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Record W4313823996 · doi:10.18280/ijdne.170620

Spectroscopic Measurement of High Argon Jet Plasma Flow Rate for Methane Hydrate Decomposition

2022· article· en· W4313823996 on OpenAlexvenueno aff
Ismail Rahim, Novriany Amaliyah, Mohammad Ahsan S. Mandra, Shinfuku Nomura

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsArgonPlasmaMethaneAnalytical Chemistry (journal)Volumetric flow rateExcitation temperatureHydrogenChemistryJet (fluid)Plasma cleaningAtomic physicsAtmospheric pressureMaterials scienceEmission spectrumThermodynamicsSpectral linePhysics

Abstract

fetched live from OpenAlex

Methane hydrate is believed to contain a massive amount of potentially extractable hydrogen gas due to methane as the main component. A high-frequency argon jet plasma method has been proposed for decomposing hydrogen content. The excitation temperature of plasma can be directly observed from atomic emission lines. This information is more efficient to characterize the plasma behavior to optimize the decomposition process. In this study, the plasma excitation temperature was determined using spectroscopy and Boltzmann’s plot with a higher argon gas flow rate. An argon gas flow rate varied from 300, 400, 500, 1000, 1500, 2000, 2500, and 3000 mL/min. It flows inside a hollow tube in the counter electrode. A 27.12MHz high-frequency power source of plasma was applied to produce jet plasma at atmospheric pressure. The excitation temperature was observed in the range of 4310K to 5133K. The highest excitation temperature of 5133K was obtained at an argon gas flow rate of 500 mL/min.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.379

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations3
Published2022
Admission routes1
Has abstractyes

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