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Record W4390102940 · doi:10.1021/acsaelm.3c00910

Advanced Applications of Metal–Organic Decomposition Inks in Printed Electronics

2023· article· en· W4390102940 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

VenueACS Applied Electronic Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of OttawaNational Research Council Canada
Fundersnot available
KeywordsPrinted electronicsElectronicsMaterials scienceNanotechnologyInkwellAerospaceElectrical conductorElectrical engineeringEngineeringComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Technology-based industries are constantly seeking materials and methods to fabricate compact, high-performance, and increasingly more complex printed electronic devices, fostering research in advanced conductive inks and processing techniques. Metal–organic decomposition (MOD) inks are a class of conductive inks based on molecular metal precursors that decompose into metallic features once printed, imparting them with unique attributes that distinguish them from particle-based inks. This Spotlight on Applications summarizes recent progress in understanding the chemistry, rheology, and printability of MOD inks that enable them to be printed, processed, and used with material substrates in unconventional ways. Their unique properties and capabilities are being leveraged to advance the field of printed electronics, from their use in 2.5D and 3D surfaces, wearables, and fine line printing and thus broadening the form factors and improving properties of devices used in the medial, automotive and aerospace industries.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 spread0.226 · 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