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Record W2526639726 · doi:10.2109/jcersj2.16097

The hardness of group 14 spinel nitrides revisited

2016· article· en· W2526639726 on OpenAlexafffund
Teak D. Boyko, A. Moewes

Bibliographic record

VenueJournal of the Ceramic Society of Japan · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersWestern Canada Research GridCompute Canada
KeywordsSpinelNitrideMaterials scienceTernary operationBand gapDiamondDensity functional theoryAb initioCrystallographyComputational chemistryMetallurgyNanotechnologyOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

The hypothetical spinel carbon nitride (γ-C3N4) has received a large amount of attention due to its predicted hardness being comparable to that of diamond. The group 14 spinel binary nitrides that have been synthesized are limited so far to: γ-Si3N4, γ-Ge3N4 and γ-Sn3N4. However, there still remains significant interest in γ-C3N4 in the hope that it will eventually be synthesized, but there are no successful reports, thus making the study of γ-C3N4 strictly theoretical. Through an empirical relationship that correlates hardness, crystal structure and the electronic band gap, we examine a series of group 14 spinel nitrides: γ-C3N4 γ-Si3N4, γ-Ge3N4 and γ-Sn3N4, as well as their ternary compounds. The hardness and electronic band gap of these materials are calculated using ab initio density functional theory. These results show that in the case of the solid solutions, γ-(Si,Ge)3N4 and γ-(Ge,Sn)3N4, the tetrahedral site is filled first by the larger cation, Ge and Sn, respectively. Furthermore, the deviation of carbon containing group 14 spinel nitrides from the expected hardness and bandgap trend suggests that γ-Si3N4 may remain the hardest known group 14 spinel nitride. Additionally, an improved method to calculate the hardness using the nitrogen bonding tetrahedron provides more unambiguous results and the trend of the hardness agrees with experimental measurements.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.168
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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 teacher head, 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

Citations6
Published2016
Admission routes2
Has abstractyes

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