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Record W1480282250 · doi:10.1002/9781118310212.ch3

Optofluidic Techniques for the Manipulation of Micro Particles: Principles and Applications to Bioanalyses

2012· other· en· W1480282250 on OpenAlexaff
Honglei Guo, Gaozhi Xiao, Jianping Yao

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsNational Research Council CanadaInstitute for Microstructural SciencesUniversity of Ottawa
Fundersnot available
KeywordsLens (geology)PolydimethylsiloxaneChipMaterials scienceOptofluidicsLab-on-a-chipNanotechnologyOptoelectronicsMicrofluidicsOpticsComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

This chapter classifies optofluidic techniques into three categories, (i) fiber-based optofluidic technique; (ii) near-field optofluidic technique; and (iii) axial-type and cross-type optical chromatography. The fundamental mechanism and recent development of these optofluidic techniques are reviewed. Then, the chapter presents a novel SU-8/polydimethylsiloxane (PDMS) optofluidic chip in which an on-chip lens structure is introduced to enhance the performance of microparticle manipulation. Finally, applications of optofluidic techniques to bioanalyses are discussed. Both theoretical and experimental results have shown that the optical manipulation performance could be enhanced by the proposed on-chip lens structures, because the light beam waist radius was reduced by these lens structures. Controlled Vocabulary Terms chromatography; OBIC; symbol manipulation

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.044
GPT teacher head0.272
Teacher spread0.229 · 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
GenreMethods

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

Citations0
Published2012
Admission routes1
Has abstractyes

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