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Record W2010117839 · doi:10.1021/ie0402923

Abatement of Perfluorocompounds by Tandem Packed-Bed Plasmas for Semiconductor Manufacturing Processes

2005· article· en· W2010117839 on OpenAlexfundno aff
How Ming Lee, Moo Been Chang, Rung Feng Lu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsnot available
FundersMcMaster University
KeywordsSemiconductorDielectricArrhenius equationPacked bedDielectric barrier dischargePelletsTandemPlasmaMaterials scienceAnalytical Chemistry (journal)DecompositionSemiconductor device fabricationResidence time (fluid dynamics)Chemical engineeringChemistryActivation energyPhysical chemistryComposite materialOptoelectronicsChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The technological feasibility and chemical kinetics of carbon tetrafluoride (CF 4 ) decomposition with tandem packed-bed plasmas (TPBPs) are investigated in this study. Relevant parameters including types of packing dielectric materials, applied voltage, discharge gap, and Ar additives are extensively investigated. Results demonstrate that the addition of Ar is beneficial to CF 4 abatement. Operating at a higher voltage results in higher removal efficiencies. The effect of packing dielectric materials on CF 4 abatement is in the order of BaTiO 3 > Al 2 O 3 > glass pellets, being correlative with their dielectric constants. An overall kinetic model is also developed. Experimental results and kinetic analysis indicate that the CF 4 removal efficiency achieved with a TPBP obeys a first-order rate law. The overall removal rate constant ( k, s -1 ) can be expressed in an Arrhenius form, k = 0.1313 P s 0.6887, where P s represents the specific power (W/cm 3 ). The requirement of 90% reduction of perfluorocompounds for a semiconductor manufacturing process can be accomplished by combining five TPBP reactors in series, being operated at a residence time of 3.2 s and a specific power of 1.2 W/cm 3 .

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)0.001
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 score0.096
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.094
GPT teacher head0.355
Teacher spread0.260 · 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

Citations9
Published2005
Admission routes1
Has abstractyes

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