A method based on cloud point extraction was developed to determine phthalate esters including di-ethyl-phthalate （DEP）, di- （2-ethylhexyl）-phthalate （DEHP） and di-cyclohexyl-phthalate （DCP） in environmental w...A method based on cloud point extraction was developed to determine phthalate esters including di-ethyl-phthalate （DEP）, di- （2-ethylhexyl）-phthalate （DEHP） and di-cyclohexyl-phthalate （DCP） in environmental water samples using high-performance liquid chromatography separation and ultraviolet detection （HPLC-UV）. The non-ionic surfactant Triton X-114 was chosen as extraction solvent. The parameters affecting extraction efficiency, such as concentrations of Triton X-114 and Na2SO4, equilibration temperature, equilibration time and centrifugation time were evaluated and optimized. Under the optimum conditions, the method can achieve preconcentration factors of 35, 88, 111 and detection of limits of 2.0, 3.8, 1.0 ng/ml for DEP, DEHP and DCP in 10-ml water sample, respectively. The proposed method was successfully applied to the determination of trace amount of phathalate esters in effluent water of the wastewater treatment plant and the lixivium of plastic fragments.展开更多
A novel approach was developed for the determination of ultratrace amounts of copper in water samples by using electrothermal atomic absorption spectrometry （ETAAS） after cloud point extraction （ CPE ）. 1-（ 2-Pyr...A novel approach was developed for the determination of ultratrace amounts of copper in water samples by using electrothermal atomic absorption spectrometry （ETAAS） after cloud point extraction （ CPE ）. 1-（ 2-Pyridylazo ） -2- naphthol was used as the chelating reagent and Triton X-114 as the mieellar-forming surfactant. CPE was conducted in a pH 8. 0 medium at 40 ℃ for 10 rain. After the separation of the phases by contrifugafion, the surfactant-rieh phase was diluted with 1 mL of a methanol solution of 0. 1 mol/L HNO3. Then 20μL of the diluted surfactant-rieh phase was injected into the graphite furnace for atomization in the absence of any matrix modifier. Various experimental conditions that affect the extraction and atomization processes were optimized. A detection limit of 5 ng/L was obtained after preconeentration. The linear dynamic range of the copper mass concentration was found to be 0-2.0 ng/mL, and the relative standard deviation was found to be less than 3. 1% for a sample containing 1.0 ng/mL Cu （ Ⅱ ）. This developed method was successfully applied to the determination of uhratraee amounts of Cu in drinking water, tap water, and seawater samples.展开更多
Cloud point extraction (CPE) with Tergitol TMN-6 was applied for the extraction of trace amounts of palladium (Pd(Ⅱ)), platinum (Pt(Ⅳ)), and gold (Au(Ⅲ)) in the soil of industrial sewage. Ammonium pyrolysine dithio...Cloud point extraction (CPE) with Tergitol TMN-6 was applied for the extraction of trace amounts of palladium (Pd(Ⅱ)), platinum (Pt(Ⅳ)), and gold (Au(Ⅲ)) in the soil of industrial sewage. Ammonium pyrolysine dithiocarbamate (APDC) was adopted as the chelating agent prior to CPE and then was detected by atomic absorption spectrometry (AAS). Different parameters such as the concentration of surfactants, chelating agent and salt, sample pH, equilibration temperature and time, centrifugation time and rates, and the effect of foreign ions were studied. Under optimum conditions, the low limits of detections are 1.4, 2.8 and 1.2 ng·ml^-1 and the enrichment factors are 21, 12, and 24 for Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ, respectively. The relative standard deviations vary from 0.6% to 1.0% (n=11). All correlation coefficients of the calibration curves are >0.9960. The proposed method was successfully applied for the determination of Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ) in the real soil of industrial sewage samples.展开更多
Cloud point extraction （CPE） processes with two silicone surfactants, Dow Coming DC-190 and DC-193, were studied as preconcentration and treatment for the water polluted by three trace polycyclic aromatic hydrocarbo...Cloud point extraction （CPE） processes with two silicone surfactants, Dow Coming DC-190 and DC-193, were studied as preconcentration and treatment for the water polluted by three trace polycyclic aromatic hydrocarbons （PAHs）： anthracene, phenanthrene and pyrene. For all cases, the volumes of surfactant-rich phase obtained by two silicone surfactants were very small, i.e. a lower water content in the surfactant-rich phase was obtained. For example, less than 3% of the initial solution was obtained in a 1% （by mass） surfactant solution, which was much smaller than that of TX-114 in the same surfactant concentration. And TX-114 is known as a high compact surfactant-rich phase