province(2021ZDSYS12)National Natural Science Foundation of China(22076086,21777089)+3 种基金Taishan Scholar Program of Shandong Province(ts20190948)Shandong Province Science and Technology Small and Medium Enterprises Innovation Ability Enhancement Project(2023TSGC0689,2023TSGC0055)Natural Science Foundation of Shandong Province(ZR2021MB086,ZR2023QB035)Jinan City University and Institute Innovation Team Project(2021GXRC061,20228045,202333027)。
文摘Organophosphorus pesticides(OPPs)in foods pose a serious threat to human health,motivating the development of novel analytical methods for their rapid detection and quantification.A magnetic covalent organic framework(M-COF)adsorbent for the magnetic solid-phase extraction(MSPE)of OPPs from foods was reported.M-COF was synthesized by the Schiff base condensation reaction of 1,3,5-tris(4-aminophenyl)benzene and 4,4-biphenyldicarboxaldehyde on the surface of amino-functionalized magnetic nanoparticles.Density functional theory(DFT)calculations showed that adsorption of OPPs onto the surface of M-COF involved hydrophobic effects,van der Waals interactions,π-πinteractions,halogen-N bonding,and hydrogen bonding.Combined with gas chromatography-mass spectrometry(GC-MS)technology,the MSPE method features low limits of detection for OPPs(0.002-0.015μg/L),good reproducibility(1.45%-6.14%),wide linear detection range(0.01-1μg/L,R≥0.9935),and satisfactory recoveries(87.3%-110.4%).The method was successfully applied for the trace analysis of OPPs in spiked fruit juices.
基金supported by the National Natural Science Foundation of China(22371091,21975104,22150004,21731002 and 22101099)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20240598)+2 种基金the China Postdoctoral Science Foundation(2024M751118)the Guangdong Major Project of Basic and Applied Research(2019B030302009)the support from the Guangdong Basic and Applied Basic Research Foundation(2024A1515010897)。
文摘The development of novel metal-organic frameworks(MOFs)as solid adsorbents for rapid and efficient extraction of uranium from natural seawater is a long-term pursuit,yet remains challenging.In this work,we have prepared four two-dimensional(2D)vinylene-linked cyclic trinuclear units(CTUs)based MOFs.The metal nodes in the skeleton can be regulated,resulting in two Ag-CTU and two Cu-CTU-based MOFs with similar 2D hexagonal structures.These MOFs exhibit not only good stability in acid/base,γ-ray irradiation and natural seawater but also feature excellent anti-biofouling properties against five marine bacteria with inhibition rates as high as~99%.Interestingly,the alteration of Ag(I)to Cu(I)remarkably enhances the uranium sorption capacity due to the reduction of soluble U(VI)to insoluble U(IV)triggered by the chemical and photo-redox reaction of Cu-CTU.Due to the multiple functions,the Cu-CTU-based MOF delivers the best overall performance for uranium extraction from natural seawater with a high adsorption capacity of 7.96 mg g^(-1)and adsorption rate of~1.00 mg g^(-1)day^(-1),which is much higher than most of reported representative solid adsorbents.Our work paves a new way for rationally designing synergistic MOFs with superior performance for extracting uranium from natural seawater.
