Evidence map›Paper›PMID 40710699›Full record

ReviewGels (Basel, Switzerland)2025

A State-of-the-Art Review on the Freeze-Thaw Resistance of Sustainable Geopolymer Gel Composites: Mechanisms, Determinants, and Models.

Peng Zhang, Baozhi Shi, Xiaobing Dai, Cancan Chen, Canhua Lai

Abstract readReview
In one paragraph

Review in Gels (Basel, Switzerland), 2025. 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. Article
  2. Article
  3. Article
  4. 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

5 authors.

Peng ZhangSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.ORCID 0000-0003-2787-3357
Baozhi ShiSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.
Xiaobing DaiSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.
Cancan ChenSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.ORCID 0009-0006-2758-4458
Canhua LaiSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.

Funding

Program for Leading Talents in Central Plains Science and Technology Innovation 254000510027
6 · The paper itself

Abstract

Geopolymer, as a sustainable, low-carbon gel binder, is regarded as a potential alternative to cement. Freeze-thaw (F-T) resistance, which has a profound influence on the service life of structures, is a crucial indicator for assessing the durability of geopolymer composites (GCs). Consequently, comprehending the F-T resistance of GCs is of the utmost significance for their practical implementation. In this article, a comprehensive and in-depth review of the F-T resistance of GCs is conducted. This review systematically synthesizes several frequently employed theories regarding F-T damage, with the aim of elucidating the underlying mechanisms of F-T damage in geopolymers. The factors influencing the F-T resistance of GCs, including raw materials, curing conditions, and modified materials, are meticulously elaborated upon. The results indicate that the F-T resistance of GCs can be significantly enhanced through using high-calcium-content precursors, mixed alkali activators, and rubber aggregates. Moreover, appropriately increasing the curing temperature has been shown to improve the F-T resistance of GCs, especially for those fabricated with low-calcium-content precursors. Among modified materials, the addition of most fibers and nano-materials remarkably improves the F-T resistance of GCs. Conversely, the effect of air-entraining agents on the F-T resistance of GCs seems to be negligible. Furthermore, evaluation and prediction models for the F-T damage of GCs are summarized, including empirical models and machine learning models. In comparison with empirical models, the models established by machine learning algorithms exhibit higher predictive accuracy. This review promotes a more profound understanding of the factors affecting the F-T resistance of GCs and their mechanisms, providing a basis for engineering and academic research.

Indexed as

deterioration mechanismsfreeze–thaw resistancegeopolymer gel compositesinfluence factorsprediction models

Identifiers

PMID40710699
PMCPMC12294357

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.