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Record W2907823240 · doi:10.1109/icsens.2018.8589951

Microfluidic Cantilever Biosensors

2018· article· nl· W2907823240 on OpenAlexafffund
Thomas Thundat, Faheem Khan, Swathi Chaudhari, Kamalesh Chaudhari, Seokbeom Kim, Jungcul Lee

Bibliographic record

Venuenot available
Typearticle
Languagenl
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Alberta
FundersCanada Research Chairs
KeywordsCantileverMicrofluidicsBiomoleculeNanotechnologyBiosensorMaterials scienceCapillary actionFabricationComposite material

Abstract

fetched live from OpenAlex

Although a microfabricated cantilever with a high quality factor can detect changes in its mass with unprecedented sensitivity, severe damping reduces it mass sensitivity when operated in a liquid medium thus restricting its use as a mass sensor for biomolecular detection. However, fabrication of cantilevers with embedded microfluidic channel allows confining liquids within them and enables their operation even in vacuum conditions for greatly reduced damping. Operating the microfluidic cantilever in vacuum allows mass-based detection of biomolecules in the liquid environment with unprecedented sensitivity. Although biomolecular detection based on mass using microfluidic cantilever is extremely sensitive, it cannot provide any molecular selectivity. However, coupling the cantilever with capillary gel electrophoresis for separation of biomolecules prior to detection provides an ideal technique for biomolecular detection. Making the cantilever bi-material will also allow infrared photothermal spectroscopy of molecules inside the cantilever. Mid-IR spectroscopy together with mass measurements of confined biomolecules in liquids offers molecular recognition in their native state without using any receptors.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.961

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)0.0520.040

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2018
Admission routes2
Has abstractyes

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