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Record W1965119177 · doi:10.1039/b808820a

Analytical and physical optimization of nanohole-array sensors prepared by modified nanosphere lithography

2008· article· en· W1965119177 on OpenAlex
Marie-Pier Murray-Méthot, Nicola Menegazzo, Jean‐François Masson

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

VenueThe Analyst · 2008
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNanosphere lithographyMaterials scienceRefractive indexMonolayerLithographyPlasmonBiosensorResolution (logic)Deposition (geology)WavelengthSurface plasmonSurface plasmon resonanceOpticsNanotechnologyOptoelectronicsNanoparticleFabricationPhysics

Abstract

fetched live from OpenAlex

The analytical and physical properties are reported for nanohole arrays prepared with glancing angle deposition (GLAD) or plasma treatment of a nanosphere lithography (NSL) mask prior to the deposition of a thin Au film. The nanohole arrays obtained with a 450 nm nanospheres mask are characterized using atomic force microscopy (AFM) to determine the depth and the width of the nanoholes, and the periodicity of the nanohole arrays. The analytical properties are reported in terms of the surface plasmon (SP) excitation wavelength (500 nm to 1000 nm), sensitivity to refractive index (27 nm RIU(-1) to 487 nm RIU(-1)), sensitivity to monolayer formation (shift of the SP band by approx. 1 nm), and refractive index resolution (10(-4) RIU). These simple techniques produce well-ordered nanohole arrays with tunable analytical and physical properties for the development of biosensors.

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 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 score0.452
Threshold uncertainty score0.311

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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