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Record W2886263508 · doi:10.3791/56925

A 3D-printed Chamber for Organic Optoelectronic Device Degradation Testing

2018· article· en· W2886263508 on OpenAlexafffund
Emma Mogus, Benjamin Torres-Kulik, Christopher Gustin, Ayse Turak

Bibliographic record

VenueJournal of Visualized Experiments · 2018
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsQueen's UniversityMcMaster University
FundersMcMaster University
KeywordsMaterials scienceHumidityDegradation (telecommunications)Relative humidityEnvironmental chamberWater vaporPolylactic acidMoistureOptoelectronicsComposite materialComputer sciencePolymerChemistryTelecommunicationsMeteorology

Abstract

fetched live from OpenAlex

In this manuscript, we outline the manufacture of a small, portable, easy-to-use atmospheric chamber for organic and perovskite optoelectronic devices, using 3D-printing. As these types of devices are sensitive to moisture and oxygen, such a chamber can aid researchers in characterizing the electronic and stability properties. The chamber is intended to be used as a temporary, reusable, and stable environment with controlled properties (including humidity, gas introduction, and temperature). It can be used to protect air-sensitive materials or to expose them to contaminants in a controlled way for degradation studies. To characterize the properties of the chamber, we outline a simple procedure to determine the water vapor transmission rate (WVTR) using relative humidity as measured by a standard humidity sensor. This standard operating procedure, using a 50% infill density of polylactic acid (PLA), results in a chamber that can be used for weeks without any significant loss of device properties. The versatility and ease of use of the chamber allows it to be adapted to any characterization condition that requires a compact-controlled atmosphere.

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 score0.012
Threshold uncertainty score0.716

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 head0.040
GPT teacher head0.375
Teacher spread0.335 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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