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Record W2298663197 · doi:10.1039/c6lc00182c

Acoustic force mapping in a hybrid acoustic-optical micromanipulation device supporting high resolution optical imaging

2016· article· en· W2298663197 on OpenAlexaff
Gregor Thalhammer, Craig McDougall, Michael P. MacDonald, Monika Ritsch‐Marte

Bibliographic record

VenueLab on a Chip · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsInstitute of Cancer Research
FundersEuropean Research CouncilEngineering and Physical Sciences Research CouncilLandes TirolsEuropean Cooperation in Science and TechnologyScottish Funding CouncilUniversity of Dundee
KeywordsPiezoelectricityResolution (logic)Materials scienceAcousticsHigh resolutionOptoacoustic imagingTransducerOptical imagingOptical forceOpticsOptical tweezersComputer sciencePhysicsRemote sensingArtificial intelligenceGeology

Abstract

fetched live from OpenAlex

Many applications in the life-sciences demand non-contact manipulation tools for forceful but nevertheless delicate handling of various types of sample. Moreover, the system should support high-resolution optical imaging. Here we present a hybrid acoustic/optical manipulation system which utilizes a transparent transducer, making it compatible with high-NA imaging in a microfluidic environment. The powerful acoustic trapping within a layered resonator, which is suitable for highly parallel particle handling, is complemented by the flexibility and selectivity of holographic optical tweezers, with the specimens being under high quality optical monitoring at all times. The dual acoustic/optical nature of the system lends itself to optically measure the exact acoustic force map, by means of direct force measurements on an optically trapped particle. For applications with (ultra-)high demand on the precision of the force measurements, the position of the objective used for the high-NA imaging may have significant influence on the acoustic force map in the probe chamber. We have characterized this influence experimentally and the findings were confirmed by model simulations. We show that it is possible to design the chamber and to choose the operating point in such a way as to avoid perturbations due to the objective lens. Moreover, we found that measuring the electrical impedance of the transducer provides an easy indicator for the acoustic resonances.

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 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 score0.282
Threshold uncertainty score0.728

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 head0.014
GPT teacher head0.223
Teacher spread0.208 · 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 teacher head, 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

Citations31
Published2016
Admission routes1
Has abstractyes

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