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Record W2338794244 · doi:10.1038/srep24366

Electron work function–a promising guiding parameter for material design

2016· article· en· W2338794244 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsSuncor Energy (Canada)University of Alberta
FundersShell CanadaSuncor Energy Incorporated
KeywordsWork functionWork (physics)ElectronMaterials scienceFunction (biology)Composite materialMetalMetallurgyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Using nickel added X70 steel as a sample material, we demonstrate that electron work function (EWF), which largely reflects the electron behavior of materials, could be used as a guide parameter for material modification or design. Adding Ni having a higher electron work function to X70 steel brings more "free" electrons to the steel, leading to increased overall work function, accompanied with enhanced e(-)-nuclei interactions or higher atomic bond strength. Young's modulus and hardness increase correspondingly. However, the free electron density and work function decrease as the Ni content is continuously increased, accompanied with the formation of a second phase, FeNi3, which is softer with a lower work function. The decrease in the overall work function corresponds to deterioration of the mechanical strength of the steel. It is expected that EWF, a simple but fundamental parameter, may lead to new methodologies or supplementary approaches for metallic materials design or tailoring on a feasible electronic base.

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)0.003
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 score0.238
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.282
Teacher spread0.249 · 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