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Record W1505345408 · doi:10.1002/9780470291344.ch5

Ceramics in Non-Thermal Plasma Discharges for Hydrogen Generation

2008· book-chapter· en· W1505345408 on OpenAlexaff
R. Vintila, G. Mendoza-Suárez, Janusz A. Koziński, R. A. L. Drew

Bibliographic record

VenueCeramic engineering and science proceedings · 2008
Typebook-chapter
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsMcGill University
Fundersnot available
KeywordsDielectricPlasmaMaterials scienceHydrogenCapacitanceMethaneCeramicPermittivityYield (engineering)Analytical Chemistry (journal)Dielectric barrier dischargeElectrodeChemistryComposite materialOptoelectronicsChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The influence of ceramic specific capacitance (i.e. area, thickness and dielectric permittivity) on plasma intensity, which ultimately is correlated with the charge transferred in microdischarges, was studied in a dielectric barrier discharge reactor (DBD) at ambient conditions. The reactor operates at applied voltages ranging from 1–10kV and applied frequencies of l-8kHz. The natural gas is injected in the plasma zone yielding hydrogen and solid carbon, with no CO or CO2 release. This study showed that the increase in ceramic specific capacitance results in the increase in hydrogen yield and methane conversion rates due to the increase in the number of micro-discharges for higher dielectric constants. For dielectric constants ranging from 9 to 166 the hydrogen yield increased from 0.3% to 1.35%. In addition, the results suggest that CH4 conversion rates of 2 and 6% were obtained for ceramics with dielectric constants of 9 and 166, respectively.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.226
Teacher spread0.211 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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