Evidence map›Paper›PMID 28823034›Full record

ArticleJournal of molecular modeling2017

Effect of pristine and functionalized single- and multi-walled carbon nanotubes on CO

Karim Golzar, Hamid Modarress, Sepideh Amjad-Iranagh

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In one paragraph

Article in Journal of molecular modeling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Symbiosis-inspired de novo synthesis of ultrahigh MOF growth mixed matrix membranes for sustainable carbon capture.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  3. Article
  4. 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

3 authors.

Karim GolzarDepartment of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran.
Hamid ModarressDepartment of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran. hmodares@aut.ac.ir.
Sepideh Amjad-IranaghDepartment of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular dynamics (MD) and grand canonical Monte Carlo (GCMC) simulations were conducted to investigate the transport properties of carbon dioxide, methane, nitrogen, and oxygen through pure and mixed matrix membranes (MMMs) based on polymers of intrinsic microporosity (PIM-1). For this purpose, first, 0.5 to 3 wt% of pristine single-walled carbon nanotube (p-SWCNT) and multi-walled carbon nanotube (p-MWCNT) were embedded into the pure PIM-1, and then for better dispersion of CNT particles into the polymer matrix and to improve the performance of the resulting MMMs, polyethylene glycol (PEG) functionalized SWCNT and MWCNT (f-SWCNT and f-MWCNT, respectively) were loaded. The characterization of the obtained MMMs was carried out by using density, glass transition temperature, X-ray pattern, and fractional free volume calculations. Comparing the obtained results with the available reported experimental data, indicate the authenticity of the applied simulation approach. The simulation results exhibit that the pristine and PEG-functionalized CNT particles improve the transport properties such as diffusivity, solubility, and permeability of the PIM-1 membranes, without sacrificing their selectivity. Also, the MMMs incorporated with 2 wt% of the functionalized CNT particles indicate better performance for the CO

Indexed as

Mixed matrix membraneMolecular dynamics simulations, grand canonical Monte Carlo simulationPolymers of intrinsic microporosity (PIM-1)Single- and multi-walled carbon nanotube

Identifiers

What OpenQuestion holds

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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.