Cloud point extraction coupled with HPLC-UV for the determination of phthalate esters in environmental water samples

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.

Determination of Ultratrace Amounts of Copper(Ⅱ) in Water Samples by Electrothermal Atomic Absorption Spectrometry After Cloud Point Extraction

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.

Cloud point extraction and flame atomic absorption spectrometry analysis of palladium, platinum, and gold ions from industrial polluted soil

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.

Cloud Point Extraction of Polycyclic Aromatic Hydrocarbons in Aqueous Solution with Silicone Surfactants

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.

Determination of Trace Amounts of Nickel (Ⅱ) by Graphite Furnace Atomic Absorption Spectrometry Coupled with Cloud Point Extraction

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%.

Determination of Mecruy at Trace Level in Natural Water Samples by Hydride Generation Atomic Absorption Spectrophotometry after Cloud Point Extraction Preconcentration

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）.

Extraction of Sodium Copper Chlorophyll in Food Samples Based on Cloud Point Extraction with a Nonionic Surfactant

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.

Studies on the Cloud Points of Nonionic Surfactants with QSPR

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.

Preconcentration of Cadmium in Environmental Samples by Cloud Point Extraction and Determination by FAAS

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.

A New Thioamide Derivative for Separation and Preconcentration of Multi Elements in Aquatic Environment by Cloud Point Extraction

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.

Recovery of polyphenols from camu-camu(Myrciaria dubia H.B.K.McVaugh)depulping residue by cloud point extraction

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.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on Point Cloud

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

基于PointCloudTransformer和优化集成学习的三维点云分类

针对三维点云的不规则性和无序性所导致的难于提取特征并进行分类的问题,提出了一种融合深度学习和集成学习的三维点云分类方法。首先,训练深度学习点云分类网络PointCloudTransformer,并使用主干网络提取点云特征,进而训练基分类器,获得基分类器集合;然后,针对集成学习算法设计基分类器选择模型,模型的优化目标为基分类器组合的差异性和平均总体精度。为了降低集成规模,本文基于增强后的白鲸优化算法提出了二元多目标白鲸优化算法,并使用该算法优化基分类器选择模型,获得集成剪枝方案集合;最后,采用多数投票法集成每个基分类器组合在测试集点云特征上的分类结果,获得最优基分类器组合,从而构建基于多目标优化剪枝的集成学习点云分类模型。在点云分类数据集上的实验结果表明,本文方法使用了更小的集成规模,获得了更高的集成精度,能够对多类别三维点云进行准确分类。

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

Estimating wood quality attributes from dense airborne LiDAR point clouds

Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees.Unlike object reconstruction methods,our approach is based on simple metrics computed on vertical slices that summarize information on point distances,angles,and geometric attributes of the space between and around the points.Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans.We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios.Our approach provides a simple,clear,and scalable solution that can be adapted to different situations both for research and more operational mapping.

A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging applications.Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities.Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds.However,there has been a lack of focus on making the most of the numerous existing augmentation techniques.Addressing this deficiency,this research investigates the possibility of combining two fundamental data augmentation strategies.The paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named RandomFusion.Instead of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or sample.This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D.The crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data set.The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks.This is achieved without compromising computational efficiency.By examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds.RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models.The insights gained from this research can pave the way for future work aimed at developing more advanced and efficient data augmentation strategies for 3D lidar point cloud analysis.

A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

Optimization of the Use of Spherical Targets for Point Cloud Registration Using Monte Carlo Simulation

Registrations based on the manual placement of spherical targets are still being employed by many professionals in the industry.However,the placement of those targets usually relies solely on personal experience without scientific evidence supported by numerical analysis.This paper presents a comprehensive investigation,based on Monte Carlo simulation,into determining the optimal number and positions for efficient target placement in typical scenes consisting of a pair of facades.It demonstrates new check-up statistical rules and geometrical constraints that can effectively extract and analyze massive simulations of unregistered point clouds and their corresponding registrations.More than 6×10^(7) sets of the registrations were simulated,whereas more than IOO registrations with real data were used to verify the results of simulation.The results indicated that using five spherical targets is the best choice for the registration of a large typical registration site consisting of two vertical facades and a ground,when there is only a box set of spherical targets available.As a result,the users can avoid placing extra targets to achieve insignificant improvements in registration accuracy.The results also suggest that the higher registration accuracy can be obtained when the ratio between the facade-to-target distance and target-to-scanner distance is approximately 3:2.Therefore,the targets should be placed closer to the scanner rather than in the middle between the facades and the scanner,contradicting to the traditional thought. Besides,the results reveal that the accuracy can be increased by setting the largest projected triangular area of the targets to be large.