To improve the efficiency of high-throughput analysis, a three-dimensional (3D) magnetic FeO-biochar composite was synthesized and used as adsorbent of QuEChERS pretreatment method for the analysis of 49 pesticide residues in vegetables containing complex pigments. The structure evolution mechanism of FeO-biochar was discussed and the structural morphology was confirmed by a series of characterization methods. Multivariate approach was employed to optimize the extraction parameters including sample amount, solvent volume, NaCl amount, extraction time, anhydrous MgSO amount, adsorbent amount, purification time. The results revealed that extraction time and FeO-biochar amount had significant influences on the recovery yield of pesticides and the 20 min extraction time and 13.9 mg FeO-biochar are optimal conditions. Under these conditions, the FeO-biochar exhibited better cleanup of matrix co-extracts than conventional adsorbents, which reduced matrix effect and simplified extraction process. Moreover, the adsorption mechanism was further probed and turned out that the aromatic sheets on FeO-biochar dominated the π-π EDA (electron donor-acceptor) interaction for interfering substances. The proposed extraction method exhibited good linearity with correlation coefficient greater than 0.9902. The limits of quantitation (LOQs) were in the range of 0.03-0.67 μg kg. The average recoveries were between 81.3% and 117.3% with relative standard deviation (intra-day RSD = 0.5-7.5% and inter-day RSD = 0.6-6.9%). All results highlighted the excellent potential of FeO-biochar strategy in analysis of pesticide residues in vegetables containing complex pigments.
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http://dx.doi.org/10.1016/j.chroma.2019.460770 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Health Policy and Management, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Family physician program is one of the effective reforms of the health system in Iran, but despite the implementation of this program in rural areas and the passage of ten years since its implementation in two provinces of Fars and Mazandaran, its implementation has faced problems. The aim of this study is to identify and prioritize implementation solutions related to the challenges of the family physician program in Iran.
Methods: This is a qualitative study using semi-structured interviews with 22 snowball-sampled experts and managers of basic health insurers to extract problems and executive solutions through coding and data analysis using Atlas Ti software and content analysis in the first stage.
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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January 2025
Department of Electrical and Electronics, Faculty of Engineering, Alberoni University, Kapisa, Afghanistan.
This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA's reference voltage output.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFLight Sci Appl
January 2025
National Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, 410082, Changsha, China.
Accurately and swiftly characterizing the state of polarization (SoP) of complex structured light is crucial in the realms of classical and quantum optics. Conventional strategies for detecting SoP, which typically involves a sequence of cascaded optical elements, are bulky, complex, and run counter to miniaturization and integration. While metasurface-enabled polarimetry has emerged to overcome these limitations, its functionality predominantly remains confined to identifying SoP within the standard Poincaré sphere framework.
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