Evidence map›Paper›PMID 40942356›Full record

ArticleMaterials (Basel, Switzerland)2025

Rheological Optimization of 3D-Printed Cementitious Materials Using Response Surface Methodology.

Chenfei Wang, Junyin Lian, Yunhui Fang, Guangming Fan, Yixin Yang, Wenkai Huang, Shuqin Shi

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Chenfei WangCollege of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
Junyin LianCollege of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
Yunhui FangAdmixture Research Institute, KZJ New Materials Group Co., Ltd., Xiamen 361011, China.
Guangming FanCollege of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
Yixin YangCollege of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
Wenkai HuangCollege of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
Shuqin ShiCollege of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.

Funding

the National Natural Science Foundation of China 52108247
6 · The paper itself

Abstract

This study employed response surface methodology (RSM) to optimize admixture proportions in 3D-printed cementitious materials, with the aim of enhancing printability. Based on preliminary tests, three additives, namely, an accelerator, hydroxypropyl methylcellulose (HPMC), and polycarboxylate superplasticizer (PCE), were incorporated to evaluate their effects on flowability and dynamic yield stress. A Box-Behnken central composite design was used to establish a mathematical model, followed by the RSM-driven optimization of mix proportions. The optimized formulation (0.32% accelerator, 0.24% HPMC, and 0.23% PCE) achieved a flowability of 147.5 mm and a dynamic yield stress of 711 Pa, which closely matched the predicted values and fulfilled the printability requirements, thus establishing RSM as an effective approach for designing printable cementitious composites. This approach established an RSM-based optimization framework for mix proportion design. These findings offer a mechanistic framework for rational 3DPC mixture design, combining theoretical insights and practical implementation in additive construction.

Indexed as

3D-printed cementitious materialsadmixturesflowabilitymixture design approachrheological properties

Identifiers

PMID40942356
PMCPMC12429625

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.