Evidence map›Paper›PMID 41407816›Full record

ArticleScientific reports2025

Integrated persulfate/ozonation process using Fe

Zahra Shahbeiki, Hojjat Nadimi, Maryam Khodadadi, Yousef Dadban Shahamat

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. Article
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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

4 authors.

Zahra ShahbeikiStudent Research Committee, Birjand University of Medical Sciences, Birjand, Iran.
Hojjat NadimiDepartment of Environmental Health Engineering, School of Health, Tehran University of Medical Sciences, Tehran, Iran.
Maryam KhodadadiDepartment of Environmental Health Engineering, School of Health, Medical Toxicology and Drug Abuse Research Center (MTDRC), , Birjand University of Medical Sciences, Birjand, Iran. maryam.khodadadi@gmail.com.
Yousef Dadban ShahamatEnvironmental Health Research Centre, Department of Environmental Health Engineering, School of Health, Golestan University of Medical Sciences, Gorgan, Iran. ydadban@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibiotics are considered emerging contaminants due to their persistence and potential ecological risks. Among them, cefixime is frequently detected in pharmaceutical effluents and requires effective removal strategies. This study aimed to optimize catalytic ozonation for cefixime degradation using persulfate activated by magnetic activated carbon (Fe3O4/AC or MAC). The effects of pH (5–11), contact time (5–60 min), catalyst dose (0–2.5 g/L), initial cefixime concentration (10–200 mg/L), and persulfate concentration (0–70 mM) were evaluated. A central composite design (CCD) under response surface methodology (RSM) was applied to model and optimize the process. Model performance was assessed by statistical indicators. The quadratic model showed good predictive ability (R2 = 0.90). Reaction time and pH were the most significant variables influencing cefixime degradation. Under optimized conditions (pH 8, cefixime 57.5 mg/L, MAC 1.5 g/L, persulfate 1.75 mM, and 46.25 min contact time), 97% removal efficiency was achieved. The findings indicate that catalytic ozonation activated with persulfate and MAC is an effective and statistically validated process for cefixime removal from aqueous solutions. These results support its potential applicability for pharmaceutical wastewater treatment.

Indexed as

Anti-Bacterial AgentsCefiximeCharcoalFerric CompoundsOzoneSulfatesWater Pollutants, ChemicalCatalysisHydrogen-Ion ConcentrationKineticsWater PurificationAnti-Bacterial AgentsCefiximeCharcoalFerric CompoundsOzoneSulfatesWater Pollutants, ChemicalAdvanced oxidation processesCatalytic ozonationCefiximeMagnetic activated carbonPersulfateRSM

Identifiers

PMID41407816
PMCPMC12779629

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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.