Cyclobutane-linked nanothreads through thermal and photochemically mediated polymerization of cyclohexadiene

Carbon nanothreads are a rapidly growing class of 1D nanomaterials with sp<sup>3</sup>-hybridized diamond-like backbones. They are typically synthesized through pressure-induced polymerizations of aromatics, resulting in diverse structures and functionalities. Aside from precursor selection, there are limited means to control reaction pathway or polymerization outcome. Analogous to selection rules that govern outcomes in molecular chemistry, we investigated thermally and photochemically mediated pressure-induced polymerizations of 1,4-cyclohexadiene and explored the resultant products. Thermally mediated polymerization of 1,4-cyclohexadiene yields a crystalline product; yet identification of the backbone architecture is complicated by the product's less ordered packing in which only two Friedel pairs are observed. Support for cyclobutane-based structures is present when comparing experimentally-obtained data to computed structures, yet further evidence suggesting eliptical cross-sections consistent with <i>anti</i>-cyclobutanes is obtained when comparing experimental data obtained from a Paris Edinburgh (PE) synthesis. In contrast, the recovered product obtained from the photochemically mediated polymerization exhibits different <i>d</i>-spacings and is consistent with simulations that support a single pathway toward highly elliptical <i>syn</i>-cyclobutane-linked nanothreads. These results suggest that photochemistry can enable reaction selectivity in nanothread synthesis.

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Publication Details

Journal
Polymer Chemistry
Published
2025-01-01
DOI
https://doi.org/10.1039/d5py00470e
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Cyclobutane-linked nanothreads through thermal and photochemically mediated polymerization of cyclohexadiene

Bohan Xu, John V. Badding, Steven Huss, Elizabeth Elacqua et al.
1 citations
Polymer Chemistry
Machine Learning in Materials Science
0.52
article

Cyclobutane-linked nanothreads through thermal and photochemically mediated polymerization of cyclohexadiene

Bohan Xu, John V. Badding, Steven Huss, Elizabeth Elacqua, Vincent H. Crespi, Steven A. Lopez, Sikai Wu, Morgan Murphy, Katie Rank
article en
1 citations

Abstract

Carbon nanothreads are a rapidly growing class of 1D nanomaterials with sp<sup>3</sup>-hybridized diamond-like backbones. They are typically synthesized through pressure-induced polymerizations of aromatics, resulting in diverse structures and functionalities. Aside from precursor selection, there are limited means to control reaction pathway or polymerization outcome. Analogous to selection rules that govern outcomes in molecular chemistry, we investigated thermally and photochemically mediated pressure-induced polymerizations of 1,4-cyclohexadiene and explored the resultant products. Thermally mediated polymerization of 1,4-cyclohexadiene yields a crystalline product; yet identification of the backbone architecture is complicated by the product's less ordered packing in which only two Friedel pairs are observed. Support for cyclobutane-based structures is present when comparing experimentally-obtained data to computed structures, yet further evidence suggesting eliptical cross-sections consistent with <i>anti</i>-cyclobutanes is obtained when comparing experimental data obtained from a Paris Edinburgh (PE) synthesis. In contrast, the recovered product obtained from the photochemically mediated polymerization exhibits different <i>d</i>-spacings and is consistent with simulations that support a single pathway toward highly elliptical <i>syn</i>-cyclobutane-linked nanothreads. These results suggest that photochemistry can enable reaction selectivity in nanothread synthesis.

Polymer ChemistryVol. 16(25)
Northeastern University (US), Pennsylvania State University (US)
National Science Foundation, U.S. Department of Energy, Alfred P. Sloan Foundation, National Institutes of Health, Office of Science, National Nuclear Security Administration, Materials Research Science and Engineering Center, Harvard University, Division of Materials Research, Division of Chemistry, Argonne National Laboratory, Brookhaven National Laboratory
Openalex Percentile: Top 38%
Machine Learning in Materials Science
0.52
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