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Record W4283581074 · doi:10.11159/ffhmt22.121

Effects of Inlet Flow Rates on Purge Durations in an Atomic Layer Deposition Process

2022· article· en· W4283581074 on OpenAlexvenueno aff
Betelhiem N. Mengesha, Mohammad Reza Shaeri

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2022
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyU.S. Department of EnergyNational Science Foundation
KeywordsPurgeAtomic layer depositionDeposition (geology)InletLayer (electronics)Flow (mathematics)Process (computing)Volumetric flow rateMaterials scienceEnvironmental scienceMechanicsComputer scienceWaste managementGeologyMechanical engineeringEngineeringPhysicsComposite materialOperating system

Abstract

fetched live from OpenAlex

The effects of inlet flow rates on the purge durations in an atomic layer deposition (ALD) process are investigated through the simulation of three-dimensional laminar multicomponent flow in viscous flow reactors. The operating pressure and temperature are 10 torr (1333 Pa) and 300 C, respectively. Purge durations in reactors with inlet located on the top surface of the reactor are compared with those in a base reactor with one inlet at the bottom surface of the reactor. It is found that purge durations are reduced by an increase in the flow rates, but they are independent from the number of inlets if the flow rates are maintained equal among different reactors. One exception is the reactor with one inlet at the center of the top surface of the reactor, which experiences the longest purge durations, most likely due to the axisymmetric gas injection in this reactor. The acquired results will provide a better understanding about designing efficient viscous flow reactors to reduce both purge duration and gas consumption.

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 score0.333
Threshold uncertainty score0.430

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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