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Record W2087151930 · doi:10.1063/1.3265993

Transfer-arm evaporator cell for rapid loading and deposition of organic thin films

2009· article· en· W2087151930 on OpenAlexaff
Mark Greiner, Michael G. Helander, Z. B. Wang, Zheng‐Hong Lu

Bibliographic record

VenueReview of Scientific Instruments · 2009
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSublimation (psychology)Materials scienceThin filmCharacterization (materials science)EvaporatorDeposition (geology)OptoelectronicsIn situNanotechnologyMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

Described herein is a transfer-arm evaporator cell (TAE-cell), which allows for rapid loading of materials into vacuum for low-temperature sublimation deposition of thin films. This design can be incorporated with an existing analysis system for convenient in situ thin film characterization. This evaporator is especially well suited for photoemission characterization of organic semiconductor interfaces. Photoemission is one of the most important techniques for characterizing such, however, it generally requires in situ sample preparation. The ease with which materials can be loaded and evaporated with this design increases the throughput of in situ photoemission characterization, and broadens the research scope of the technique. Here, we describe the design, operation, and performance of the TAE-cell.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.262
Teacher spread0.249 · 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
GenreMethods

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

Citations14
Published2009
Admission routes1
Has abstractyes

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