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Record W34325822 · doi:10.1016/j.jsmc.2021.05.004

A novel method to fabricate open-cell silicon nitride foams with a high and controlled level of porosity

2014· dissertation· en· W34325822 on OpenAlexfundno aff
Ali Alem

Bibliographic record

VenueSleep Medicine Clinics · 2014
Typedissertation
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeNordForsk
KeywordsMaterials scienceFabricationPorositySilicon nitrideNitridingCastingComposite materialNitrideMonomerSiliconChemical engineeringPolymerMetallurgyLayer (electronics)

Abstract

fetched live from OpenAlex

There is a significant lack of study on silicon nitride (Si3N4) foams. This is due to the fabrication issues and difficulties of working with silicon nitride powder. In this study a new fabrication procedure has been designed to fabricate highly porous and homogeneous silicon nitride foams with open-cell structures and controlled porosity levels. The combination of three methods including the sacrificial template method, gel-casting, and reaction bonding techniques resulted in the fabrication of reaction bonded silicon nitride (RBSN) foams. The fabrication procedure was studied and optimized in terms of suspension preparation and rheology, gel-casting parameters, and also reaction bonding conditions. The results revealed that pH 8.5 and the presence of 1.5 wt% DS001 would lead to the highest suspension stability. Therefore, the least sediment height and the highest zeta potential would be obtained. Si-PMMA suspensions showed a near Newtonian behavior at pH 8.5 and for 60 wt% solid. The gel-casting parameters including the monomer content, the ratio of monomer to cross-linker, the gelation time, and the sample warpage were also studied. Based on the result the optimum monomer to crass-linker weight ratio was selected to be 15:1. After nitridation, the foams have a precisely controlled level of porosity, which can be controlled between 41 vol% to 87 vol%. The pore interconnectivity was examined both before and after nitriding and showed complete interconnectedness in the foam porosity. \nThe parameters influencing the mechanical strength of the RBSN foams were also investigated. These considerations include the foam porosity, homogeneity, gel-casting parameters, and nitriding conditions. Depending on the foam porosity, the strength can vary between 1 MPa and 18 MPa. It was also observed that for high Si/PMMA ratios, a monomer content of more than 25 wt% in the premix solution is required. Otherwise, the foam strength drops significantly due to inhomogeneities formed in the cast body. In terms of the effect of nitriding conditions on the foam strength, maximum strength was obtained under N2-H2 atmospheres rather than N2. Extensive investigation on the nitridation process also revealed that the high porosity level of the foam and consequently its large surface area significantly affect the nitriding mechanisms and microstructures compared to conventional RBSN ceramics. It was observed that α- and -Si3N4 form based on specific reactions and each phase has distinct morphologies depending on the nitriding reactions. \nThe effect of iron disilicide (FeSi2) on the properties and microstructure of the fabricated RBSN foams was studied in the next step of the investigation. It was observed that the addition of 1 wt% of FeSi2 significantly increases the foam strength regardless of the nitriding condition. After the addition of FeSi2, a maximum strength of 3.41 MPa was achieved under a N2 atmosphere at 1390°C with a foam of 71 vol% porosity. FeSi2 also affects the α/ phase ratio considerably. It was observed that a considerable increase in the α-Si3N4 content occurs up to 1 wt% FeSi2 while the β-Si3N4 content starts to increase thereafter. The XRD and microstructural analysis showed that α-Si3N4 is present in the form of both matte and whiskers while β-Si3N4 forms as whiskers and large faceted angular grains. \nThe influence of α- and -Si3N4 seeds has also been investigated. It was observed that both α- and -seeds improve the foam strength by 30% and 85%, respectively. The optimum contents of the α- and -seeds correspond to the maximum foam strength which was observed for 5 wt% α-Si3N4 and 10 wt% -Si3N4 seeds. The vapor phase reactions were also enhanced by the addition of -seeds resulting in a significant increase in the -whisker content of the microstructure. \nSintered reaction bonded silicon nitride (SRBSN) foams were also fabricated via sintering of RBSN foams in the presence of MgO as a sintering aid. The effect of different amounts of MgO on the foam microstructure and porosity was also studied both before and after sintering. The addition of MgO resulted in significant changes to the microstructure of the RBSN and SRBSN foams. MgO stopped whisker-forming reactions during nitriding; therefore, foams with clean porosity and without the presence of whiskers were produced. The SRBSN foams can have a maximum of 85 vol% porosity and the foam microstructure contained only -Si3N4 grains embedded in an amorphous intergranular phase.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.356
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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