ArticleMethodsX2023
Simple multi-residue analysis of persistent organic pollutants and molecular tracers in atmospheric samples.
Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed, 2 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a simple, selective and sensitive analytical method to quantitatively determine a wide range of halogenated persistent organic pollutants and molecular tracers in atmospheric samples. Identification and quantification was carried out by high-resolution gas chromatography, hyphenated with low-resolution mass spectrometry operating in electron impact (EI) and electron capture negative ionization (ECNI) mode. Optimization on a number of instrumental parameters was conducted to obtain ultra-trace detection limits, in the range of few fg/m
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Registered trials
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.