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
实体关系抽取是构建大规模知识图谱和专业领域数据集的重要基础之一,为此提出了一种基于预训练大语言模型的实体关系抽取框架(entity relation extraction framework based on pre-trained large language model, PLLM-RE),并针对循环...实体关系抽取是构建大规模知识图谱和专业领域数据集的重要基础之一,为此提出了一种基于预训练大语言模型的实体关系抽取框架(entity relation extraction framework based on pre-trained large language model, PLLM-RE),并针对循环经济政策进行了实体关系抽取研究。基于所提出的PLLM-RE框架,首先使用RoBERTa模型进行循环经济政策文本的实体识别,然后选取基于Transformer的双向编码器表示(bidirectional encoder representation from Transformers, BERT)模型进行循环经济政策实体关系抽取研究,以构建该政策领域的知识图谱。研究结果表明,PLLM-RE框架在循环经济政策实体关系抽取任务上的性能优于对比模型BiLSTM-ATT、PCNN、BERT以及ALBERT,验证了所提框架在循环经济政策实体关系抽取任务上的适配性和优越性,为后续循环经济领域资源的信息挖掘和政策分析提供了新思路。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.
文摘实体关系抽取是构建大规模知识图谱和专业领域数据集的重要基础之一,为此提出了一种基于预训练大语言模型的实体关系抽取框架(entity relation extraction framework based on pre-trained large language model, PLLM-RE),并针对循环经济政策进行了实体关系抽取研究。基于所提出的PLLM-RE框架,首先使用RoBERTa模型进行循环经济政策文本的实体识别,然后选取基于Transformer的双向编码器表示(bidirectional encoder representation from Transformers, BERT)模型进行循环经济政策实体关系抽取研究,以构建该政策领域的知识图谱。研究结果表明,PLLM-RE框架在循环经济政策实体关系抽取任务上的性能优于对比模型BiLSTM-ATT、PCNN、BERT以及ALBERT,验证了所提框架在循环经济政策实体关系抽取任务上的适配性和优越性,为后续循环经济领域资源的信息挖掘和政策分析提供了新思路。
