The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Carbon Nanotubes-Adsorbed Electrospun PA66 Nanofiber Bundles with Improved Conductivity and Robust Flexibility
Abstract
Electrospun polyamide (PA) 66 nanofiber bundles with high conductivity, improved strength, and robust flexibility were successfully manufactured through simply adsorbing multiwall carbon nanotubes (MWNTs) on the surface of electrospun PA66 nanofibers. The highest electrical conductivity (0.2 S/cm) and tensile strength (103.3 MPa) were achieved for the bundles immersed in the suspension with 0.05 wt % MWNTs, indicating the formation of conductive network from adsorbed MWNTs on the surface of PA66 nanofibers. The decrease of porosity for the bundles immersed in the MWNT dispersion and the formation of hydrogen bond between PA66 nanofibers and MWNTs suggest a superb interfacial interaction, which is responsible for the excellent mechanical properties of the nanocomposite bundles. Furthermore, the resistance fluctuation under bending is less than 3.6%, indicating a high flexibility of the nanocomposite bundles. The resistance of the nanocomposite bundle had a better linear dependence on the temperature applied between 30 and 150 °C. More importantly, such highest working temperature of 150 °C far exceeded that of other polymer-based temperature sensors previously reported. This suggests that such prepared MWNTs-adsorbed electrospun PA66 nanofiber bundles have great potentials in high temperature detectors.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
One‐Dimensional Nanostructures: Synthesis, Characterization, and Applications
Xia, Y., Yang, P., Sun, Y., Wu, Y., Mayers, B., Gates, B., Yin, Y., Kim, F., & Yan, H. (2003) · Advanced Materials · doi:10.1002/adma.200390087 · Cited by 8,559
Cited by
Electrically conductive polymer composites for smart flexible strain sensors: a critical review
Liu, H., Li, Q., Zhang, S., Yin, R., Liu, X., He, Y., Dai, K., Shan, C., Guo, J., Liu, C., et al. (2018) · Journal of Materials Chemistry C · doi:10.1039/c8tc04079f · Cited by 709
Self-citationLi, Y., Zhou, B., Zheng, G., Liu, X., Li, T., Yan, C., Cheng, C., Dai, K., Liu, C., Shen, C., et al. (2018) · Journal of Materials Chemistry C · doi:10.1039/c7tc04959e · Cited by 459
Self-citationGuan, X., Xu, B., & Gong, J. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.104516 · Cited by 359
Self-citationEnhanced Cr(VI) removal from acidic solutions using biochar modified by Fe3O4@SiO2-NH2 particles
Shi, S., Yang, J., Liang, S., Li, M., Gan, Q., Xiao, K., & Hu, J. (2018) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.02.091 · Cited by 316
Polymer-based composites by electrospinning: Preparation & functionalization with nanocarbons
Lee, J. K. Y., Chen, N., Peng, S., Li, L., Tian, L., Thakor, N., & Ramakrishna, S. (2018) · Progress in Polymer Science · doi:10.1016/j.progpolymsci.2018.07.002 · Cited by 284
Metadata
- Authors
- Guan, X., Zheng, G., Dai, K., Liu, C., Yan, X., Shen, C., & Guo, Z.
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-05-24
- Volume
- 8
- Issue
- 22
- Pages
- 14150-14159
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W2360893915
- Cited by
- 254
- References
- 1
- FWCI
- 25.47
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.