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Record W2105037152 · doi:10.1109/icmens.2003.1222030

Design and fabrication of microchannels for magnetohydrodynamic flow

2004· article· en· W2105037152 on OpenAlexaff
Jian‐Bin Bao, D. Jed Harrison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMicrochannelPolishingElectroplatingFabricationSurface roughnessAnodic bondingOptoelectronicsElectropolishingWaferElectrodeComposite materialLayer (electronics)NanotechnologyElectrolyteChemistry

Abstract

fetched live from OpenAlex

This paper introduces a new microchannel design for magnetohydrodynamic (MHD) flow that maximizes MHD pumping power. An electromagnetic channel (EMC), which has a rectangular cross-section and electrodes that run the full length of the channels, is the key design element. EMC fabrication on Si requires a two-mask process. ICP-RIE was employed to open rectangular grooves, and then an insulation layer and a seed layer for electroplating were sputtered into these grooves. Current densities for electroplating were adjusted to obtain as planar surface as possible. However, a polishing process needed to be developed to achieve the required planarity for subsequent bonding. Profilometry showed that the surface roughness after polishing was less than 200nm. which was adequate for bonding. The conditions for the second ICP-RIE step, which opened a 10/spl mu/m wide channel between the electrodes, were adjusted to protect the electrodes. Finally, HF bonding was employed to bond the wafer to an O211 glass coverplate. Bubble voltage threshold tests indicated 2.2 V could be applied within the device at 960 Hz before electrolysis created gas bubbles. These voltages are high enough to give the required pumping velocities for open tubular liquid chromatography.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2004
Admission routes1
Has abstractyes

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