ArticleRSC advances2026
Efficacy of advanced Fenton-photo systems for the degradation of petroleum hydrocarbons using complex neural networks.
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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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.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Environmental contamination from petroleum hydrocarbons poses significant challenges, particularly in regions affected by oil industry activities and conflict-related environmental disasters. This study investigated AI-optimized photo-Fenton systems for remediation of oil-contaminated desert soils through three key objectives: (1) systematically evaluating multiple catalysts, particularly iron(iii) sulfate pentahydrate, and EDTA as a chelating agent for TPH removal, determining optimal conditions for specific reduction thresholds; (2) assessing four advanced photo-Fenton systems incorporating EDTA and Fe
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Registered trials
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.