Evidence map›Paper›PMID 42146748›Full record

ArticleACS materials Au2026

Cross-Linkable Protein Nanofiber Aerogels for the Removal of Cationic and Anionic Dyes.

Nasim Fadaie, Lenka Vitkova, Wenqiang Wang, Xiaying Xin, Kevin De France

Abstract read
In one paragraph

Article in ACS materials Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Nasim FadaieDepartment of Chemical Engineering, Queen's University, 19 Division St, Kingston, Ontario K7L 3N6, Canada.
Lenka VitkovaDepartment of Chemical Engineering, Queen's University, 19 Division St, Kingston, Ontario K7L 3N6, Canada.ORCID https://orcid.org/0000-0002-6747-1785
Wenqiang WangDepartment of Civil Engineering, Queen's University, 58 University Ave, Kingston, Ontario K7L 3N9, Canada.ORCID https://orcid.org/0009-0002-2337-1499
Xiaying XinDepartment of Civil Engineering, Queen's University, 58 University Ave, Kingston, Ontario K7L 3N9, Canada.ORCID https://orcid.org/0000-0002-4724-3902
Kevin De FranceDepartment of Chemical Engineering, Queen's University, 19 Division St, Kingston, Ontario K7L 3N6, Canada.ORCID https://orcid.org/0000-0002-5545-4793

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Water contamination by synthetic dyes from textile, plastic, paint, and paper industries poses a significant environmental and public health challenge. These dyes are chemically stable, toxic, and prone to bioaccumulation, necessitating efficient and sustainable removal strategies. Here, we present protein nanofiber-based aerogels derived from bovine serum albumin (BSA) as effective biobased adsorbents for dye removal. BSA nanofibers were prepared through a high-temperature, acid-mediated self-assembly process and subsequently cross-linked and freeze-dried to form porous aerogels. Degree of cross-linking was varied to assess impacts on aerogel porosity, mechanical strength, and adsorption efficiency. Optimized aerogel formulations exhibited high removal efficiencies exceeding 99% for both cationic (crystal violet) and anionic (rose bengal) dyes. Adsorption isotherms were fitted using Langmuir, Freundlich, and Redlich-Peterson models; while all three showed good correlation, the Redlich-Peterson model provided the best overall fit, indicating a hybrid adsorption mechanism. The maximum Langmuir adsorption capacities achieved were 344 mg g

Indexed as

aerogelsamyloid nanofibersbovine serum albumin (BSA)dye adsorptionprotein cross-linkingwater treatment

Identifiers

PMID42146748
PMCPMC13177405

What OpenQuestion holds

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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.