Novel strategies are proposed to circumvent the main drawbacks of flow-based cloud point extraction (CPE). The surfactant-rich phase (SRP) was directly retained into the optical path of the spectrophotometric cell, thus avoiding its dilution previously to the measurement and yielding higher sensitivity. Solenoid micro-pumps were exploited to improve mixing by the pulsed flow and also to modulate the flow-rate for retention and removal of the SRP, thus avoiding the elution step, often carried out with organic solvents. The heat released and the increase of the salt concentration provided by an on-line neutralization reaction were exploited to induce the cloud point without an external heating device. These innovations were demonstrated by the spectrophotometric determination of iron, yielding a linear response from 10 to 200 μg L(-1) with a coefficient of variation of 2.3% (n=7). Detection limit and sampling rate were estimated at 5 μg L(-1) (95% confidence level) and 26 samples per hour, respectively. The enrichment factor was 8.9 and the procedure consumed only 6 μg of TAN and 390 μg of Triton X-114 per determination. At the 95% confidence level, the results obtained for freshwater samples agreed with the reference procedure and those obtained for digests of bovine muscle, rice flour, brown bread and tort lobster agreed with the certified reference values. The proposed procedure thus shows advantages in relation to previously proposed approaches for flow-based CPE, being a fast and environmental friendly alternative for on-line separation and pre-concentration.
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http://dx.doi.org/10.1016/j.aca.2014.02.035 | DOI Listing |
Data Brief
February 2025
North Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United States.
Contemporary research in 3D object detection for autonomous driving primarily focuses on identifying standard entities like vehicles and pedestrians. However, the need for large, precisely labelled datasets limits the detection of specialized and less common objects, such as Emergency Medical Service (EMS) and law enforcement vehicles. To address this, we leveraged the Car Learning to Act (CARLA) simulator to generate and fairly distribute rare EMS vehicles, automatically labelling these objects in 3D point cloud data.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Mechanical Engineering Department, Tianjin University, No. 135, Yaguan Road, Haihe Education Park, Jinnan District, Tianjin City, 300350, China.
The use of AR technology in image-guided neurosurgery enables visualization of lesions that are concealed deep within the brain. Accurate AR registration is required to precisely match virtual lesions with anatomical structures displayed under a microscope. The purpose of this work was to develop a real-time augmented surgical navigation system using contactless line-structured light registration, microscope calibration, and visible optical tracking.
View Article and Find Full Text PDFUrban Inform
January 2025
IVL Swedish Environmental Research Institute LTD., PO Box 530 21, SE-400 14 Gothenburg, Sweden.
In response to the demand for advanced tools in environmental monitoring and policy formulation, this work leverages modern software and big data technologies to enhance novel road transport emissions research. This is achieved by making data and analysis tools more widely available and customisable so users can tailor outputs to their requirements. Through the novel combination of vehicle emissions remote sensing and cloud computing methodologies, these developments aim to reduce the barriers to understanding real-driving emissions (RDE) across urban environments.
View Article and Find Full Text PDFWaste Manag
January 2025
ZheJiang University, Department of Mechanical Engineering, ZheJiang, 310000, China.
With the rapid increase in end-of-life smartphones, enhancing the automation and intelligence of their recycling processes has become an urgent challenge. At present, the disassembly of discarded smartphones predominantly relies on manual labor, which is not only inefficient but also associated with environmental pollution and high labor intensity. In the context of end-of-life smartphone recycling, complex situations such as stacking and occlusion are commonly encountered.
View Article and Find Full Text PDFJ Colloid Interface Sci
January 2025
Institute of Advanced Chemistry of Catalonia (IQAC), Consejo Superior de Investigaciones Científicas (CSIC), Jordi Girona, 18-26, 08034 Barcelona, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN, ISCIII), Jordi Girona, 18-26, 08034 Barcelona, Spain. Electronic address:
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