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Record W2021918387 · doi:10.1063/1.3574222

A layered microchip conductance detector with through-layer access to detection fields and high sensitivity to dielectric constant

2011· article· en· W2021918387 on OpenAlexaff
Yoshinori Suganuma, Al-Amin Dhirani

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDielectricMaterials scienceElectrodeOptoelectronicsDetectorInsulator (electricity)OpticsPhysics

Abstract

fetched live from OpenAlex

The present study explores a novel apertured microchip conductance detector (AMCD) that is sensitive to dielectric constant. Fashioned on silicon oxide/silicon using optical microlithography, the detector has novel parallel-plate geometry with a top mesh electrode, a middle apertured insulator, and a bottom conducting electrode. This monolithic apertured architecture is planar and may be provided with a thin insulator layer enabling large capacitances, while the top mesh electrode and middle apertured-insulator enable access to regions of the capacitor where electric fields are strong. Hence, the detector is sensitive yet mechanically robust. To test its response, the AMCD was immersed in various solvents, namely water, methanol, acetonitrile, and hexanes. Its response was found to vary in proportion to the solvents' respective dielectric constants. The AMCD was also able to distinguish quantitatively the presence of various molecules in solution, including molecules with chromophores [such as acetylsalicylic acid (ASA)] in methanol and those without chrompohores [such as polyethylene glycol 200 Daltons (PEG200)] in methanol or water. The universal nature of dielectric constant and the microchip detector's sensitivity point to a wide range of potential applications.

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 score0.020
Threshold uncertainty score0.528

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 integrity0.0000.000
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 head0.040
GPT teacher head0.267
Teacher spread0.227 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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