Evidence map›Paper›PMID 33806990›Full record

ArticlePolymers2021

Rice Husk Ash/Silicone Rubber-Based Binary Blended Geopolymer Coating Composite: Fire Retardant, Moisture Absorption, Optimize Composition, and Microstructural Analysis.

Mohd Salahuddin Mohd Basri, Tee Hui Yek, Rosnita A Talib, Intan Syafinaz Mohamed Amin Tawakkal, Siti Hasnah Kamarudin, Norkhairunnisa Mazlan, Nurul Ain Maidin, Mohd Hidayat Ab Rahman

Abstract read
In one paragraph

Article in Polymers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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

8 authors.

Mohd Salahuddin Mohd BasriDepartment of Process and Food Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia.ORCID 0000-0001-7946-9351
Tee Hui YekDepartment of Process and Food Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia.
Rosnita A TalibDepartment of Process and Food Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia.ORCID 0000-0003-3848-0700
Intan Syafinaz Mohamed Amin TawakkalDepartment of Process and Food Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia.
Siti Hasnah KamarudinSchool of Industrial Technology, Faculty of Applied Sciences, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia.
Norkhairunnisa MazlanDepartment of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia.ORCID 0000-0003-0144-8360
Nurul Ain MaidinFaculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia.
Mohd Hidayat Ab RahmanFaculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Geopolymer coating using rice husk ash (RHA) as the aluminosilicate source has shown excellent fire retardant properties. However, incorporation of rice husk ash into the geopolymer matrix increased water absorption properties of the polymer composite. As such, silicone rubber (SiR) was introduced to improve the moisture absorption and fire retardant properties of the composite. Additionally, the less efficient one-factor-at-a-time (OFAT) approach was conventionally used in past studies on the RHA-based geopolymer composite. In understanding the optimum value and significant effect of factors on the fire retardant and moisture absorption properties of the binary blended geopolymer coating composite, the use of statistical analysis and regression coefficient model (mathematical model) was considered essential. The objectives of this study are to identify the significant effect of factors on moisture absorption and fire retardant properties, to determine the optimum composition, and to study the microstructure of the rice husk ash/silicone rubber (RHA/SiR)-based binary blended geopolymer coating composite. The RHA/AA and SiR/Ge ratios were chosen as factors, and the response surface methodology (RSM) was employed to design experiments and conduct analyses. Fire retardant and moisture absorption tests were conducted. A scanning electron microscope (SEM) was used to observe the microstructure of geopolymer samples. The RHA/alkaline activator (AA) and SiR/Ge ratios were shown to have a significant effect on the responses (temperature at equilibrium and moisture absorption). The high ratio of RHA/AA and SiR/Ge resulted in a lower temperature at equilibrium (TAE) below 200°C and at moisture absorption below 16%. The optimum formulation for the geopolymer coating composite can be achieved when the RHA/AA ratio, SiR/Ge ratio, and sodium hydroxide concentration are set at 0.85, 0.70, and 14 M, respectively. SEM micrographs of samples with good fire retardant properties showed that the char residue of the geopolymer composite coating, which is a layer of excess silicone rubber, is porous and continuous, thus providing a shielding effect for the layer of geopolymer underneath. The sample with good moisture absorption showed the formation of a thin outer layer of silicone rubber without any cracks. The unreacted SiR formed a thin layer beneath the geopolymer composite matrix providing a good moisture barrier.

Indexed as

coatingfire retardantgeopolymermoisture absorptionresponse surface methodology (RSM)rice husk ashsilicone rubber resinstatistical

Identifiers

PMID33806990
PMCPMC8004628

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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.