WeChat Mini Program
Old Version Features

Hollow Ruthenium Nanoparticles with Enhanced Catalytic Activity for Colorimetric Detection of C-Reactive Protein

ACS APPLIED NANO MATERIALS(2023)

Hanyang Univ | Korea Basic Sci Inst

Cited 2|Views5
Abstract
Hollow structures can improve thephysical and chemical characteristicsof nanoparticles. In the present work, we synthesized hollow rutheniumnanoparticles (HRNs) using a galvanic replacement reaction. Comparedto normal nanoparticles, the hollow structures had greater surfacearea, leading to enhanced transport of reactants. Transmission electronmicroscopy images revealed the formation of distinct hollow structureswith an average size of 30 nm. As a peroxidase mimic, the HRNs showedexcellent catalytic activity for the oxidation of 3,3 & PRIME;,5,5 & PRIME;-tetramethylbenzidinedue to the increased surface area of the hollow structure. Moreover,the catalytic efficiency of the HRNs was greater than that of horseradishperoxidase, due to the presence of hollow structures. The HRNs wereapplied to the colorimetric detection of C-reactive protein (CRP)by enzyme-linked immunosorbent assay (ELISA). The results displayedgreat sensitivity for CRP levels of 0.12-7.8 ng/mL and a limitof detection of 33.2 pg/mL. In the recovery test, the assay showedaccurate detection of CRP in spiked human serum with recovery valuesof 97.0-98.0%. The results of the present study reveal thevalidity and possibility of HRNs as alternatives to natural enzymesfor application to conventional ELISA.
More
Translated text
Key words
hollow nanoparticles,galvanicreplacement,noble metal,nanozymes,peroxidasemimic
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
Related Papers
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