among most nonionic surfactants, thus the comparison showed that an excellent enrichment was ensured in the analysis application by the CPE process with the silicone surfactants, and the lower water content obtained in the surfactant-rich phase is also important in the large scale water treatment. The influences of additives and phase separation methodology on the recovery of PAHs were discussed. Comparing with DC-193, DC-190 has a lower cloud point and a higher recovery （near 100%） of all the three PAHs in same surfactant concentration, which was required for application as a preconcentration process prior to HPLC system. However the DC-190 solution is hard to be phase separated only by heating, whereas DC-193 has a relative higher phase separating speed by heating, but a high cloud point （around 360K） limits its application. Due to the phase separation by heating is the only method of CPE suitable to the large scale water treatment, the mixtures of two silicone surfacrants solutions were investigated in this study. A solution containing 1% of mixed DC-190 and DC-193 （in the ratio of 90 ： 10） removed anthracene, phenanthrene and pyrene near 100% with a relative low cloud point and quick phase separating speed.展开更多
A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-...A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-hydroxyquinoline and Triton X-100 were used as the ligand and surfactant respectively. Nickel（Ⅱ） can form a hy-drophobic complex with 8-hydroxyquinoline, the complex can be extracted into the small volume surfactant rich phase at the cloud point temperature（CPT） for GFAAS determination. The factors affecting the cloud point extraction, such as pH, ligand concentration, surfactant concentration, and the incubation time were optimized. Under the optimal conditions, a detection limit of 12 ng/L and a relative standard deviation（RSD） of 2.9% were obtained for Ni（Ⅱ） determination. The enrichment factor was found to be 25. The proposed method was successfully applied to the determination of nickel（Ⅱ） in certified reference material and different types of water samples and the recovery was in a range of 95%―103%.展开更多
A method for the determination of trace mercury in water samples by hydride generation atomic absorption spectrophotometry after cloud point extraction was proposed in the present work. The effects of pH, concentratio...A method for the determination of trace mercury in water samples by hydride generation atomic absorption spectrophotometry after cloud point extraction was proposed in the present work. The effects of pH, concentration of surfactant, and equilibration time on cloud point extraction were discussed. The enhancement factor of 20 and the detection limit of 0.039 μg/L were obtained for mercury with relative standard deviation of 4.8% （n = 11）.展开更多
A new method was developed for the determination of sodium copper chlorophyll(SCC) by cloud point extraction preconcentration and spectrophotometry, for which Triton X-114 was selected as a nonionic surfactant. Severa...A new method was developed for the determination of sodium copper chlorophyll(SCC) by cloud point extraction preconcentration and spectrophotometry, for which Triton X-114 was selected as a nonionic surfactant. Several factors affecting the extraction efficiency of SCC and its subsequent determination, including the p H of the sample solution, salt and surfactant concentrations, and equilibration temperature and time, were studied and optimized. The extraction efficiency approached 99.4%.The calibration graph under the optimum conditions was linear in the concentration range of 3–220 mg/L with correlation coefficients> 0.9997(n = 8). The limit of detection for the analytes was 0.6 mg/L(S/N = 3). The proposed method is inexpensive, simple, and accurate for the extraction and determination of SCC in food samples.展开更多
With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship（QSPR） has been found for the cloud points of four series of non...With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship（QSPR） has been found for the cloud points of four series of nonionic surfactants（ a total of 65 surfactants）. The best-regressed model includes six descriptors, and the correlation coefficient of multiple determination is as high as 0. 962.展开更多
