Evidence map›Paper›PMID 42280489›Full record

ArticlePolymers2026

Effect of Vacuum Heat Treatment on Surface Hydrophobicity and Chemical Composition of Moso Bamboo for Natural Fiber Composites.

Zilu Liang, Haiyun Jiang, Yimin Tan

Abstract read
In one paragraph

Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Zilu LiangSchool of Packaging and Materials Engineering, Hunan University of Technology, Zhuzhou 412007, China.
Haiyun JiangSchool of Packaging and Materials Engineering, Hunan University of Technology, Zhuzhou 412007, China.ORCID 0000-0002-2179-9523
Yimin TanSchool of Packaging and Materials Engineering, Hunan University of Technology, Zhuzhou 412007, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To enhance the interfacial bonding between bamboo and the polymer matrix in natural fiber composites (NFCs), vacuum heat treatment was applied to moso bamboo strips at temperatures ranging from 140 to 180 °C with holding times of 4 and 6 h. The effects of treatment conditions on the surface characteristics and chemical composition of bamboo were systematically investigated. Scanning electron microscopy (SEM), contact angle measurements, and Fourier transform infrared spectroscopy (FTIR) were employed to evaluate the changes in microstructure, surface wettability, and the main functional groups including α-cellulose, hemicellulose, and lignin. The results indicate that the severity of heat treatment (temperature-time combination) significantly influences the physicochemical properties of bamboo. Hemicellulose, which exhibited the lowest thermal stability, underwent pronounced degradation above 140 °C and showed the most substantial compositional variation. Although the relative contents of α-cellulose and lignin increased with increasing treatment severity, their absolute contents decreased. The vacuum environment was found to retard the degradation of α-cellulose to some extent. At 180 °C, severe disruption of the cell wall structure was observed, accompanied by the deformation and collapse of cell lumens. In addition, heat treatment increased the surface contact angle, indicating enhanced hydrophobicity, with temperature exerting a more pronounced effect than treatment time. FTIR analysis revealed a marked reduction in the intensity of the C=O stretching vibration of hemicellulose (~1730 cm

Indexed as

bamboochemical compositionheat treatmentsurface properties

Identifiers

PMID42280489
PMCPMC13259473

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.