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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.936

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 integrity0.0000.000
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 head0.016
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