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Record W2085125539 · doi:10.1049/ip-nbt:20060012

Absorption detection of enzymatic reaction using optical microfluidics based intermittent flow microreactor system

2006· article· en· W2085125539 on OpenAlexafffund
Arvind Chandrasekaran, Muthukumaran Packirisamy

Bibliographic record

VenueIEE Proceedings - Nanobiotechnology · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMicroreactorMicrofluidicsBiosensorNanotechnologyMaterials scienceAbsorption (acoustics)Lab-on-a-chipHorseradish peroxidasePhotonicsChemistryOptoelectronicsCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The advantages of integrating microfluidics into photonics-based biosensing for fabricating microreactor type lab-on-a-chip devices carries a lot of advantages, such as smaller sample volume handling, controlled drug delivery and high throughput diagnosis, which is useful for in situ medical diagnosis and point-of-care (POC) testing. A hybrid integrated optical microfluidic system has been developed for the study of single molecules and enzymatic reactions. The method of optical absorption has been employed for biosensing and the feasibility of absorption-based detection on the microfluidic platform has been demonstrated using horseradish peroxidase and hydrogen peroxide, as an example. The results show that the device is useful for the analysis of both the individual chemical specimen and also the study of chemical and biological reaction between two reacting species. The hybrid integration of microfluidics and optical ensembles thus forms the basis for developing the microreactor type lab-on-a-chip device, which would have several important applications in the area of nanobiotechnology.

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 categoriesMeta-epidemiology (narrow)
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 score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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.

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

Citations10
Published2006
Admission routes2
Has abstractyes

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