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Record W2899848973 · doi:10.1002/smll.201803342

Patterned Optoelectronic Tweezers: A New Scheme for Selecting, Moving, and Storing Dielectric Particles and Cells

2018· article· en· W2899848973 on OpenAlexafffund
Shuailong Zhang, Nika Shakiba, Yujie Chen, Yanfeng Zhang, Pengfei Tian, Jastaranpreet Singh, M. Dean Chamberlain, Monika Satkauskas, Andrew G. Flood, Nazir P. Kherani, Siyuan Yu, Peter W. Zandstra, Aaron R. Wheeler

Bibliographic record

VenueSmall · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaUniversity of Toronto
FundersUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsMaterials scienceOptical tweezersPhotoconductivityOptoelectronicsElectrodeDielectricTweezersLayer (electronics)NanotechnologyOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

Optical micromanipulation has become popular for a wide range of applications. In this work, a new type of optical micromanipulation platform, patterned optoelectronic tweezers (p-OET), is introduced. In p-OET devices, the photoconductive layer (that is continuous in a conventional OET device) is patterned, forming regions in which the electrode layer is locally exposed. It is demonstrated that micropatterns in the photoconductive layer are useful for repelling unwanted particles/cells, and also for keeping selected particles/cells in place after turning off the light source, minimizing light-induced heating. To clarify the physical mechanism behind these effects, systematic simulations are carried out, which indicate the existence of strong nonuniform electric fields at the boundary of micropatterns. The simulations are consistent with experimental observations, which are explored for a wide variety of geometries and conditions. It is proposed that the new technique may be useful for myriad applications in the rapidly growing area of optical micromanipulation.

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 score0.003

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 communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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

Citations59
Published2018
Admission routes2
Has abstractyes

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