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Record W2581450533 · doi:10.1039/c6ra27847g

ALD preparation of high-k HfO<sub>2</sub> thin films with enhanced energy density and efficient electrostatic energy storage

2017· article· en· W2581450533 on OpenAlexaff
Le Zhang, Ming Liu, Wei Ren, Ziyao Zhou, Guohua Dong, Yijun Zhang, Bin Peng, Xihong Hao, Chenying Wang, Zhuangde Jiang, Weixuan Jing, Zuo‐Guang Ye

Bibliographic record

VenueRSC Advances · 2017
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsSimon Fraser University
FundersNational Key Research and Development Program of ChinaFundamental Research Funds for the Central UniversitiesHigher Education Discipline Innovation ProjectInternational Joint Laboratory for MicroNano Manufacturing and Measurement TechnologiesNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsHigh-κ dielectricMaterials scienceEnergy storageEnergy densityEnergy (signal processing)Thin filmNanotechnologyChemical engineeringOptoelectronicsEngineering physicsPhysicsThermodynamicsDielectricEngineering

Abstract

fetched live from OpenAlex

The energy density and energy efficiency deteriorate slightly from room temperature to 150 °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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 communication0.0000.000
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 head0.004
GPT teacher head0.213
Teacher spread0.209 · 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 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

Citations66
Published2017
Admission routes1
Has abstractyes

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