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Record W2018618787 · doi:10.1149/2.024402jes

Challenges and Opportunities for Capillary Based Biofunctionalization of Microcantilever Arrays

2014· article· en· W2018618787 on OpenAlexaff
Naga Siva Kumar Gunda, Sushanta K. Mitra

Bibliographic record

VenueJournal of The Electrochemical Society · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSurface modificationCapillary actionMaterials scienceCantileverNanotechnologyBiomoleculeGroove (engineering)ChipLab-on-a-chipMicrofluidicsComputer scienceChemistryComposite material

Abstract

fetched live from OpenAlex

Microcantilever as sensing platform has been proven to be extremely sensitive for investigating various biochemical events. Despite of its rapid and label-free detection, obtaining molecular finger print for simultaneous detection of biomarkers is limited due to the difficulty in simultaneous functionalization of cantilevers. Though capillary based functionalization has been considered as one of the best method for simultaneous functionalization, it is limited by alignment of different sized capillary arrays. In the present work, we demonstrated the challenges and opportunities available for capillary based functionalization of microcantilever arrays and address the challenges by proposing an innovative capillary functionalization using on-chip V-grooves connected to flexible capillaries. Cantilever array chip comprising 8 cantilevers spaced at 250 μm distance is simultaneously functionalized with different biomolecules by inserting into dimensional–matched V-groove arrays. We demonstrated that a simple on-chip V-groove capillary functionalization is an easy, effective and highly repeatable by performing a standard sandwich immunoassay for detecting various cardiac markers such as myoglobin and troponin T.

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: none
Teacher disagreement score0.772
Threshold uncertainty score0.163

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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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