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Record W2318724973 · doi:10.1021/am300203g

Facile Size-Tunable Fabrication of Functional Tin Dioxide Nanostructures by Multiple Size Reduction Lithography

2012· article· en· W2318724973 on OpenAlexaff
Hyeong‐Ho Park, Wai Lung Law, Xin Zhang, Seon-Yong Hwang, Sang Hyun Jung, Hyun-Beom Shin, Ho Kwan Kang, Hyung‐Ho Park, Ross H. Hill, Chul Ki Ko

Bibliographic record

VenueACS Applied Materials & Interfaces · 2012
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials scienceTin dioxideLithographyFabricationNanotechnologyNanostructureTitanium dioxideTinReduction (mathematics)OptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

A novel ultraviolet (UV)-assisted imprinting procedure that employs photosensitive tin(II) 2-ethylhexanoate is presented for the facile size-tunable fabrication of functional tin dioxide (SnO(2)) nanostructures by varying annealing temperatures. These imprinted SnO(2) nanostructures were also used as new masters for size reduction lithography. SnO(2) lines down to 40 nm wide were obtained from a silicon master with 200 nm wide lines by simply performing size reduction lithography twice. This leads to 80 and 87.5% reduction in the width and height of imprinted lines, respectively. An imprinted pattern annealed at 400 °C demonstrated transmittance greater than 90% over the range of 350-700 nm, which is high enough to make the pattern useful as a transparent SnO(2) mold. This demonstrated approach allows the accessibility to size-tunable molds, eliminating the need for conventional expensive imprinting masters with very fine structures, as well as functional SnO(2) nanostructures, potentially useful in applications where ordered surface nanostructures are required, such as photonic crystals, biological sensors, and model catalysts.

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 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 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 score0.004
Threshold uncertainty score0.972

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)0.0010.000

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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 teacher head, 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

Citations14
Published2012
Admission routes1
Has abstractyes

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