Evidence map›Paper›PMID 40319120›Full record

ArticleScientific reports2025

Ultrafast green synthesis of silver nanoparticles as fluorescent nanosensors for determination of isoniazid and nitrofurantoin in biological fluids and pharmaceuticals.

Galal Magdy, Eman Aboelkassim, Fathalla Belal

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Galal MagdyPharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33511, Egypt. galal_magdy@pharm.kfs.edu.eg.
Eman AboelkassimPharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33511, Egypt.
Fathalla BelalPharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current study presents a green, straightforward, and ultrafast microwave-assisted method for the synthesis of Ag-NPs in only 40 s, using the Paeonia officinalis root extract as a reducing agent, for the first time. Characterization of the produced Ag-NPs was carried out using different spectroscopic and microscopic methods. They exhibited strong emission peak at 318 nm following excitation at 227 nm. The developed Ag-NPs were utilized as a green fluorescent nanosensor for determination of isoniazid (ISN) and nitrofurantoin sodium (NIF). The native fluorescence of Ag-NPs was observed to be quantitatively quenched by the studied drugs. Validation of the method exhibited linear relationship for ISN and NIF in the range of 20.0-100.0 µM and 10.0-60.0 µM with detection limits of 1.12 µM and 0.98 µM, respectively. Due to the high selectivity and sensitivity, the suggested method was also applied for ISN and NIF estimation in biological fluid samples and pharmaceutical formulations with acceptable % RSD and % recoveries. Assessment of the Ag-NPs' antimicrobial activity was performed, and the synthesized Ag-NPs showed noteworthy antimicrobial activities. The evaluation of the method's eco-friendliness and greenness was also performed using AGREE and ComplexGAPI metrics. ICHQ2 (R2) guidelines were followed to validate the developed method.

Indexed as

IsoniazidMetal NanoparticlesNitrofurantoinSilverGreen Chemistry TechnologyHumansLimit of DetectionIsoniazidNitrofurantoinSilverFluorescent nanosensorsIsoniazidNitrofurantoinSilver nanoparticles

Identifiers

PMID40319120
PMCPMC12049529

What OpenQuestion holds

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Registered trials

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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.