Evidence map›Paper›PMID 25246864›Full record

ArticleNanoscale research letters2014

Temperature-dependent gas transport performance of vertically aligned carbon nanotube/parylene composite membranes.

Lei Zhang, Junhe Yang, Xianying Wang, Bin Zhao, Guangping Zheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nanoscale research letters, 2014. 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. Review
  3. Ultra-Permeable Single-Walled Carbon Nanotube Membranes with Exceptional Performance at Scale.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020
    Article
  4. Review
  5. Article
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.

Lei ZhangSchool of Materials Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Junhe YangSchool of Materials Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Xianying WangSchool of Materials Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Bin ZhaoSchool of Materials Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Guangping ZhengDepartment of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A novel composite membrane consisting of vertically aligned carbon nanotubes (CNTs) and parylene was successfully fabricated. Seamless filling of the spaces in CNT forests with parylene was achieved by a low-pressure chemical vapor deposition (CVD) technique and followed with the Ar/O2 plasma etching to expose CNT tips. Transport properties of various gases through the CNT/parylene membranes were explored. And gas permeances were independent on feed pressure in accordance with the Knudsen model, but the permeance values were over 60 times higher than that predicted by the Knudsen diffusion kinetics, which was attributed to specular momentum reflection inside smooth CNT pores. Gas permeances and enhancement factors over the Knudsen model firstly increased and then decreased with rising temperature, which confirmed the existence of non-Knudsen transport. And surface adsorption diffusion could affect the gas permeance at relatively low temperature. The gas permeance of the CNT/parylene composite membrane could be improved by optimizing operating temperature.

Indexed as

Carbon nanotubeComposite membraneGas transportParylene

Identifiers

PMID25246864
PMCPMC4158770

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Registered trials

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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.