In the present study, a new cloud point extraction methodology based on the selective preconcentration and the extraction of stable lead in acidic conditions with 4',4''(5'')-di-tert-butyldicyclohexano-18-crown-6 as a chelating agent was developed, optimized and validated. A mixture of Triton X-114 as non-ionic surfactant and CTAB as cationic surfactant was used to produce micellar structures that incorporate the chelating agent. Phase separation, induced by coacervation, was achieved by increasing the temperature of the system above the cloud point temperature. Pb extraction efficiency was maximized through an optimisation process where the effect of each parameter (i.e. non-ionic and ionic surfactant concentrations, pH, chelating agent concentration and cloud point temperature) on the chemical recoveries of Pb was assessed. Under optimum experimental conditions, the method reaches recoveries greater than 67% for Pb in a variety of complex matrices. In order to facilitate the quantification of Pb by plasma based instrumentations, a back-extraction procedure using aqueous solution of ammonium citrate were performed on the surfactant rich phase in order to reduce the effects on sample introduction and non-spectral interferences. LOD and LOQ of 0.8µgL and 2.6µgL, respectively, were determined by ICP-OES for the complete procedure. Using the back-extraction approach, a preconcentration factor of 39 was achieved for an initial sample volume of 195mL. The ruggedness of the methodology was validated by determining Pb concentration in various environmental and biological samples.
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http://dx.doi.org/10.1016/j.talanta.2017.11.015 | DOI Listing |
Sensors (Basel)
January 2025
The 54th Research Institute, China Electronics Technology Group Corporation, College of Signal and Information Processing, Shijiazhuang 050081, China.
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January 2025
School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518000, China.
Exploring the relationships between plant phenotypes and genetic information requires advanced phenotypic analysis techniques for precise characterization. However, the diversity and variability of plant morphology challenge existing methods, which often fail to generalize across species and require extensive annotated data, especially for 3D datasets. This paper proposes a zero-shot 3D leaf instance segmentation method using RGB sensors.
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January 2025
Engineering Design, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g.
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January 2025
Sensor Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20878, USA.
Terrestrial laser scanners (TLS) are portable dimensional measurement instruments used to obtain 3D point clouds of objects in a scene. While TLSs do not require the use of cooperative targets, they are sometimes placed in a scene to fuse or compare data from different instruments or data from the same instrument but from different positions. A contrast target is an example of such a target; it consists of alternating black/white squares that can be printed using a laser printer.
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January 2025
Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency.
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