Evidence map›Paper›PMID 35457992›Full record

ArticleNanomaterials (Basel, Switzerland)2022

Electrospun Cellulose-Acetate/Chitosan Fibers for Humic-Acid Removal: Improved Efficiency and Robustness with a Core-Sheath Design.

Yirong Zhang, Yixiang Wang

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

2 authors.

Yirong ZhangDepartment of Food Science and Agricultural Chemistry, McGill University, Ste Anne de Bellevue, QC H9X 3V9, Canada.
Yixiang WangDepartment of Food Science and Agricultural Chemistry, McGill University, Ste Anne de Bellevue, QC H9X 3V9, Canada.ORCID 0000-0001-8386-7491

Funding

China Scholarship Council CSC No. 202107970010Fonds de Recherche du Québec - Nature et Technologies 266796Natural Sciences and Engineering Research Council NSERC DGECR-2019-00472Natural Sciences and Engineering Research Council NSERC RGPIN-2019-04498
6 · The paper itself

Abstract

Recycling biomass waste into functional materials has attracted much attention, and a rational structural design can make more effective use of each component. In our previous work, the fabrication of electrospun cellulose-acetate (CA)/chitosan (CS) adsorbents for humic-acid (HA) removal guided by the intermolecular interaction mechanism was demonstrated. Herein, a core-sheath structure was designed via one-step co-axial electrospinning, where a mixture of CS and CA was employed as the sheath layer to efficiently adsorb HA, and cellulose nanocrystals (CNCs) derived from waste cotton fabrics were incorporated into the CA core as load-bearing components. Compared to the non-layered electrospun CS/CA fibers, all the CS/CA-CNC fibers with a core-sheath structure exhibited smaller diameters, greater homogeneity, and significantly improved mechanical strength. Meanwhile, their maximum adsorption capacities towards HA had no significant differences. Even after the complete hydrolysis of CA into cellulose, the electrospun fibers maintained the fibrous structures and showed a higher tensile strength while exhibiting an acceptable adsorption capacity towards HA. Therefore, this work demonstrates the importance of rational design in the efficient preparation of functional materials and the feasibility of using electrospun core-sheath fibers derived from biomass wastes for the removal of water contaminants.

Indexed as

adsorbentscellulose acetatecellulose nanocrystalschitosanco-axial electrospinning

Identifiers

PMID35457992
PMCPMC9026307

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.