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Record W3182100255 · doi:10.53063/synsint.2021.1228

Corrosion and mechanical behavior evaluation of in-situ synthesized Cu-TiB2 nanocomposite

2021· article· en· W3182100255 on OpenAlexvenueno aff
Hossein Aghajani, Seyed Ali Naziri Mehrabani, Arvin Taghizadeh Tabrizi, Falih Hussein Saddam

Bibliographic record

VenueSynthesis and Sintering · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceNanocompositeCorrosionDiffractometerDielectric spectroscopyCopperScanning electron microscopePolarization (electrochemistry)Chemical engineeringNanoparticleSinteringMetallurgyGalvanic corrosionComposite materialElectrochemistryNanotechnologyElectrodeChemistry

Abstract

fetched live from OpenAlex

In this paper, the synthesis of the copper matrix nanocomposite and the effect of adding TiB2 nanoparticles on the copper matrix was investigated. Three different amounts of TiB2 nanoparticles 5, 10, and 15 wt% were added and sintering was carried out at 900 oC for 4 hours under argon atmosphere. The phase formation of achieved nanocomposites was studied by X-ray diffractometer and the morphology of the synthesized samples was studied by field emission scanning electron microscopy and atomic force microscopy. The polarization and electrochemical impedance spectroscopy (EIS) at 3.5 wt% NaCl solution at room temperature was were carried out to evaluate the corrosion behavior of synthesized samples. Results show that adding the TiB2 nanoparticles decrease the corrosion resistance by the formation of galvanic couples, but the effect of amounts of porosities on the corrosion resistance is higher. It is revealed that the variation of the surface roughness is in direct relation to the value of polarization current density.

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)0.000
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 score0.047
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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