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Record W2900599023 · doi:10.1002/adem.201800777

Carbon‐Based Nanostructured Film Materials for High‐Intense Laser‐Matter Interaction Experiments

2018· article· en· W2900599023 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

VenueAdvanced Engineering Materials · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsInstitut National de la Recherche Scientifique
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceLaser ablationGraphenePlasmaCarbon fibersLaserCarbon nanotubeLayer (electronics)Polyethylene glycolIrradiationPEG ratioComposite materialNanotechnologyChemical engineeringOpticsComposite number

Abstract

fetched live from OpenAlex

In this paper, a new synthesis of carbon ‐ based nanostructured films for application as plasma facing materials during high‐intense laser‐matter interaction experiments is presented. The materials are produced using a Polyethylene glycol (PEG) matrix, nanostructured with multiwalled carbon nanotubes, or multi‐layer graphene flakes producing a 250 μm thick film. The films are deposited onto typical materials used in harsh environments such as plasma facing materials. These PEG reinforced materials are irradiated by laser‐accelerated particles and by an in‐air plasma, both are techniques that have shown to heavily stress and damage materials used in laser‐matter interaction experiments. The authors observe that the chemical structure of the PEG reinforced materials, when irradiated by both techniques, stays unchanged. However, one can see that the PEG layer adds considerable longevity to the underlying materials, making it more robust against erosion and ablation occurring during its operation.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.007
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)0.0060.000

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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