Cloud point extraction (CPE) has been used for the preconcentration of cadmium, after the formation of a complex with 1, 5-bis(di-2-pyridylmethylene) thiocarbonohydrazide (DPTH), and further determination by flame ato...Cloud point extraction (CPE) has been used for the preconcentration of cadmium, after the formation of a complex with 1, 5-bis(di-2-pyridylmethylene) thiocarbonohydrazide (DPTH), and further determination by flame atomic absorption spectrometry (FAAS) using Triton X-114 as surfactant. The main factors affecting the CPE, such as concentration of Triton X-114 and DPTH, pH, equilibration temperature and incubation time, were optimized for the best extract efficiency. Under the optimum conditions i.e., pH 5.4, [DPTH] = 6x10-3%, [Triton X-114] = 0.25% (v/v), an enhancement factor of 10.5 fold was reached. The lower limit of detection (LOD) obtained under the optimal conditions was 0.95 μg L?1. The precision for 8 replicate deter- minations at 20 and 100 μgL?1 Cd were 2.4 % and 2 % relative standard deviation (R.S.D.). The calibration graph using the preconcentration method was linear with a correlation coefficient of 0,998 at levels close to the detection limit up to at least 200 μgL?1. The method was successfully applied to the determination of cadmium in water, environmental and food samples and in a BCR-176 standard reference material.展开更多
2-(pyridine-2-yl)-N-p-chlorohydrazinecarbothioamide (HCPTS) was synthesized, characterized and successfully applied for the preconcentration of Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II) in wat...2-(pyridine-2-yl)-N-p-chlorohydrazinecarbothioamide (HCPTS) was synthesized, characterized and successfully applied for the preconcentration of Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II) in water, blood, and urine samples prior to graphite furnace atomic absorption determination (GFAAS);Hg was determined by cold vapor technique. Under the optimum experimental conditions (i.e. pH = 8, 10–4 M of HCPTS, 0.05% w/v of Triton X-114), calibration graphs were linear in the range of 0.02 to 200 ng?mL–1 for Co(II), Cd(II), Pb(II) and Ni(II);0.03 to 200 ng?mL–1 for Cu(II);0.07 to 200 ng?mL–1 for Fe(II) and Zn(II) and 0.02 to 150 ng?mL–1 for Hg(II). The enrichment factors were 43, 51, 41, 46, 54, 40, 45 and 52 for Cu(II), Ni(II),Zn (II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The limit of detection were found to be 0.019, 0.094, 0.0514, 0.052, 0.0165, 0.047, 0.068 and 0.041 ng?mL–1 for Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The developed method was applied to the determination of these metal ions in water, blood and urine samples with satisfactory results.展开更多
In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the clou...In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the cloud point extraction(CPE) on the polyphenol recovery and volumetric ratio were investigated. The results showed a maximum recovery of 95.71% that was obtained using 7.0 wt% Triton X-114, native pH(3.25), and 80 wt%polyphenol extract at 30 °C. The use of cloud point extraction was successful to recover the polyphenols from agroindustrial residue since it is a simple as well as of low-cost technique.展开更多
Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)...Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.展开更多
Mapping and analyzing rock mass discontinuities based on 3D(three-dimensional)point cloud(3DPC)is one of the most important work in the engineering geomechanical survey.To efficiently analyze the distribution of disco...Mapping and analyzing rock mass discontinuities based on 3D(three-dimensional)point cloud(3DPC)is one of the most important work in the engineering geomechanical survey.To efficiently analyze the distribution of discontinuities,a self-developed code termed as the cloud-group-cluster(CGC)method based on MATLAB for mapping and detecting discontinuities based on the 3DPC was introduced.The identification and optimization of discontinuity groups were performed using three key parameters,i.e.K,θ,and f.A sensitivity analysis approach for identifying the optimal key parameters was introduced.The results show that the comprehensive analysis of the main discontinuity groups,mean orientations,and densities could be achieved automatically.The accuracy of the CGC method was validated using tetrahedral and hexahedral models.The 3D point cloud data were divided into three levels(point cloud,group,and cluster)for analysis,and this three-level distribution recognition was applied to natural rock surfaces.The densities and spacing information of the principal discontinuities were automatically detected using the CGC method.Five engineering case studies were conducted to validate the CGC method,showing the applicability in detecting rock discontinuities based on 3DPC model.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,direc...The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.展开更多
Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing di...Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing distinct chain ends.Despite these structural differences,both types of polymers exhibit locally random-walk-like conformations,making it challenging to detect subtle spatial variations using conventional methods.In this study,we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers.By utilizing the Dynamic Graph CNN(DGCNN)model,we classify polymer conformations based on the 3D coordinates of monomers,capturing local and global topological differences without considering chain connectivity sequentiality.Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight,aligning with theoretical predictions.Additionally,interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences:cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis,while linear polymers show elongated ends with more spherical centers.These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern,providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.展开更多
Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the nove...Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.展开更多
Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more compl...Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.展开更多
Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results ca...Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.展开更多
The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreach...The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes.In contrast,unmanned aerial vehicle(UAV)photogrammetry is not limited by terrain conditions,and can efficiently collect high-precision three-dimensional(3D)point clouds of rock masses through all-round and multiangle photography for rock mass characterization.In this paper,a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling.The method is based on four steps:(1)Establish a point cloud spatial topology,and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms;(2)Extract discontinuities using the density-based spatial clustering of applications with noise(DBSCAN)algorithm and fit the discontinuity plane by combining principal component analysis(PCA)with the natural breaks(NB)method;(3)Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud;and(4)Adopt a Poisson reconstruction method for refined rock block modeling.The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys.The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks.The calculation results are accurate and reliable,which can meet the practical requirements of engineering.展开更多
基金Projected supported by the National Basic Research Program (973)of China (No. 2003CB415001)the Pilot Program of KnowledgeInnovation Program of Chinese Academy of Sciences (No. KZCX3-SW-431).
文摘A method based on cloud point extraction was developed to determine phthalate esters including di-ethyl-phthalate （DEP）, di- （2-ethylhexyl）-phthalate （DEHP） and di-cyclohexyl-phthalate （DCP） in environmental water samples using high-performance liquid chromatography separation and ultraviolet detection （HPLC-UV）. The non-ionic surfactant Triton X-114 was chosen as extraction solvent. The parameters affecting extraction efficiency, such as concentrations of Triton X-114 and Na2SO4, equilibration temperature, equilibration time and centrifugation time were evaluated and optimized. Under the optimum conditions, the method can achieve preconcentration factors of 35, 88, 111 and detection of limits of 2.0, 3.8, 1.0 ng/ml for DEP, DEHP and DCP in 10-ml water sample, respectively. The proposed method was successfully applied to the determination of trace amount of phathalate esters in effluent water of the wastewater treatment plant and the lixivium of plastic fragments.
基金the Analysis and Testing Foundation of Zhejiang Province(No 04045)
文摘A novel approach was developed for the determination of ultratrace amounts of copper in water samples by using electrothermal atomic absorption spectrometry （ETAAS） after cloud point extraction （ CPE ）. 1-（ 2-Pyridylazo ） -2- naphthol was used as the chelating reagent and Triton X-114 as the mieellar-forming surfactant. CPE was conducted in a pH 8. 0 medium at 40 ℃ for 10 rain. After the separation of the phases by contrifugafion, the surfactant-rieh phase was diluted with 1 mL of a methanol solution of 0. 1 mol/L HNO3. Then 20μL of the diluted surfactant-rieh phase was injected into the graphite furnace for atomization in the absence of any matrix modifier. Various experimental conditions that affect the extraction and atomization processes were optimized. A detection limit of 5 ng/L was obtained after preconeentration. The linear dynamic range of the copper mass concentration was found to be 0-2.0 ng/mL, and the relative standard deviation was found to be less than 3. 1% for a sample containing 1.0 ng/mL Cu （ Ⅱ ）. This developed method was successfully applied to the determination of uhratraee amounts of Cu in drinking water, tap water, and seawater samples.
