In this work, a metal-organic framework derived nanoporous carbon （MOF-5-C） was fabricated and modified with Fe3O4 magnetic nanoparticles. The resulting magnetic MOF-5-derived porous carbon （Fe304@MOF-5-C） was the...In this work, a metal-organic framework derived nanoporous carbon （MOF-5-C） was fabricated and modified with Fe3O4 magnetic nanoparticles. The resulting magnetic MOF-5-derived porous carbon （Fe304@MOF-5-C） was then used for the magnetic solid-phase extraction of chlorophenols （CPs） from mushroom samples prior to high performance liquid chromatography-ultraviolet detection. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and N2 adsorption were used to characterize the adsorbent. After experimental optimization, the amount of the adsorbent was chosen as 8.0 mg, extraction time as 10 min, sample volume as 50 mL, desorption solvent as 0.4 mL （0.2 mL × 2） of alkaline methanol, and sample pH as 6. Under the above optimized conditions, good linearity for the analytes was obtained in the range of 0.8-100.0 ng g 1 with the correlation coefficients between 0.9923 and 0.9963. The limits of detection （SIN= 3） were in the range of 0.25-0.30 ng g-1, and the relative standard deviations were below 6.8%. The result showed that the Fe304@MOF-5-C has an excellent adsorption capacity for the analytes.展开更多
This series of papers deal with vessels-target recognition. The project is conducted by using fuzzy neuraI networks and basing recognition on the spectra of vessel radiated-noise. Paper (Ⅰ) describes the characterist...This series of papers deal with vessels-target recognition. The project is conducted by using fuzzy neuraI networks and basing recognition on the spectra of vessel radiated-noise. Paper (Ⅰ) describes the characteristics of vessel radiated-noise spectra, which are composed of two distinctive categories: the stationary and the non-stationary. The project framework is introduced in the paper. It includes two steps. One is to extract effectively recognizable features (those common in one category and those distinguish categories). The other is to memorize the characteristics of specific vessel targets. The memorization is realized with characteristics pattern plate library of specific vessels (including line spectrum, double-frequency spectrum and average power spectrum pattern plate library). Detailed discussions on theories, models, parameter analysis, line-spectrum extraction methods, as well as gaps between reality and theory concerning vessels radiated-noise are also included in Paper (Ⅰ). Paper (Ⅰ) finally proposes a method of automatic extraction of line spectrum by using machines. Paper (Ⅱ) will discuss the stability, uniqueness of line spectrum and its pattern plate. Paper (Ⅲ) will focus on the extraction of features from double-frequency spectrum and average power spectrum, and the establishment of their pattern plates. Paper (Ⅳ) will discuss fuzzy neural networks and recognition approaches展开更多
基金Financial support from the National Natural Science Foundation of China (Nos. 31471643, 31571925)the Innovation Research Program of the Department of Education of Hebei for Hebei Provincial Universities (No. LJRC009)
文摘In this work, a metal-organic framework derived nanoporous carbon （MOF-5-C） was fabricated and modified with Fe3O4 magnetic nanoparticles. The resulting magnetic MOF-5-derived porous carbon （Fe304@MOF-5-C） was then used for the magnetic solid-phase extraction of chlorophenols （CPs） from mushroom samples prior to high performance liquid chromatography-ultraviolet detection. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and N2 adsorption were used to characterize the adsorbent. After experimental optimization, the amount of the adsorbent was chosen as 8.0 mg, extraction time as 10 min, sample volume as 50 mL, desorption solvent as 0.4 mL （0.2 mL × 2） of alkaline methanol, and sample pH as 6. Under the above optimized conditions, good linearity for the analytes was obtained in the range of 0.8-100.0 ng g 1 with the correlation coefficients between 0.9923 and 0.9963. The limits of detection （SIN= 3） were in the range of 0.25-0.30 ng g-1, and the relative standard deviations were below 6.8%. The result showed that the Fe304@MOF-5-C has an excellent adsorption capacity for the analytes.
文摘This series of papers deal with vessels-target recognition. The project is conducted by using fuzzy neuraI networks and basing recognition on the spectra of vessel radiated-noise. Paper (Ⅰ) describes the characteristics of vessel radiated-noise spectra, which are composed of two distinctive categories: the stationary and the non-stationary. The project framework is introduced in the paper. It includes two steps. One is to extract effectively recognizable features (those common in one category and those distinguish categories). The other is to memorize the characteristics of specific vessel targets. The memorization is realized with characteristics pattern plate library of specific vessels (including line spectrum, double-frequency spectrum and average power spectrum pattern plate library). Detailed discussions on theories, models, parameter analysis, line-spectrum extraction methods, as well as gaps between reality and theory concerning vessels radiated-noise are also included in Paper (Ⅰ). Paper (Ⅰ) finally proposes a method of automatic extraction of line spectrum by using machines. Paper (Ⅱ) will discuss the stability, uniqueness of line spectrum and its pattern plate. Paper (Ⅲ) will focus on the extraction of features from double-frequency spectrum and average power spectrum, and the establishment of their pattern plates. Paper (Ⅳ) will discuss fuzzy neural networks and recognition approaches
