ArticleACS omega2025
Transforming Cellulose Waste into Advanced Superhydrophobic Membranes for Effective Oil/Water Separation.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- MXene-based materials for oil-water separation: mechanisms, performance, environmental implications, and future directions.RSC advances · 2026Review
- MnOACS omega · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Separation of oily wastewater and emulsions is essential for environmental protection and for curbing the associated health and economic consequences. Recently, there has been tremendous interest in developing materials with special wettability toward the oil and water phases and utilizing them for oil/water separation applications. These materials are designed by precisely tuning the surface chemistry, surface energy, and roughness. Herein, we report a novel two-step approach for upcycling cellulose-rich wastepaper into superhydrophobic membranes. Wastepaper first underwent femtosecond laser surface structuring inside a dilute aqueous solution of FeCl
Identifiers
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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.