WeChat Mini Program
Old Version Features

Lighting Up Industrial Mechanochemistry: Real-Time in Situ Monitoring of Reactive Extrusion Using Energy-Dispersive X-Ray Diffraction

Chem(2024)SCI 1区

BAM Fed Inst Mat Res & Testing

Cited 1|Views13
Abstract
Mechanochemistry is an environmentally friendly synthetic approach that enables the sustainable production of a wide range of chemicals while reducing or eliminating the need for solvents. Reactive extrusion aims to move mechanochemistry from its conventional gram-scale batch reactions, typically performed in laboratory ball mills, to a continuous, large-scale process. Meeting this challenge requires in situ monitoring techniques to gain insights into reactive extrusion and its underlying processes. While the effectiveness of in situ Raman spectroscopy in providing molecular-level information has been demonstrated, our study uses energy-dispersive X-ray diffraction to monitor reactive extrusion in real time at the crystalline level. Our results provide previously unavailable control over the reactive extrusion process, promoting its perception as an industrially feasible green alternative to traditional solvent- based syntheses.
More
Translated text
Key words
mechanochemistry,reactive extrusion,green chemistry,in situ studies,time-resolved in situ,TRIS,synchrotron radiation,scalable synthesis,solid-state reactions,reaction mechanisms
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