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Record W2067073035 · doi:10.1063/1.1290598

Template-grown high-density nanocapacitor arrays

2000· article· en· W2067073035 on OpenAlex
Konstantin B. Shelimov, D. N. Davydov, Martin Moskovits

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.

Bibliographic record

VenueApplied Physics Letters · 2000
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceFabricationTemplateChemical vapor depositionInsulator (electricity)ElectrodeCapacitanceNanotechnologySiliconOptoelectronicsCapacitorLayer (electronics)MetalOxideVoltageMetallurgyElectrical engineering

Abstract

fetched live from OpenAlex

The fabrication and electrical properties of high-density arrays of cylindrical nanoscale capacitors grown in anodic aluminum oxide templates is described. Using chemical vapor deposition, alternating metallic (carbon) and insulating (boron nitride) layers are created within the template pores, thereby forming composite metal/insulator/metal nanotubules. With the metal electrodes evaporated on the two sides of the template, the structure is converted to an array of nanocapacitors connected in parallel. For 50-μm-thick templates, specific capacitances as high as 2.5 μF/cm2 were measured and capacitances as high as 13 μF/cm2 should be attainable by optimizing the insulating layer properties. The fabrication process can be made compatible with the silicon technology and might, therefore, be used to fabricate high-capacitance elements on tightly packed chips. At the same time, the leakage resistance of the arrays fabricated in the preliminary studies reported here is rather low, presumably due to the contamination of the insulating layer.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.001

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 head0.008
GPT teacher head0.176
Teacher spread0.168 · 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