WeChat Mini Program
Old Version Features

HPLC Using Capillary Monolithic Column Molecularly Imprinted with Composite Metal Organic Frame for Enrichment and Detection of Ponceau 4R in Carthami Flos

PubMed(2020)

Department of Pharmacy

Cited 0|Views10
Abstract
OBJECTIVE:To assess the performance of high-performance liquid chromatography (HPLC) combined with capillary monolithic column molecularly imprinted with metal organic frame (UiO-66-NH2@MIPs capillary monolithic column) for enrichment, purification and detection of Ponceau 4R in Carthami flos. METHODS:UiO-66-NH2@MIPs monolithic columns were prepared via in situ polymerization, and the adsorption properties and morphology of the columns were characterized by HPLC, scanning electron microscopy (SEM) and infrared (IR) spectral analysis. HPLC with the prepared columns was performed for detecting the content of Ponceau 4R in Carthami flos samples. RESULTS:The UiO-66-NH2@MIPs system showed a good linearity for detecting Ponceau 4R over the concentration range of 0.1-10.0 μg/mL with a correlation coefficient > 0.9999 and a detection limit (S/N=3) of 2.7×10-4 μg/mL. The mean recovery of Ponceau 4R in Carthami flos samples ranged from 82.60% to 105.56%, and the intra-day and inter-day relative standard deviation (RSD) values ranged from 2.4% to 3.4%. The recycling experiment showed that the system could be reused for sensitive detection of Ponceau 4R in Carthami flos. The capacity of UiO-66-NH2@MIPs column was 0.178 μg/mg, which was superior to that of other monolithic columns (0.089, 0.080, and 0.111 μg/ mg), demonstrating that the addition of UiO-66-NH2 increased the adsorption capacity of the system. Under the optimized conditions, the UiO-66-NH2@MIPs-HPLC system had an enrichment factor of over 73 folds with obviously reduced interference by the impurity peaks. CONCLUSIONS:The UiO-66-NH2@MIPs column-HPLC system has much better performance for enrichment, purification and detection of Ponceau 4R in Carthami flos than direct HPLC.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined