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Record W2557537941 · doi:10.1002/admi.201600780

Electron Beam Lithography on Irregular Surface Using Grafted PMMA Monolayer as Resist

2016· article· en· W2557537941 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.

Bibliographic record

VenueAdvanced Materials Interfaces · 2016
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResistMaterials scienceNanolithographyMonolayerElectron-beam lithographyPhotoresistLithographyRaman spectroscopyCoatingNanotechnologySpin coatingLayer (electronics)OptoelectronicsOpticsFabrication

Abstract

fetched live from OpenAlex

An electron beam resist is usually coated by conventional coating methods such as spin‐coating, which cannot be reliably applied on irregular surfaces. Here, it is demonstrated that a monolayer resist can be grafted on nonflat surface to enable nanofabrication on it. As a proof‐of‐concept of patterning on irregular surfaces, poly(methyl methacrylate) (contains 1.6% methacrylic acid that has the carboxyl group needed for grafting) is chosen and is grafted on irregular surfaces by thermal treatment which induces a chemical reaction of the carboxyl group with the hydroxyl group on substrate. Subsequently, nanostructures are patterned by electron beam lithography on this monolayer resist grafted on nonflat surface such as atomic force mocroscopy (AFM) cantilevers, and then the patterns are transferred to the layer underneath. A high resolution of 30 nm line width is achieved using this monolayer resist. Nanofabrication on irregular surfaces may have applications in the fields of tip‐enhanced Raman spectroscopy for chemical analysis and lab‐on‐fiber technology for sensor applications.

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)
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 score0.012
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)0.0000.000

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 head0.008
GPT teacher head0.246
Teacher spread0.238 · 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