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Record W2964822232 · doi:10.1109/jsen.2019.2932414

Optofluidic Dissolved Oxygen Sensing With Sensitivity Enhancement Through Multiple Reflections

2019· article· en· W2964822232 on OpenAlexafffund
Eric Mahoney, Huan‐Hsuan Hsu, Fei Du, Bo Xiong, P. Ravi Selvaganapathy, Qiyin Fang

Bibliographic record

VenueIEEE Sensors Journal · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSensitivity (control systems)FluorescenceTotal internal reflectionQuenching (fluorescence)Oxygen sensorOptoelectronicsExcitationOpticsOxygenReflection (computer programming)ChemistryElectronic engineeringComputer science

Abstract

fetched live from OpenAlex

The development of compact and low-cost dissolved oxygen (DO) sensors is essential for the continuous in situ monitoring of environmental water quality and wastewater treatment processes. The optical detection of dynamic and reversible quenching of fluorescent dyes by oxygen has been used for DO sensing. In this paper, we have optimized a multilayer optofluidic device based on the measurement of fluorescence quenching in a Ruthenium-based oxygen sensitive dye by employing total internal reflection (TIR) of the excitation light to achieve sensitivity enhancement for the detection of 0-20-ppm DO in water. The incident angles of light and sensitive layer thickness are optimized experimentally in order to increase the path length of light in the sensitive layer of the device through multiple reflections. A model is developed to demonstrate how light propagates through different layers of the device at varying angles of excitation and to describe the mechanism of fluorescence generation for each of the types of TIR observed. The design principles identified in this paper may be applied to the development and optimization of new multilayered optofluidic sensors by employing TIR for sensitivity enhancement.

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 score0.052
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.264
Teacher spread0.244 · 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

Citations21
Published2019
Admission routes2
Has abstractyes

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