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Record W2150883444 · doi:10.1155/2014/190320

Thermodynamic Modeling of Hydrogen Storage Capacity in Mg-Na Alloys

2014· article· en· W2150883444 on OpenAlexafffund
S. Abdessameud, M. Mezbahul-Islam, Mamoun Medraj

Bibliographic record

VenueThe Scientific World JOURNAL · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCALPHADThermodynamicsTernary operationPhase diagramHydrogen storageWork (physics)Ternary numeral systemHydrideMaterials sciencePhase (matter)HydrogenChemistryComputer sciencePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Thermodynamic modeling of the H-Mg-Na system is performed for the first time in this work in order to understand the phase relationships in this system. A new thermodynamic description of the stable NaMgH3 hydride is performed and the thermodynamic models for the H-Mg, Mg-Na, and H-Na systems are reassessed using the modified quasichemical model for the liquid phase. The thermodynamic properties of the ternary system are estimated from the models of the binary systems and the ternary compound using CALPHAD technique. The constructed database is successfully used to reproduce the pressure-composition isotherms for MgH2 + 10 wt.% NaH mixtures. Also, the pressure-temperature equilibrium diagram and reaction paths for the same composition are predicted at different temperatures and pressures. Even though it is proved that H-Mg-Na does not meet the DOE hydrogen storage requirements for onboard applications, the best working temperatures and pressures to benefit from its full catalytic role are given. Also, the present database can be used for thermodynamic assessments of higher order systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.240
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 source (direct Gemma or distilled Codex), 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

Citations16
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueThe Scientific World JOURNALSame topicHydrogen Storage and MaterialsFrench-language works237,207