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Record W1968486186 · doi:10.1021/cm501002b

Thin Film Receiver Materials for Deterministic Assembly by Transfer Printing

2014· article· en· W1968486186 on OpenAlexaff
Tae‐il Kim, Mo Joon Kim, Yei Hwan Jung, Hyejin Jang, Canan Dağdeviren, Hsuan An Pao, Sang June Cho, J. Andrew Carlson, Ki Jun Yu, Abid Ameen, Hyun‐Joong Chung, Sung Hun Jin, Zhenqiang Ma, John A. Rogers

Bibliographic record

VenueChemistry of Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransfer printingMaterials scienceNanostructureNanotechnologyAdhesiveCoatingElectronicsThin filmYield (engineering)Composite materialLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

We present a specially designed materials chemistry that provides ultrathin adhesive layers with persistent tacky surfaces in solid, nonflowable forms for use in transfer printing and related approaches to materials and micro/nanostructure assembly. The material can be photocured after assembly, to yield a robust and highly transparent coating that is also thermally and electrically stable, for applications in electronics, optoelectronics, and other areas of interest.

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)0.001
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.006

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

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