Evidence map›Paper›PMID 36435832›Full record

ArticleNature communications2022

Fast light-switchable polymeric carbon nitride membranes for tunable gas separation.

Timur Ashirov, Julya Stein Siena, Mengru Zhang, A Ozgur Yazaydin, Markus Antonietti, Ali Coskun

Abstract read
In one paragraph

Article in Nature communications, 2022. 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. Ultrafast Spectroscopy with anACS physical chemistry Au · 2026
    Article
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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

6 authors.

Timur AshirovDepartment of Chemistry, University of Fribourg, Chemin du Musee 9, 1700, Fribourg, Switzerland.ORCID http://orcid.org/0000-0002-7832-5716
Julya Stein SienaDepartment of Colloid Chemistry, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, D-14476, Potsdam, Germany.ORCID http://orcid.org/0000-0001-7412-7715
Mengru ZhangDepartment of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.
A Ozgur YazaydinDepartment of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.ORCID http://orcid.org/0000-0001-8562-723X
Markus AntoniettiDepartment of Colloid Chemistry, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, D-14476, Potsdam, Germany. office.cc@mpikg.mpg.de.ORCID http://orcid.org/0000-0002-8395-7558
Ali CoskunDepartment of Chemistry, University of Fribourg, Chemin du Musee 9, 1700, Fribourg, Switzerland. ali.coskun@unifr.ch.ORCID http://orcid.org/0000-0002-4760-1546

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) 200021-175947
6 · The paper itself

Abstract

Switchable gas separation membranes are intriguing systems for regulating the transport properties of gases. However, existing stimuli-responsive gas separation membranes suffer from either very slow response times or require high energy input for switching to occur. Accordingly, herein, we introduced light-switchable polymeric carbon nitride (pCN) gas separation membranes with fast response times prepared from melamine precursor through in-situ formation and deposition of pCN onto a porous support using chemical vapor deposition. Our systematic analysis revealed that the gas transport behavior upon light irradiation is fully governed by the polarizability of the permeating gas and its interaction with the charged pCN surface, and can be easily tuned either by controlling the power of the light and/or the duration of irradiation. We also demonstrated that gases with higher polarizabilities such as CO

Identifiers

PMID36435832
PMCPMC9701225

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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.