基金supported by the National Natural Science Foundation of China(No.20961012)the Medical Neurobiology Key Laboratory of Kunming University of Science and Technology,Basic and Applied Research Project in Yunnan Province(No.2008ZC082M)+3 种基金the Analysis and Testing Foundation of Kunming University of Science and Technology(No.2010121)Innovation Fund for Smalland Medium Technology Based Firms(No.11C26215305936)Natural and Science Foundation of Yunnan Province(No.2010ZC027)Focus Fund of Department of Education in Yunnan Province(No.2010Z016)
文摘Cloud point extraction (CPE) with Tergitol TMN-6 was applied for the extraction of trace amounts of palladium (Pd(Ⅱ)), platinum (Pt(Ⅳ)), and gold (Au(Ⅲ)) in the soil of industrial sewage. Ammonium pyrolysine dithiocarbamate (APDC) was adopted as the chelating agent prior to CPE and then was detected by atomic absorption spectrometry (AAS). Different parameters such as the concentration of surfactants, chelating agent and salt, sample pH, equilibration temperature and time, centrifugation time and rates, and the effect of foreign ions were studied. Under optimum conditions, the low limits of detections are 1.4, 2.8 and 1.2 ng·ml^-1 and the enrichment factors are 21, 12, and 24 for Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ, respectively. The relative standard deviations vary from 0.6% to 1.0% (n=11). All correlation coefficients of the calibration curves are >0.9960. The proposed method was successfully applied for the determination of Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ) in the real soil of industrial sewage samples.
文摘Cloud point extraction （CPE） processes with two silicone surfactants, Dow Coming DC-190 and DC-193, were studied as preconcentration and treatment for the water polluted by three trace polycyclic aromatic hydrocarbons （PAHs）： anthracene, phenanthrene and pyrene. For all cases, the volumes of surfactant-rich phase obtained by two silicone surfactants were very small, i.e. a lower water content in the surfactant-rich phase was obtained. For example, less than 3% of the initial solution was obtained in a 1% （by mass） surfactant solution, which was much smaller than that of TX-114 in the same surfactant concentration. And TX-114 is known as a high compact surfactant-rich phase among most nonionic surfactants, thus the comparison showed that an excellent enrichment was ensured in the analysis application by the CPE process with the silicone surfactants, and the lower water content obtained in the surfactant-rich phase is also important in the large scale water treatment. The influences of additives and phase separation methodology on the recovery of PAHs were discussed. Comparing with DC-193, DC-190 has a lower cloud point and a higher recovery （near 100%） of all the three PAHs in same surfactant concentration, which was required for application as a preconcentration process prior to HPLC system. However the DC-190 solution is hard to be phase separated only by heating, whereas DC-193 has a relative higher phase separating speed by heating, but a high cloud point （around 360K） limits its application. Due to the phase separation by heating is the only method of CPE suitable to the large scale water treatment, the mixtures of two silicone surfacrants solutions were investigated in this study. A solution containing 1% of mixed DC-190 and DC-193 （in the ratio of 90 ： 10） removed anthracene, phenanthrene and pyrene near 100% with a relative low cloud point and quick phase separating speed.
基金Supported by the National Natural Science Foundation of China(No.20075009)
文摘A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-hydroxyquinoline and Triton X-100 were used as the ligand and surfactant respectively. Nickel（Ⅱ） can form a hy-drophobic complex with 8-hydroxyquinoline, the complex can be extracted into the small volume surfactant rich phase at the cloud point temperature（CPT） for GFAAS determination. The factors affecting the cloud point extraction, such as pH, ligand concentration, surfactant concentration, and the incubation time were optimized. Under the optimal conditions, a detection limit of 12 ng/L and a relative standard deviation（RSD） of 2.9% were obtained for Ni（Ⅱ） determination. The enrichment factor was found to be 25. The proposed method was successfully applied to the determination of nickel（Ⅱ） in certified reference material and different types of water samples and the recovery was in a range of 95%―103%.
