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Record W2476119834 · doi:10.1002/9781119211747.ch16

The Effects Of Ni <sub>3</sub> Al Binder Content on The Electrochemical Response of Tic‐ni <sub>3</sub> Al Cermets

2015· other· en· W2476119834 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCeramic engineering and science proceedings · 2015
Typeother
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCermetCorrosionMaterials scienceAlloyMetallurgyPassivationMicrostructureMetalElectrochemistryAluminideNichromeIntermetallicComposite materialElectrodeLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

TiC-based cermet samples were successfully fabricated with varying amounts of nickel aluminide (alloy IC-50) metal binder, ranging from 10 to 40 vol. %, through a simple melt 0infiltration process. Each of the fabricated compositions was then assessed to determine its degree of resistance to corrosion in an aqueous environment (with 3.5 wt. % NaCl addition), using a variety of electrochemical testing methods. The preliminary results indicate that samples with the lowest binder contents exhibit a greater potential to resist corrosion. However, it is also conjectured that the higher metal binder content samples (i.e. 30 and 40 vol. %) display fewer areas of breakdown and passivation/repassivation after corrosion testing, and hence an overall greater resistance to corrosive attack in terms of the overall extent of sample degradation. The influence of corrosion attack on the microstructure/composition of these materials will be discussed, through the use of electron microscopy and inductively coupled plasma optical emission spectroscopy.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.007
GPT teacher head0.196
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