Systematic Optimization of the Synthesis of Confined Carbyne.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Clara Freytag, Christin Schuster, Emil Parth, Dido Denier van der Gon, Takeshi Saito, Kazuhiro Yanagi, Paola Ayala, Thomas Pichler
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引用次数: 0

Abstract

Confined carbyne, an sp1-hybridized linear carbon chain inside a carbon nanotube, is a novel material with remarkable properties and potential applications. Among its currently successful synthesis methods, high temperature high vacuum annealing is prevalent. Further optimization could be achieved by tuning the synthesis pathway. Here, a systematic analysis of key synthesis parameters including precursor filling, annealing step sequences, and temperature conditions during high temperature vacuum processing is performed. A novel yield determination model that overcomes previous limitations related to the irregular resonance Raman behavior of carbyne is applied to evaluate bulk yield and realized growth potential. With this refined model, it is possible to make a quantitative assessment of bulk yield optimization potential in multi-step annealing processes. These results provide crucial insights into the fundamental formation mechanisms of confined carbyne, advancing our understanding of this promising hybrid nanomaterial system. It is therefore possible to establish improved protocols for maximizing confined carbyne yields through precise control of synthesis conditions.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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