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Record W2147896185 · doi:10.1117/12.906949

Microscopy using water droplets

2012· article· en· W2147896185 on OpenAlexaff
Faqrul A. Chowdhury, Kenneth J. Chau

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceOpticsMicroscopyOptical microscopeMicroscopeEvaporationLight scatteringScatteringScanning electron microscopeComposite material

Abstract

fetched live from OpenAlex

Water droplets are an attractive medium to realize visible-frequency optical elements. The smoothness of a droplet surface mitigates losses due to light scattering, the shape of a water droplet is reconfigurable by either applying pressure or a potential, water is nearly transparent over the visible frequency range, and water is highly abundant. Here, we explore a simple methodology to dispense and shape water droplets for application as the magnifying element in a microscope using either reflection-mode or transmission-mode illumination. A water droplet is created at the end of a syringe and then coated with a thin layer of silicone oil to mitigate evaporation. By applying mechanical pressure to the water droplet using a metal tip, the shape of the droplet is tuned to yield focusing properties amenable for microscopy. Images captured using the microscope demonstrate micron-scale resolution, variable magnification, and imaging quality comparable to that obtained by a conventional, laboratory-grade microscope.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.227
Teacher spread0.217 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicElectrowetting and Microfluidic TechnologiesFrench-language works237,207