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Record W3186935103 · doi:10.1116/6.0001125

Green CVD—Toward a sustainable philosophy for thin film deposition by chemical vapor deposition

2021· article· en· W3186935103 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2021
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsCarleton University
Fundersnot available
KeywordsChemical vapor depositionSustainabilityCombustion chemical vapor depositionNanotechnologyMaterials scienceThin filmSustainable developmentDeposition (geology)Environmental scienceCarbon filmPolitical scienceGeology

Abstract

fetched live from OpenAlex

Thin films of materials are critical components for most areas of sustainable technologies, making thin film techniques, such as chemical vapor deposition (CVD), instrumental for a sustainable future. It is, therefore, of great importance to critically consider the sustainability aspects of CVD processes themselves used to make thin films for sustainable technologies. Here, we point to several common practices in CVD that are not sustainable. From these, we offer a perspective on several principles for a sustainable, “Green CVD” philosophy, which we hope will spur research on how to make CVD more sustainable without affecting the properties of the deposited film. We hope that these principles can be developed by the research community over time and be used to establish research on how to make CVD more sustainable and that a Green CVD philosophy can develop new research directions for both precursor and reactor design to reduce the precursor and energy consumption in CVD processes.

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.

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)0.001
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 score0.022
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.244
Teacher spread0.231 · 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