Modular Assembly Drives Synthesis of High-Energy Linked/Fused Molecules

In this study, we propose a "modular assembly" strategy to synthesize a series of new trinitromethyl high-energy molecules. For a typical molecule 1, this approach reduces the number of synthetic steps from 7 to 2 and improves the yield from 1.0% to 48.3%, representing a more than 48-fold increase compared to the traditional "skeleton first, group later" method. All synthesized molecules exhibit exceptional energetic performance, with molecule <b>1</b> demonstrating a density of 1.913 g cm<sup>-3</sup>, a detonation velocity of 9151 m s<sup>-1</sup>, and a thermal decomposition temperature of 177 °C, marking it as highly promising among trinitromethyl high-energy molecules.

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Publication Details

Journal
Organic Letters
Published
2025-02-07
DOI
https://doi.org/10.1021/acs.orglett.4c04599
Citations
4
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.07

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Modular Assembly Drives Synthesis of High-Energy Linked/Fused Molecules

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Modular Assembly Drives Synthesis of High-Energy Linked/Fused Molecules

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article en
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Abstract

In this study, we propose a "modular assembly" strategy to synthesize a series of new trinitromethyl high-energy molecules. For a typical molecule 1, this approach reduces the number of synthetic steps from 7 to 2 and improves the yield from 1.0% to 48.3%, representing a more than 48-fold increase compared to the traditional "skeleton first, group later" method. All synthesized molecules exhibit exceptional energetic performance, with molecule <b>1</b> demonstrating a density of 1.913 g cm<sup>-3</sup>, a detonation velocity of 9151 m s<sup>-1</sup>, and a thermal decomposition temperature of 177 °C, marking it as highly promising among trinitromethyl high-energy molecules.

Organic LettersVol. 27(18)
Beijing Institute of Technology (CN)
National Natural Science Foundation of China, Beijing Institute of Technology Research Fund Program for Young Scholars
Affordable and clean energy
Openalex Percentile: Top 15%
Machine Learning in Materials Science
2.07
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