Evidence map›Paper›PMID 35808040›Full record

ArticleNanomaterials (Basel, Switzerland)2022

Cavitand Decorated Silica as a Selective Preconcentrator for BTEX Sensing in Air.

Andrea Rozzi, Alessandro Pedrini, Roberta Pinalli, Enrico Cozzani, Ivan Elmi, Stefano Zampolli, Enrico Dalcanale

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Andrea RozziDepartment of Chemistry, Life Sciences and Environmental Sustainability and INSTM UdR Parma, University of Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy.
Alessandro PedriniDepartment of Chemistry, Life Sciences and Environmental Sustainability and INSTM UdR Parma, University of Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy.
Roberta PinalliDepartment of Chemistry, Life Sciences and Environmental Sustainability and INSTM UdR Parma, University of Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy.ORCID 0000-0002-0000-8980
Enrico CozzaniPROAMBIENTE S.c.r.l., Via P. Gobetti 101, 40129 Bologna, Italy.
Ivan ElmiCNR-IMM Bologna, Via P. Gobetti 101, 40129 Bologna, Italy.
Stefano ZampolliCNR-IMM Bologna, Via P. Gobetti 101, 40129 Bologna, Italy.ORCID 0000-0002-0431-0693
Enrico DalcanaleDepartment of Chemistry, Life Sciences and Environmental Sustainability and INSTM UdR Parma, University of Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy.ORCID 0000-0001-6964-788X

Funding

Ministry of Education, Universities and Research Department of Excellence initiativeRegione Emilia Romagna POR-FSE Bando Alte Competenze
6 · The paper itself

Abstract

The monitoring of benzene and other carcinogenic aromatic volatile compounds at the ppb level requires boosting both the selectivity and sensitivity of the corresponding sensors. A workable solution is the introduction in the devices of preconcentrator units containing molecular receptors. In particular, quinoxaline cavitands (QxCav) resulted in very efficient preconcentrator materials for the BTEX in air to the point that they have been successfully implemented in a commercial sensor. In this work, we report a highly efficient quinoxaline-based preconcentrator material, in which the intrinsic adsorption capacity of the QxCav has been maximized. The new material consists of silica particles covalently coated with a suitable functionalized QxCav derivative (QxCav@SiO

Indexed as

air monitoringBTEX sensingMEMS columnpreconcentratorquinoxaline cavitand

Identifiers

PMID35808040
PMCPMC9268237

What OpenQuestion holds

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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.