文摘A method for the determination of trace mercury in water samples by hydride generation atomic absorption spectrophotometry after cloud point extraction was proposed in the present work. The effects of pH, concentration of surfactant, and equilibration time on cloud point extraction were discussed. The enhancement factor of 20 and the detection limit of 0.039 μg/L were obtained for mercury with relative standard deviation of 4.8% （n = 11）.
文摘A new method was developed for the determination of sodium copper chlorophyll(SCC) by cloud point extraction preconcentration and spectrophotometry, for which Triton X-114 was selected as a nonionic surfactant. Several factors affecting the extraction efficiency of SCC and its subsequent determination, including the p H of the sample solution, salt and surfactant concentrations, and equilibration temperature and time, were studied and optimized. The extraction efficiency approached 99.4%.The calibration graph under the optimum conditions was linear in the concentration range of 3–220 mg/L with correlation coefficients> 0.9997(n = 8). The limit of detection for the analytes was 0.6 mg/L(S/N = 3). The proposed method is inexpensive, simple, and accurate for the extraction and determination of SCC in food samples.
基金Supported by the National Natural Science Foundation of China(Nos.20676051,20573048)Youth Foundation of SouthernYangtze University(No.006283).
文摘With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship（QSPR） has been found for the cloud points of four series of nonionic surfactants（ a total of 65 surfactants）. The best-regressed model includes six descriptors, and the correlation coefficient of multiple determination is as high as 0. 962.
文摘Cloud point extraction (CPE) has been used for the preconcentration of cadmium, after the formation of a complex with 1, 5-bis(di-2-pyridylmethylene) thiocarbonohydrazide (DPTH), and further determination by flame atomic absorption spectrometry (FAAS) using Triton X-114 as surfactant. The main factors affecting the CPE, such as concentration of Triton X-114 and DPTH, pH, equilibration temperature and incubation time, were optimized for the best extract efficiency. Under the optimum conditions i.e., pH 5.4, [DPTH] = 6x10-3%, [Triton X-114] = 0.25% (v/v), an enhancement factor of 10.5 fold was reached. The lower limit of detection (LOD) obtained under the optimal conditions was 0.95 μg L?1. The precision for 8 replicate deter- minations at 20 and 100 μgL?1 Cd were 2.4 % and 2 % relative standard deviation (R.S.D.). The calibration graph using the preconcentration method was linear with a correlation coefficient of 0,998 at levels close to the detection limit up to at least 200 μgL?1. The method was successfully applied to the determination of cadmium in water, environmental and food samples and in a BCR-176 standard reference material.
文摘2-(pyridine-2-yl)-N-p-chlorohydrazinecarbothioamide (HCPTS) was synthesized, characterized and successfully applied for the preconcentration of Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II) in water, blood, and urine samples prior to graphite furnace atomic absorption determination (GFAAS);Hg was determined by cold vapor technique. Under the optimum experimental conditions (i.e. pH = 8, 10–4 M of HCPTS, 0.05% w/v of Triton X-114), calibration graphs were linear in the range of 0.02 to 200 ng?mL–1 for Co(II), Cd(II), Pb(II) and Ni(II);0.03 to 200 ng?mL–1 for Cu(II);0.07 to 200 ng?mL–1 for Fe(II) and Zn(II) and 0.02 to 150 ng?mL–1 for Hg(II). The enrichment factors were 43, 51, 41, 46, 54, 40, 45 and 52 for Cu(II), Ni(II),Zn (II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The limit of detection were found to be 0.019, 0.094, 0.0514, 0.052, 0.0165, 0.047, 0.068 and 0.041 ng?mL–1 for Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The developed method was applied to the determination of these metal ions in water, blood and urine samples with satisfactory results.
