Evidence map›Paper›PMID 42196066›Full record

ReviewGels (Basel, Switzerland)2026

Cellulose-Based Composite Hydrogels for Heavy Metal Ion Removal: Recent Advances and Engineering Perspectives.

Xiaobo Xue, Jihang Hu, Panrong Guo, Liyun Wang, Luohui Wang, Youming Dong, Fei Xiao, Cheng Li, Shen Ding

Abstract readReview
In one paragraph

Review in Gels (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Biodegradable Hydrogels for PbGels (Basel, Switzerland) · 2026
    Review
  8. Review
  9. Review
  10. Article
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

9 authors.

Xiaobo XueCollege of Forestry, Henan Agricultural University, Zhengzhou 450002, China.
Jihang HuResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China.
Panrong GuoCollege of Forestry, Henan Agricultural University, Zhengzhou 450002, China.
Liyun WangCollege of Forestry, Henan Agricultural University, Zhengzhou 450002, China.
Luohui WangCollege of Forestry, Henan Agricultural University, Zhengzhou 450002, China.
Youming DongCollege of Materials Science and Technology, Nanjing Forestry University, Nanjing 210037, China.ORCID 0000-0002-6496-3178
Fei XiaoHunan Academy of Forestry, Changsha 410018, China.
Cheng LiCollege of Forestry, Henan Agricultural University, Zhengzhou 450002, China.ORCID 0000-0001-5830-155X
Shen DingCollege of Forestry, Henan Agricultural University, Zhengzhou 450002, China.ORCID 0000-0003-0727-0268

Funding

Development and industrialization of new technologies and intelligent equipment for processing and utilizing bamboo ZL2025A002Quality and Safety Monitoring of Bamboo and Wood Products and Standard Formulation CS2025A007the Fundamental Research Funds for the Central Non-profit Research Institution of CAF CAFYBB2024MA033the Special Fund for Young Talents in Henan Agricultural University 30500928
6 · The paper itself

Abstract

With the rapid intensification of industrial and agricultural activities, water contamination by heavy metal ions has emerged as a critical global challenge, gravely imperiling ecosystem stability and public health. Among the various remediation technologies, adsorption has been widely adopted due to its high efficiency, low-cost water treatment, and simplicity of operation. However, conventional inorganic or synthetic adsorbents often exhibit poor degradability and pose a risk of secondary contamination, substantially limiting their sustainable application. Consequently, the development of environmentally benign and renewable adsorbent materials has become a central research focus in this field. Recently, cellulose-based composite hydrogels, derived from renewable resources and characterized by excellent eco-friendliness and highly tunable three-dimensional porous structures, have attracted considerable attention as promising green adsorption materials. These hydrogels demonstrate outstanding performance in the efficient sequestration of heavy metal contaminants from aqueous environments. This review systematically summarizes recent advances in cellulose-based composite hydrogels for heavy metal removal, to elucidate the structure-performance relationships linking material fabrication strategies, structural modulation, and adsorption efficiency. First, we outline the principal construction approaches, including physical crosslinking, chemical modification, and supramolecular self-assembly, and comprehensively analyze how different synthesis routes regulate pore architecture, mechanical properties, and the distribution of surface functional groups. Second, the underlying adsorption mechanisms, primarily coordination complexation, electrostatic interactions, and ion exchange, are discussed in detail. Finally, recent studies on the adsorption of cationic heavy metals (e.g., Pb(II), Cu(II), and Cd(II)) and anionic oxyanions (e.g., As(III) and Cr(VI)) are critically reviewed, with particular emphasis on the relationships between selective adsorption performance, material design principles, and specific recognition mechanisms. Overall, this review provides a theoretical foundation and practical guidance for the design and development of next-generation water treatment materials with high adsorption capacity, excellent selectivity, non-toxicity, and strong environmental compatibility, followed by future research recommendations.

Indexed as

celluloseheavy metal adsorptionhydrogelpollution remediationsustainable development

Identifiers

PMID42196066
PMCPMC13206167

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.