Chrome Extension
WeChat Mini Program
Use on ChatGLM

Reliable and Accessible Methods for Urea Quantification in Co-Reduction of Carbon-Dioxide- and Nitrogen-Containing Species

Yan Zhang, Gefei Huang, Haichuan Zhang, Xiaoyi Qiu, Guimei Liu, Yinuo Wang,Juhee Jang, Yian Wang,Zidong Wei,Zongwei Cai,Minhua Shao

Chem Catalysis(2025)

School of Chemistry and Chemical Engineering

Cited 0|Views4
Abstract
Electrocatalytic urea synthesis by the co-reduction of CO2 and nitrogen sources under mild conditions offers an attractive alternative to the conventional protocol. However, the quantification of urea poses significant challenges because of low yields and diverse byproducts, thereby raising concerns regarding the reliability of catalyst performance. This study systematically assesses the commonly used methods (urease, diacetyl monoxime, and 1H-NMR) in real electrochemical systems and identifies their potential limitations. We then propose an advanced analytical platform that uses ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) to quantify urea in electrolytes. This method exhibits high sensitivity, even at ultralow urea concentrations of 0.01 μg mL−1, without compromising accuracy in the presence of byproducts. Its reliability is validated through a series of experimental cases, eliminating the occurrence of false positives. These findings contribute to establishing a benchmark for quantifying urea in electrosynthesis, facilitating the development of efficient electrocatalysts.
More
Translated text
Key words
electrocatalytic synthesis,urea quantification,ultra-high-performance liquid chromatography,CO2 reduction,nitrogen sources,C–N coupling,nitrate reduction
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

要点】:该论文提出了一种可靠且易于使用的高效尿素量化方法,用于二氧化碳和含氮物种共还原过程中的电催化尿素合成,提高了催化剂性能评估的准确性。

方法】:作者采用了一种结合超高效液相色谱与高分辨率质谱(UHPLC-HRMS)的先进分析平台,对尿素在电解质中的含量进行量化。

实验】:通过在真实电化学系统中对常用量化方法(脲酶、二乙酰一肟和1H-NMR)进行评估,并使用UHPLC-HRMS方法进行实验验证,证明了该方法在痕量尿素浓度下的高灵敏度和准确性,实验数据集名称未在文中提及。