Evidence map›Paper›PMID 39794370›Full record

ArticleScientific reports2025

Kinetics, central composite design and artificial neural network modelling of ciprofloxacin antibiotic photodegradation using fabricated cobalt-doped zinc oxide nanoparticles.

Asmaa I Meky, Mohamed A Hassaan, Mohamed A El-Nemr, Howida A Fetouh, Amel M Ismail, Ahmed El Nemr

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

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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

Who cites it

2 citing papers in PubMed.

  1. Review
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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

6 authors.

Asmaa I MekyDepartment of Chemistry, Faculty of Science, Alexandria University, Alexandria, Egypt.
Mohamed A HassaanEnvironment Division, National Institute of Oceanography and Fisheries (NIOF), Kayet Bey, Elanfoushy, Alexandria, Egypt.
Mohamed A El-NemrDepartment of Chemical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt.
Howida A FetouhDepartment of Chemistry, Faculty of Science, Alexandria University, Alexandria, Egypt.
Amel M IsmailDepartment of Chemistry, Faculty of Science, Alexandria University, Alexandria, Egypt.
Ahmed El NemrEnvironment Division, National Institute of Oceanography and Fisheries (NIOF), Kayet Bey, Elanfoushy, Alexandria, Egypt. ahmedmoustafaelnemr@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cobalt-doped zinc oxide nanoparticles were fabricated and examined in this study as a potential photocatalyst for the antibiotic ciprofloxacin (CIPF) degradation when exposed to visible LED light. The Co-precipitation technique created Cobalt-doped zinc oxide nanoparticles that were 5, 10, and 15% Co-loaded. Different known techniques have been used to characterize the synthesized ZnO and cobalt-doped ZnO nanoparticles. Compared to ZnO and other Cobalt-doped ZnO nanoparticles, the experiments showed that 10% Cobalt-doped ZnO nanoparticles were a very effective catalyst for CIPF photodegradation. According to XRD, these NPs have a hexagonal Wurtzite structure with an average size of between 38.47 and 48.06 nm. Tauc plot displayed that the optical energy band-gap of ZnO NPs (3.21) slowly declines with Co doping (2.75 eV). The enhanced photocatalytic activity of Cobalt-doped ZnO nanoparticles, which avoids electron-hole recombination, is brought on by the implantation of Co. Within 90 min, a 30 mg/L solution of ciprofloxacin was destroyed (> 99%). The kinetics studies demonstrated that the first-order model, with R

Indexed as

Anti-Bacterial AgentsCiprofloxacinCobaltMetal NanoparticlesNanoparticlesNeural Networks, ComputerPhotolysisZinc OxideCatalysisKineticsLightAnti-Bacterial AgentsCiprofloxacinCobaltZinc OxideCentral composite designCiprofloxacinCo-precipitationCo-ZnO NPsPhotocatalysisWastewater treatment

Identifiers

PMID39794370
PMCPMC11724027

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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.