基金Supported by CAPES and Brazilian National Council for Scientific and Technological Development(CNPq)(150522/2018-5)
文摘In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the cloud point extraction(CPE) on the polyphenol recovery and volumetric ratio were investigated. The results showed a maximum recovery of 95.71% that was obtained using 7.0 wt% Triton X-114, native pH(3.25), and 80 wt%polyphenol extract at 30 °C. The use of cloud point extraction was successful to recover the polyphenols from agroindustrial residue since it is a simple as well as of low-cost technique.
基金Guangxi Key Laboratory of Spatial Information and Geomatics(21-238-21-12)Guangxi Young and Middle-aged Teachers’Research Fundamental Ability Enhancement Project(2023KY1196).
文摘Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.
基金supported by the National Key Research and Development Program of China(Grant Nos.2023YFC2907400 and 2021YFC2900500)the National Natural Science Foundation of China(Grant No.52074020).
文摘Mapping and analyzing rock mass discontinuities based on 3D(three-dimensional)point cloud(3DPC)is one of the most important work in the engineering geomechanical survey.To efficiently analyze the distribution of discontinuities,a self-developed code termed as the cloud-group-cluster(CGC)method based on MATLAB for mapping and detecting discontinuities based on the 3DPC was introduced.The identification and optimization of discontinuity groups were performed using three key parameters,i.e.K,θ,and f.A sensitivity analysis approach for identifying the optimal key parameters was introduced.The results show that the comprehensive analysis of the main discontinuity groups,mean orientations,and densities could be achieved automatically.The accuracy of the CGC method was validated using tetrahedral and hexahedral models.The 3D point cloud data were divided into three levels(point cloud,group,and cluster)for analysis,and this three-level distribution recognition was applied to natural rock surfaces.The densities and spacing information of the principal discontinuities were automatically detected using the CGC method.Five engineering case studies were conducted to validate the CGC method,showing the applicability in detecting rock discontinuities based on 3DPC model.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704。
文摘The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
基金the National Key R&D Program of China(No.2022YFB3707303)National Natural Science Foundation of China(No.52293471)。
文摘Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing distinct chain ends.Despite these structural differences,both types of polymers exhibit locally random-walk-like conformations,making it challenging to detect subtle spatial variations using conventional methods.In this study,we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers.By utilizing the Dynamic Graph CNN(DGCNN)model,we classify polymer conformations based on the 3D coordinates of monomers,capturing local and global topological differences without considering chain connectivity sequentiality.Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight,aligning with theoretical predictions.Additionally,interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences:cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis,while linear polymers show elongated ends with more spherical centers.These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern,providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.
文摘Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.
基金supported in part by the National Natural Science Foundation of China under Grant(62171257,U22B2001,U19A2052,62020106011,62061015)in part by the Natural Science Foundation of Chongqing under Grant(2023NSCQMSX2930)+1 种基金in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT under Grant(SCIE-QN-2022-05)in part by the Graduate Scientifc Research and Innovation Project of Chongqing under Grant(CYS22469).
文摘Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)the Scientific Research Project of PowerChina Huadong Engineering Corporation Limited(HDEC-2022-0301).
文摘Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.
基金supported by the National Natural Science Foundation of China(Grant Nos.41941017 and 42177139)Graduate Innovation Fund of Jilin University(Grant No.2024CX099)。
文摘The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes.In contrast,unmanned aerial vehicle(UAV)photogrammetry is not limited by terrain conditions,and can efficiently collect high-precision three-dimensional(3D)point clouds of rock masses through all-round and multiangle photography for rock mass characterization.In this paper,a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling.The method is based on four steps:(1)Establish a point cloud spatial topology,and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms;(2)Extract discontinuities using the density-based spatial clustering of applications with noise(DBSCAN)algorithm and fit the discontinuity plane by combining principal component analysis(PCA)with the natural breaks(NB)method;(3)Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud;and(4)Adopt a Poisson reconstruction method for refined rock block modeling.The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys.The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks.The calculation results are accurate and reliable,which can meet the practical requirements of engineering.
