The DOI matches its Crossref record.
High-Yield Purification Process of Singlewalled Carbon Nanotubes
Abstract
Reproducible high-yield purification process of singlewalled carbon nanotubes was developed by combining two-step processes of thermal annealing in air and acid treatment. The process involves the thermal annealing in air with the powders rotated at temperatures of 470 °C for 50 min, which burns out the carbonaceous particles, and an acid treatment with HCl for 24 h, which etches away the catalytic metals. Control of the annealing temperature and rotation of the sample are crucial for high yield. Our reproducible optimal purification process provides a total yield of about 25 ∼ 30 wt % with less than 1 wt % of transition metals, which was confirmed by the thermogravimetric analysis. Bundling and length control depending on the different acid treatments will be further discussed.
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
Cited by
Carbon Nanotube−Inorganic Hybrids
Eder, D. (2010) · Chemical Reviews · doi:10.1021/cr800433k · Cited by 819
Zhang, Q., Huang, J., Qian, W., Zhang, Y., & Wei, F. (2013) · Small · doi:10.1002/smll.201203252 · Cited by 759
Electrophoretic deposition of carbon nanotubes
Boccaccini, A. R., Cho, J., Roether, J. A., Thomas, B. J. C., Jane Minay, E., & Shaffer, M. S. P. (2006) · Carbon · doi:10.1016/j.carbon.2006.06.021 · Cited by 678
Dispersion of Carbon Nanotubes in Liquids
Hilding, J., Grulke, E. A., George Zhang, Z., & Lockwood, F. (2003) · Journal of Dispersion Science and Technology · doi:10.1081/dis-120017941 · Cited by 668
Carbon nanotubes, science and technology part (I) structure, synthesis and characterisation
Aqel, A., El-Nour, K. M. M. A., Ammar, R. A. A., & Al-Warthan, A. (2012) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2010.08.022 · Cited by 615
Purification of Single-Wall Carbon Nanotubes by Electrochemical Oxidation
Fang, H.-T., Liu, C.-G., Liu, C., Li, F., Liu, M., & Cheng, H.-M. (2004) · Chemistry of Materials · doi:10.1021/cm035263h · Cited by 165
Metadata
- Authors
- Moon, J.-M., An, K. H., Lee, Y. H., Park, Y. S., Bae, D. J., & Park, G.-S.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2001-05-26
- Volume
- 105
- Issue
- 24
- Pages
- 5677-5681
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W1984250391
- Cited by
- 274
- References
- 0
- FWCI
- 11.05
- 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.
