journal article·2026-05

High-resolution separation of multiple single-chirality carbon nanotubes in organic media via ethanol and bundle regulation

Dehua Yang, Xuan Chang, Xiaoyang Yuan, Xiaofei Yang, Jiayi Xing, Linhai Li, Wei Xi, Jianxin Guo, Huaping Liu, Jianhui Chen

Carbon· Volume 255
Publisher page

Abstract

Polymer wrapping is particularly attractive for single-wall carbon nanotube (SWCNT) device because it produces nanotube dispersions in device-compatible organic solvents that enable uniform, well-oriented ultrathin films. However, achieving single-chirality SWCNTs in organic polymer systems has remained a long-standing challenge due to insufficient chirality resolution. Here we report a simple, scalable, and universal polymer-based separation strategy enabled by ethanol–bundle synergy, delivering a significant quantitative advance in separation resolution. Ethanol induces selective polymer desorption and amplifies chirality-dependent coating differences. Regulated undispersed SWCNT bundles enhance chirality recognition by selectively capturing weakly coated species, which promotes selective aggregation and improves chirality selectivity. As a result, we obtain eight single-chirality SWCNT species in organic systems, including (6, 5), (7, 5), (7, 6), (8, 6), (8, 7), (9, 7), (10, 5), and (10, 6). Five species exceed 90% purity, and (8, 6), (9, 7), (8, 7), and (10, 6) are firstly obtained as single-chirality products in polymer systems. This method delivers substantially higher purity and a much broader set of isolated chiralities than prior polymer-only approaches, while providing a dramatic improvement in yield and cost effectiveness relative to typical aqueous routes. This work establishes a universal design paradigm to boost polymer-based separation resolution and accelerate SWCNT electronics and optoelectronics.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Carbon Nanotubes in Composites · Conducting polymers and applications · Covalent Organic Framework Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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