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Record W2567528935 · doi:10.1063/1.4972339

Low-pressure plasma-enhanced behavior of thermionic converters

2016· article· en· W2567528935 on OpenAlexafffund
Amir H. Khoshaman, Alireza Nojeh

Bibliographic record

VenueJournal of Applied Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsThermionic emissionSpace chargeAnodeCathodeElectronPlasmaIonConvertersMaterials scienceAtomic physicsElectrodePhysicsChemistryVoltageNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

High-pressure plasmas have historically been used in thermionic energy converters both to reduce the electrode workfunctions and to mitigate the space-charge effect. The behavior of such devices has been studied extensively, but low-pressure thermionic converters are far less understood. Advances in nanotechnology, such as the possibility to intercalate nanomaterials-based electrodes with alkali metals in order to reduce workfunction, may alleviate the need for high gas pressures; low-pressure devices may thus play a significant role in future if they can address the space-charge problem. Here, we develop the physics of low-pressure thermionic converters by solving the Vlasov-Poisson system of equations self-consistently. We demonstrate that various possibilities arise due to intricate interactions between the spatially varying electron and ion concentrations, leading to phenomena such as plasma oscillations at higher ion fluxes. We show that even a relatively low ion flux density (∼5×10−4 times the flux density of electrons) reduces space-charge significantly and increases the electron current density by a factor of 7. We further extend the model by including electron and ion emission from both the cathode and anode electrodes.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score0.381

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 head0.006
GPT teacher head0.232
Teacher spread0.225 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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