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Record W4391348032 · doi:10.1007/s11085-023-10218-7

The Effect of Nb, Ta, and Ti on the Oxidation of a New Polycrystalline Ni-Based Superalloy

2024· article· en· W4391348032 on OpenAlexfundno aff
J. W. X. Wo, Mark Hardy, H.J. Stone

Bibliographic record

VenueHigh Temperature Corrosion of Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaRolls-Royce
KeywordsCrystalliteMaterials scienceSuperalloyMetallurgyLayer (electronics)Chemical engineeringAlloyNanotechnology

Abstract

fetched live from OpenAlex

Abstract The effect of variations in Nb, Ta, and Ti concentrations in exchange for Al on the oxidation resistance of a new polycrystalline Ni-based superalloy (C19) was studied in air at 800 °C for up to 1000 h. An external scale of Ti-doped Cr 2 O 3 and a sub-scale of discontinuous Al 2 O 3 intrusions formed on the surface of all the studied alloys. Contrary to other reports, increasing the Nb concentration improved the oxidation performance and may have promoted the formation of a CrTaO 4 layer, thereby reducing oxygen ingress. The addition of Ta also significantly improved oxidation resistance and reduced the depth of the Al 2 O 3 intrusions. Increasing the Ti concentration did not significantly affect the oxidation performance, potentially due to the relatively low Ti concentrations investigated. Several of the studied alloys with modified Ta and Ti concentrations showed regions of continuous Al 2 O 3 scale formation, suggesting that the compositions are in a transition regime between Cr 2 O 3 -forming and Al 2 O 3 -forming behaviour. The findings suggested that part of the Ti content in C19 could potentially be replaced with Nb, Ta and/or other elements to further enhance oxidation resistance and other desirable properties. Overall, the insights gained could serve as a guide to optimise the composition of C19 and similar alloys for enhanced oxidation resistance.

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 score0.006
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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