Evidence map›Paper›PMID 39687104›Full record

ArticleHeliyon2024

Development of silver-doped carbon dots sensor derived from lignin for dual-mode fluorometric and spectrophotometric determination of valsartan in a bulk powder and a commercial product.

Fatemah Aldakhil, Nawal A Alarfaj, Salma A Al-Tamimi, Maha F El-Tohamy

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

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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

4 authors.

Fatemah AldakhilDepartment of Chemistry, College of Science, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia.
Nawal A AlarfajDepartment of Chemistry, College of Science, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia.
Salma A Al-TamimiDepartment of Chemistry, College of Science, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia.
Maha F El-TohamyDepartment of Chemistry, College of Science, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Doping of carbon dots (CDs) with heteroatoms has garnered growing attention in recent years as a useful method of controlling their physicochemical properties. In this study, a new dual-mode sensor based on silver-doped CDs (AgCDs) derived from lignin was developed for fluorometric and spectrophotometric determination of valsartan (VAL). The analysis of AgCDs revealed a structure that closely resembled graphene oxide, with the successful doping of Ag. The mean particle size of AgCDs was 3.50 ± 0.89 nm and it exhibited a reasonable fluorescence quantum yield of 28.1 %. The emission at 612 nm of AgCDs is quenched by VAL after being excited at 275 nm due to a combination of dynamic and static quenching mechanisms. The enhancement in the absorbance of AgCDs upon the addition of the medication was measured at 275 nm. The most favorable circumstances for the dual-mode sensing were achieved with a pH of 8 and a volume of 0.10 mL of AgCDs. The measurements were conducted using fluorometry after 3 min at 10 °C, followed by spectrophotometry after 7 min at 20 °C. The fluorometric data indicated a linear response within the range of 2.0-50.0 μg/mL, while the spectrophotometric results showed a dynamic range of 5.0-100.0 μg/mL. The limits of detection (LODs) were 0.57 and 1.38 μg/mL for the fluorometric and spectrophotometric methods, respectively. The limits of quantification (LOQs) were 1.72 and 4.19 μg/mL for the fluorometric and spectrophotometric methods, respectively. The nano sensor efficiently assessed the presence of VAL in pharmaceutical tablets and produced a favorable outcome with the mean of recovery of 98.91 % and 99.76 % with relative standard deviation (RSD%) of 0.79 and 0.78 for the fluorometric and spectrophotometric methods, respectively.

Indexed as

Carbon quantum dotsDopingDual-mode sensorGreen chemistryValsartan

Identifiers

PMID39687104
PMCPMC11648884

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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.