Evidence map›Paper›PMID 36950076›Full record

ArticleRSC advances2023

Preparation of a single and reusable biopolymer-based film for the extraction and preconcentration of anti-inflammatory drugs from environmental water samples.

Maite V Aguinaga Martínez, Claudia E Domini, Carolina C Acebal

Open access · goldAbstract read
In one paragraph

Article in RSC advances, 2023. 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
0.6field-weighted citation impact, top 41% of its field
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, 4 citations in OpenAlex.

  1. 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 at 1 institution in 1 country.

Maite V Aguinaga MartínezINQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET Av. Alem 1253 Bahía Blanca 8000 Argentina cdomini@criba.edu.ar cacebal@uns.edu.ar.
Claudia E DominiINQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET Av. Alem 1253 Bahía Blanca 8000 Argentina cdomini@criba.edu.ar cacebal@uns.edu.ar.
Carolina C AcebalINQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET Av. Alem 1253 Bahía Blanca 8000 Argentina cdomini@criba.edu.ar cacebal@uns.edu.ar.ORCID https://orcid.org/0000-0002-6659-0889
Consejo Nacional de Investigaciones Científicas y Técnicas · AR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the main goals of green chemistry is to reduce the use of toxic materials and the generation of hazardous waste, both during method development and in the synthesis of the materials used. Thus, a biodegradable, single and reusable material composed of agarose and multi-walled carbon nanotubes was proposed. The film preparation was carefully optimized in order to obtain a one-piece sorbent, with high extraction efficiency and the possibility of reuse. The film was tested in the simultaneous extraction and preconcentration of three non-steroidal anti-inflammatory drugs (ketorolac, ketoprofen and piroxicam) from environmental water samples. The optimal extraction parameters were as follows: isopropyl alcohol as the activation solvent, a sample pH value of 3.0, extraction time of 30 min, 2.00 mL of acetonitrile as the eluent, an elution time of 5 minutes, and a sample volume of 250.00 mL. Under these conditions, the film was reusable 50 times without losing its extraction capacity significantly. HPLC with a photodiode array detector was used for the separation and determination. The method presented a linear range between 0.10 and 1.2 μg L

Identifiers

PMID36950076
PMCPMC10025811
OpenAlexW4328022255

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.