MétaCan
Menu
Back to cohort
Record W2072855270 · doi:10.1116/1.4862088

Closed-cycle cooling of cryopanels in molecular beam epitaxy

2014· article· en· W2072855270 on OpenAlexafffund
Ryan B. Lewis, Vahid Bahrami-Yekta, Medhaj J. Patel, T. Tiedje, Mostafa Masnadi‐Shirazi

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2014
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMolecular beam epitaxyShroudLiquid nitrogenMaterials scienceEpitaxyDopingAnalytical Chemistry (journal)NitrogenChemistryOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Closed-cycle cooling of the cryoshroud in a molecular beam epitaxy (MBE) system with a dimethyl polysiloxane heat transfer fluid has reduced liquid nitrogen consumption by an order of magnitude, significantly lowering operating costs. The temperature dependence of cryopanel pumping efficacy in the MBE system has been investigated. H2O, CO, CO2, and As4 are all pumped effectively by liquid nitrogen cooled cryopanels (−196 °C) in the MBE. At −78 °C, the operating temperature of the closed-cycle chiller, H2O and As4 are pumped effectively, while CO and CO2 are not. The pumping speed for H2O is found to increase exponentially with decreasing temperature. Below ∼−40 °C and ∼−95 °C, the pumping speeds for As4 and H2O saturate, respectively. AlGaAs layers grown with the closed-cycle-cooled shroud show strong photoluminescence, expected room temperature electron mobility, and background doping levels less than 4 × 1015 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.201
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicThermal Radiation and Cooling TechnologiesFrench-language works237,207