WeChat Mini Program
Old Version Features

Towards an Improved Understanding of the Evolution of the Size Distribution of Ultrafine Nanoparticles Produced by a Rapidly Quenched Vapor Source

METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE(2024)

Guangdong University of Technology

Cited 0|Views36
Abstract
In this study, we explore the evolution of the size distribution of Ag nanoparticles produced by a spark ablation vapor source. The as-prepared Ag nanoparticles exhibit a special bimodal lognormal size distribution, containing both ultrafine ( 1.5 nm) and larger (3 to 15 nm) particles. Such a size distribution differs from previous investigations of nanoparticles generated by spark ablation, which had larger particles with a lognormal distribution. The 1.5 nm size of the ultrafine Ag nanoparticles in this study is close to the theoretical size of the critical nucleus predicted by classic nucleation theory. By considering the simultaneous precipitation and coagulation of the particles and different coalescence dynamics at different sizes, we established a quantitative model that describes the evolution of the special size distribution of the Ag nanoparticles. The model’s predictions accurately reproduce the as-measured nanoparticle size distribution. This study can provide a new experimental and theoretical basis for understanding the kinetics of the growth of ultrafine nanoparticles produced by a rapidly quenched vapor source.
More
Translated text
Key words
Nanofabrication,Nanoparticles,Atomically Precise,Gold Nanoparticles
求助PDF
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