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Record W3097953827 · doi:10.1007/s11663-020-01995-6

Thermodynamic Modeling of Ni-C, Co-C, and Ni-Co-C Liquid Alloys Using the Modified Quasichemical Model

2020· article· en· W3097953827 on OpenAlexaff
Junmo Jeon, Sunyong Kwon, Daniel Lindberg, Min‐Kyu Paek

Bibliographic record

VenueMetallurgical and Materials Transactions B · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
FundersBusiness FinlandAalto-YliopistoAcademy of Finland
KeywordsTernary operationThermodynamicsSolubilityGibbs free energyEnthalpyMaterials scienceTernary numeral systemAlloyPhysical chemistryChemistryMetallurgyPhysics

Abstract

fetched live from OpenAlex

Abstract The strong interactions between the metallic elements and C in liquid Ni, Co, and Ni-Co alloys have been thermodynamically analyzed. The liquid solution properties in Ni-C and Co-C systems showed significant asymmetry because of the short-range ordering of C exhibited in the liquid solution. Using the modified quasichemical model in the pair approximation, the Ni-C and Co-C systems were re-optimized to simultaneously reproduce the present experimental results of the C solubility and the reported thermodynamic properties in the liquid phases. In particular, the partial enthalpy data of C in liquid Ni and Co alloys were considered for the first time on the thermodynamic assessment of Ni-C and Co-C liquid solutions. The asymmetric interpolation method was introduced to evaluate the Gibbs free energy in the ternary system based on the binary Gibbs free energies in the Ni-C and Co-C systems. The C solubility data measured in the ternary Ni-Co-C alloy melt over a wide Co concentration range were successfully reproduced without any additional ternary model parameter by considering the short-range ordering of C.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.516
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.245
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations12
Published2020
Admission routes1
Has abstractyes

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