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Поглощение электромагнитного излучения аморфным углеродом, модифицированным металлами

2020· article· en· W3098318882 on OpenAlexaff
С.Г. Ястребов, И.Е. Истомин, Mahi R. Singh

Bibliographic record

VenueПисьма в журнал технической физики · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsDiscrete dipole approximationScatteringElectromagnetic radiationAbsorption (acoustics)Carbon nanotubeDipoleMaterials scienceAmorphous solidComputational physicsAnisotropyRadiationPhysicsOpticsCondensed matter physicsMolecular physicsNanotechnologyChemistryQuantum mechanicsCrystallography

Abstract

fetched live from OpenAlex

This article presents a theoretical study of the scattering and absorption of an electromagnetic wave from the gigahertz to the red range for a model of amorphous carbon modified by metals. On the basis of structural studies of this material, a cylindrical anisotropic nanoparticle - a nanotube responsible for its absorbing and antireflection properties - was selected as a candidate. A model of such a particle was developed and the scattering and absorption cross sections of an electromagnetic wave were calculated within the framework of the theory of the discrete dipole approximation. A pair of nanotubes allowed us to explain the contribution of the interaction of immediate neighbors to scattering and absorption. The constructed model explains the effect of absorption of radio-frequency electromagnetic radiation, observed experimentally in amorphous carbon modified with metals.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.236
Teacher spread0.197 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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