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Record W2130038130 · doi:10.1111/jace.13112

A Novel Way to Prepare Hollow Sphere Ceramics

2014· article· en· W2130038130 on OpenAlexfundno aff
Fei Qi, Xingxing Xu, Jie Xu, Yali Wang, Jinlong Yang

Bibliographic record

VenueJournal of the American Ceramic Society · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsnot available
FundersUniversity of TorontoChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsSinteringMaterials sciencePorositySlurrySPHERESCeramicComposite materialCompressive strengthFabricationShrinkageParticle sizeChemical engineering

Abstract

fetched live from OpenAlex

Recent developments in the fabrication of hollow spheres have allowed our group to prepare a new type of macroporous ceramics: hollow sphere ceramics ( HSC s). Alumina hollow spheres were first produced by centrifugal spray‐drying of particle‐stabilized foam slurry. The obtained hollow spheres were sintered together to form HSC at high temperatures. The effect of the sintering temperature on the linear shrinkage, porosity and compressive strength of HSC samples was investigated. When the sintering temperature was increased from 1400°C to 1600°C, the samples shrunk increasingly and the porosity decreased from 59% to 42%, which lead to an increase in the strength of the alumina foams from 6.9 (at 1400°C) to 100.0 MPa (at 1550°C). The mechanical strength of the HSC highly depends on the contact area between the hollow spheres, which could be increased by increasing the sintering temperature, decreasing the size of hollow spheres or by slurry infiltration.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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
GenreEmpirical

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".

Quick stats

Citations47
Published2014
Admission routes1
Has abstractyes

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