Evidence map›Paper›PMID 42042710›Full record

ReviewNanomaterials (Basel, Switzerland)2026

Chitosan-Based Adsorbents: A Versatile Platform for the Removal of Arsenate and Copper Ions from Water.

Lingli Min, Shuhua Wang, Yuling Li, Yiting Lin, Yulang Chi

Abstract readReview
In one paragraph

Review in Nanomaterials (Basel, Switzerland), 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.

Lingli MinCollege of Resources and Environmental Science, Quanzhou Normal University, Quanzhou 362000, China.
Shuhua WangCollege of Resources and Environmental Science, Quanzhou Normal University, Quanzhou 362000, China.
Yuling LiCollege of Resources and Environmental Science, Quanzhou Normal University, Quanzhou 362000, China.
Yiting LinCollege of Resources and Environmental Science, Quanzhou Normal University, Quanzhou 362000, China.
Yulang ChiCollege of Oceanology and Food Science, Quanzhou Normal University, Quanzhou 362000, China.ORCID 0000-0002-0443-5189

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chitosan, owing to its abundant amino and hydroxyl functional groups, serves as an effective biosorbent for the removal of toxic metal(loid) ions from water. This review summarizes recent advances in chitosan-based adsorbents specifically for arsenate (As(V)) and copper ions (Cu(II)), with an emphasis on adsorption mechanisms and electrospun nanofiber technologies. A conceptual "charge adaptation-structure synergy" model is proposed to elucidate the distinct adsorption behaviors of chitosan toward anionic and cationic substances: under acidic conditions, As(V) adsorption is dominated by electrostatic attraction to protonated amino groups, whereas at pH values near or above the pKa, Cu(II) removal proceeds via synergistic chelation involving deprotonated amino and hydroxyl groups. Competitive and synergistic interactions in binary systems, particularly between As(V) and coexisting anions such as phosphate, are also discussed. Notably, the kinetic advantages of electrospun chitosan nanofibers are highlighted, with equilibrium times shortened from several hours to approximately 0.5-2.6 h. Key challenges and future research directions are further discussed, including scalable manufacturing and the treatment of complex wastewater matrices.

Indexed as

adsorption mechanismsanionic and cationic pollutantsbiosorbentwater purification

Identifiers

PMID42042710
PMCPMC13119143

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.