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Record W2129526605 · doi:10.1002/cphc.200900688

Magnetically Deformable Liquid Mirrors from Surface Films of Silver Nanoparticles

2010· article· en· W2129526605 on OpenAlexaff
Anna M. Ritcey, E. F. Borra

Bibliographic record

VenueChemPhysChem · 2010
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNanoparticleColloidSilver nanoparticleMaterials scienceNanotechnologyReflectivityFerrofluidSurface (topology)MonolayerOpticsChemistryPhysicsMagnetic fieldPhysical chemistry

Abstract

fetched live from OpenAlex

This article describes the development of a magnetically deformable mirror composed of a reflective monolayer of silver nanoparticles deposited at the surface of a ferrofluid. In order to be commercially competitive, this mirror must surpass existing technologies with respect to optical performance. Several important characteristics of the mirror, including reflectivity, deformability and long term stability, are determined in large part by the constituent chemical components. This article describes recent progress in the optimisation of these components. As outlined below, several important objectives have been met and the mirror has clearly passed the proof-of-concept stage. Challenges, however, remain, particularly with respect to the stability of the surface layer of silver nanoparticles that imparts reflectivity to the mirror. This interdisciplinary project underscores the role that fundamental colloid and surface chemistry can play in the development of a unique optical element.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

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