Evidence map›Paper›PMID 39206342›Full record

ArticleRSC advances2024

Universalized and robust length separation of carbon and boron nitride nanotubes with improved polymer depletion-based fractionation.

Pavel Shapturenka, Benjamin K Barnes, Elisabeth Mansfield, Matthew M Noor, Jeffrey A Fagan

Abstract read
In one paragraph

Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Single-wall carbon nanotube separationsChemical communications (Cambridge, England) · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Pavel ShapturenkaMaterials Science and Engineering Division, National Institute of Standards and Technology Gaithersburg MD 20899 USA jeffrey.fagan@nist.gov.ORCID https://orcid.org/0000-0001-7497-5220
Benjamin K BarnesMaterials Science and Engineering Division, National Institute of Standards and Technology Gaithersburg MD 20899 USA jeffrey.fagan@nist.gov.ORCID https://orcid.org/0000-0002-0865-8486
Elisabeth MansfieldApplied Chemicals and Materials Division, National Institute of Standards and Technology Boulder CO 80305 USA.ORCID https://orcid.org/0000-0003-2463-0966
Matthew M NoorMaterials Science and Engineering Division, National Institute of Standards and Technology Gaithersburg MD 20899 USA jeffrey.fagan@nist.gov.ORCID https://orcid.org/0000-0001-6929-9811
Jeffrey A FaganMaterials Science and Engineering Division, National Institute of Standards and Technology Gaithersburg MD 20899 USA jeffrey.fagan@nist.gov.ORCID https://orcid.org/0000-0003-1483-5554

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Partitioning nanoparticles by shape and dimension is paramount for advancing nanomaterial standardization, fundamental colloidal investigations, and technologies such as biosensing and digital electronics. Length-separation methods for single-walled carbon nanotubes (SWCNTs) have historically incurred trade-offs in precision and mass throughput, and boron nitride nanotubes (BNNTs) are a rapidly emerging material analogue. We extend and detail a polymer precipitation-based method to fractionate populations of either nanotube type at significant mass scale for four distinct nanotube sources of increasing average diameter (0.7 nm to >2 nm). Such separations result in a supernant phase containing shorter nanotubes and a pellet phase containing the longer nanotubes, with the threshold length for depletion decreasing with increasing polymer concentration. Cross-comparison through analytical ultracentrifugation, spectroscopy, and microscopy

Identifiers

PMID39206342
PMCPMC11353058

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.