WeChat Mini Program
Old Version Features

(Invited) A Fast and Sensitive Detection Method for Detecting CO2 Reduction Products and Local Ph

ECS Meeting Abstracts(2018)

Cited 0|Views1
Abstract
The electrochemical pathway of CO2 reduction reaction (CO2RR) remains a promising technique to convert CO2 to value-added chemicals (e.g. CO, CH4, HCOOH, CH3OH, etc), which helps alleviate our dependence on fossil fuels as well as mitigate the rising CO2 concentration in the atmosphere. Nowadays, most of the CO2RR study focus on designing novel catalysts to improve their activity and selectivity. However, to evaluate the performance of new catalysts, a long time electrolysis (e.g. 30 min to several hours) is usually required to build up the concentration of products (both gaseous and liquid) enough before product analysis is performed by gas chromatography-mass spectrometry (GCMS) or nuclear magnetic resonance (NMR). This results in a long “pre-concentration-collection-detection” cycle and it greatly limits the efficiency of catalyst screening process as well as increases the energy cost. In addition, for catalysts that degrade quickly with time (within minutes), it is nearly impossible to monitor their product distribution change using traditional detection methods, which directly leads to the product information loss during the study of their degradation mechanisms. Therefore, it is urgent to develop a novel and sensitive detection method that can perform product analysis in a very short time scale (e.g. within minutes). In this work, we present a systematical study of using RRDE as a fast and reliable detection method for quantifying CO2RR products generated from both heterogeneous (e.g. Pt, Au, Sn) and homogeneous (e.g. [NiII(cyclam)]2+) catalysts. Local pH change during Au and Sn catalyzed CO2RR are also carefully investigated. The fast detection towards single product systems (e.g. H2, CO) and mixture product systems (e.g. H2 and HCOO-, H2 and CO) are discussed in detail. Based on Hori’s classification, catalysis systems of Pt, Au and Sn encompass nearly 90% of the heterogeneous CO2RR reduction products. Our results show that, compared with traditional detection methods (e.g. GCMS, NMR), RRDE shows a superior detection sensitivity (e.g. LOD ring ~10-18 moles of H2, defined in this work) as well as a significant short detection time (<1 min). Meanwhile, the product distribution as a function of applied electrolysis potential could be easily extracted from the detected ring voltammograms, which provides important information for the evaluation of catalysts’ performance.
More
Translated text
上传PDF
Bibtex
收藏
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