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Record W2171830057 · doi:10.1109/tia.2004.840983

Wide-Range Two-Dimensional Imaging of NO Density Profiles by LIF Technique in a Corona Radical Shower Reactor

2005· article· en· W2171830057 on OpenAlexaff
Seiji Kanazawa, T. Sumi, Noriyosi Sato, T. Ohkubo, Y. Nomoto, M. Kočík, J. Mizeraczyk, J.S. Chang

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2005
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsMcMaster University
FundersUniversity of Tokyo
KeywordsCorona dischargeNozzleVolumetric flow rateElectrodeCorona (planetary geology)PlasmaRange (aeronautics)Upstream (networking)Analytical Chemistry (journal)ChemistryMaterials scienceMechanicsNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Planar laser-induced fluorescence was employed during the process of NO/sub x/ removal in a corona radical shower system. Using the wide-range imaging (image size: 240 mm in width and 160 mm in height), two-dimensional distributions of ground-state NO could be observed not only in the discharge zone but also both in the downstream and the upstream regions of the reactor. The obtained results showed that the density of NO molecules decreased not only in the plasma region formed by the corona streamers and the downstream region of the reactor but also in the upstream region of the reactor. The effect of the gas injection through the nozzles electrode on the NO profile in the reactor was negligible. The NO removal rate was almost the same for both cases with and without the injection gas once the streamer discharge was produced. In the present reactor at low main gas flow rate, it was considered that electrohydrodynamic flow became to be dominant, and the flow toward the upstream affected the decrease of NO in the upstream region. This fact is important for optimizing the performance of the nonthermal plasma reactor.

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 score0.372
Threshold uncertainty score0.916

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 integrity0.0000.001
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 head0.013
GPT teacher head0.273
Teacher spread0.261 · 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

Citations7
Published2005
Admission routes1
Has abstractyes

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