WeChat Mini Program
Old Version Features

Atomically Accurate Structural Tailoring of Thiacalix[4]arene-Protected Copper(ii)-Based Metallamacrocycles.

DALTON TRANSACTIONS(2023)

Cent South Univ

Cited 2|Views16
Abstract
Accurate manipulation of ligands at specific sites in robust clusters is attractive but difficult, especially for those ligands that coordinate in intricate binding patterns. By linking the shuttlecock-like {Cu4(μ4-Cl)TC4A} motif and the phenylphosphate (PhPO32-) ligand, we elaborately design and synthesize two Cu(II)-thiacalix[4]arene metallamacrocycles (MMCs), namely Cu12L3 and Cu16L4, which have regular triangular and quadrilateral topologies, respectively. While keeping the core intact, the Cl- and PhPO32- in those two MMCs, which coordinated in a μ4-bridging fashion, can be accurately substituted with salicylate ligands. Theoretical calculations have been carried out to reveal the effect of ligand tailoring on the electronic structure of clusters. Structural regulation can affect the catalytic activity of these clusters, which has been verified by using the clusters as catalysts for selective sulfide oxidation.
More
Translated text
Key words
Metal-Organic Frameworks
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