Evidence map›Paper›PMID 39431069›Full record

ArticleACS omega2024

Fluorimetric Detection of Vapor Pollutants with Diketopyrrolopyrrole Polymer Microcavities.

Laura Magnasco, Andrea Lanfranchi, Martina Martusciello, Heba Megahd, Giovanni Manfredi, Paola Lova, Beata Koszarna, Daniel T Gryko, Davide Comoretto

Abstract read
In one paragraph

Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Laura MagnascoDipartimento di Chimica e Chimica Industriale, Università di Genova, Via Dodecaneso 31, 16146 Genova, Italy.
Andrea LanfranchiDipartimento di Chimica e Chimica Industriale, Università di Genova, Via Dodecaneso 31, 16146 Genova, Italy.ORCID https://orcid.org/0000-0003-3642-2611
Martina MartuscielloDipartimento di Chimica e Chimica Industriale, Università di Genova, Via Dodecaneso 31, 16146 Genova, Italy.
Heba MegahdDipartimento di Chimica e Chimica Industriale, Università di Genova, Via Dodecaneso 31, 16146 Genova, Italy.ORCID https://orcid.org/0000-0003-2385-6648
Giovanni ManfrediNovavido S.r.l., Via Paolo Nanni Costa 20, 40133 Bologna, Italy.ORCID https://orcid.org/0000-0003-0682-0731
Paola LovaDipartimento di Chimica e Chimica Industriale, Università di Genova, Via Dodecaneso 31, 16146 Genova, Italy.ORCID https://orcid.org/0000-0002-5634-6321
Beata KoszarnaInstitute of Organic Chemistry of the Polish Academy of Sciences, 01-224 Warsaw, Poland.ORCID https://orcid.org/0000-0003-4716-7169
Daniel T GrykoInstitute of Organic Chemistry of the Polish Academy of Sciences, 01-224 Warsaw, Poland.
Davide ComorettoDipartimento di Chimica e Chimica Industriale, Università di Genova, Via Dodecaneso 31, 16146 Genova, Italy.ORCID https://orcid.org/0000-0002-2168-2851

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing prevalence and detrimental effects of volatile organic compounds are driving the need for selective on-site sensors that do not require complex sampling or instrumentation. Broadband selective sensors exhibiting selectivity based on their distinct response mechanism is becoming of increasing technological relevance in both industrial and urban settings. In this context, we propose a label-free sensor based on a polymeric planar microcavity embedded with a fluorescent organic dye, designed to detect various pollutants in the vapor phase. The sensor consists of alternating layers of cellulose acetate and poly(

Identifiers

PMID39431069
PMCPMC11483407

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.