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Record W1575529076 · doi:10.2172/1028922

Development of an inter-atomic potential for the Pd-H binary system.

2007· report· en· W1575529076 on OpenAlexaff
Jonathan A. Zimmerman, J.J. Hoyt, François Léonard, Joshua Griffin, Xiaowang Zhou

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicCold Fusion and Nuclear Reactions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPalladiumBubbleAlloyPalladium hydrideMaterials scienceMetalMolecular dynamicsThermodynamicsSpinodal decompositionChemical physicsHydridePhase (matter)NanotechnologyChemistryMetallurgyPhysicsComputational chemistryCatalysisMechanics

Abstract

fetched live from OpenAlex

Ongoing research at Sandia National Laboratories has been in the area of developing models and simulation methods that can be used to uncover and illuminate the material defects created during He bubble growth in aging bulk metal tritides. Previous efforts have used molecular dynamics calculations to examine the physical mechanisms by which growing He bubbles in a Pd metal lattice create material defects. However, these efforts focused only on the growth of He bubbles in pure Pd and not on bubble growth in the material of interest, palladium tritide (PdT), or its non-radioactive isotope palladium hydride (PdH). The reason for this is that existing inter-atomic potentials do not adequately describe the thermodynamics of the Pd-H system, which includes a miscibility gap that leads to phase separation of the dilute (alpha) and concentrated (beta) alloys of H in Pd at room temperature. This document will report the results of research to either find or develop inter-atomic potentials for the Pd-H and Pd-T systems, including our efforts to use experimental data and density functional theory calculations to create an inter-atomic potential for this unique metal alloy system.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.271
Teacher spread0.233 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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