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Article
Publication date: 5 July 2011

Haili Zhang, Fuzhen Tian, Song Chen, Qinghua Guo, Fen Liu and Donglan Sun

The purpose of this paper is to develop a kind of novel and high‐performance rheological additive, an eco‐friendly composite of anatase nano‐TiO2 particles and xanthan gum (NTX)…

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Abstract

Purpose

The purpose of this paper is to develop a kind of novel and high‐performance rheological additive, an eco‐friendly composite of anatase nano‐TiO2 particles and xanthan gum (NTX), for interior wall coatings.

Design/methodology/approach

NTX was prepared through heating and refluxing the mixture of TiO2 hydrosol and xanthan gum, and five interior wall coating samples were fabricated with different NTX contents. The morphology of nano‐TiO2 hydrosol and NTX were characterised with TEM, and the stability and rheological properties of these coating samples were studied.

Findings

TEM images showed a core‐shell structure of NTX, and that nano‐TiO2 particles in it were encapsulated by xanthan gum, which was anticipated to weaken Van der Waals force among nano‐TiO2 particles thus preventing the aggregation of nano‐particles. All of the five coating samples were found to be non‐Newtonian pseudo‐plastic fluid, and showed excellent stability and thixotropic property.

Research limitations/implications

This paper focused on the preparation, the characterisation of NTX additive and the study of the rheological behaviours of the coating samples with NTX. Some other aspects, such as coating durability, photocatalytic ability and film properties, will be studied in the future.

Practical implications

It was proven that NTX was an effective eco‐friendly rheological additive for interior wall coatings. Consequently, this paper threw light on developing eco‐friendly interior wall coatings.

Originality/value

A kind of novel and effective rheological additive was developed for interior wall coatings in the study reported in the paper. A method was also developed to introduce functional nano‐particles into coating in a good dispersion state.

Details

Pigment & Resin Technology, vol. 40 no. 4
Type: Research Article
ISSN: 0369-9420

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Article
Publication date: 26 December 2024

Haijie Wang, Jianrui Zhang, Bo Li and Fuzhen Xuan

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to…

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Abstract

Purpose

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

Design/methodology/approach

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

Findings

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

Originality/value

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

Details

International Journal of Structural Integrity, vol. 16 no. 1
Type: Research Article
ISSN: 1757-9864

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