Evidence map›Paper›PMID 40458432›Full record

ArticleRSC advances2025

Transforming henna: from natural dye to photosensitizer for efficient photocatalytic degradation of 4-nitrophenol using a green-synthesized ZnO@henna nanocomposite.

Dana A Kader, Azhin H Mohammed, Sewara J Mohammed, Dara Muhammed Aziz

Abstract read
In one paragraph

Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

4 authors.

Dana A KaderDepartment of Chemistry, College of Education, University of Sulaimani Old Campus Sulaymaniyah 46001 Kurdistan Region Iraq dana.kader@univsul.edu.iq.ORCID https://orcid.org/0000-0002-5705-5868
Azhin H MohammedDepartment of Physics, College of Education, University of Sulaimani Sulaymaniyah 46001 Kurdistan Region Iraq.
Sewara J MohammedDepartment of Anesthesia, College of Health Sciences, Cihan University Sulaimaniya Sulaymaniyah City Kurdistan Iraq.ORCID https://orcid.org/0000-0003-3257-6242
Dara Muhammed AzizDepartment of Chemistry, College of Sciences, University of Raparin, Kurdistan Regional Government Main Street Ranyah 4601 2 Iraq.ORCID https://orcid.org/0000-0003-3362-6301

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research develops a green synthesis process for the ZnO@henna nanocomposite and examines its performance in degrading the 4-nitrophenol (4-NP) pollutant in water under visible light illumination. The method of synthesizing ZnO nanoparticles (ZnONPs) started with the use of kaffir lime extract, followed by conjugating henna extract, which contains the natural photosensitizer lawsone. The analysis of the synthesized ZnO@henna nanocomposite included FTIR, XRD, FESEM, EDS, TEM, UV-vis DRS, zeta potential, PL, and BET surface area to validate its formation and show property improvements. The band gap energy of ZnO decreased to 2.80 eV during UV-vis DRS analysis, thus extending the optical absorption into visible light wavelengths. Under blue LED light illumination, the ZnO@henna nanocomposite achieved 93% degradation of 4-NP within 120 minutes. The optimized photocatalytic degradation process occurred under a catalyst dosage of 25 mg combined with a pH value of 10 and an initial 4-NP concentration at 50 ppm. After four successive reaction cycles, the catalyst managed to maintain 85% efficiency in 4-NP mineralization. Laboratory investigations using scavenger experiments along with mechanistic studies proved that hydroxyl radicals (˙OH) and superoxide radicals (O

Identifiers

PMID40458432
PMCPMC12127921

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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.