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Record W4412524219 · doi:10.31438/trf.hh2024.73

INTEGRATION OF GRAPHENE-POLYMER HETEROSTRUCTURE MEMBRANES INTO A MULTI-USER MEMS FABRICATION PROCESS

2024· article· en· W4412524219 on OpenAlexfundno aff
Katherine Smith, Daniel Morris, Aidan Retallick, Alaaeldin Elhady, Samed Kocer, Matthias Heil, Eihab Abdel‐Rahman, Aravind Vijayaraghavan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaNational Institute for Health and Care ResearchManchester Biomedical Research CentreCMC Microsystems
KeywordsFabricationGrapheneMicroelectromechanical systemsMaterials scienceMembraneHeterojunctionPolymerNanotechnologyProcess (computing)OptoelectronicsComputer scienceComposite materialChemistry

Abstract

fetched live from OpenAlex

Graphene-polymer heterostructure (GPH) membranes are an emerging high-performance option for MEMS.Here we demonstrate for the first time how GPH membranes can be integrated into commercial multiuser MEMS fabrication using the PiezoMUMPs process, to create capacitive pressure sensors and CMUTs.The pressure sensors demonstrate a linear areal sensitivity of 32 Pa -1 mm -2 over a dynamic range of 200 kPa.The CMUTs possess 9.6 MHz resonance frequency and Q factor of 10 under a bias of 2 V, which is ideal for medical and non-destructive testing applications.This is the first demonstration of an industry-standard process for the fabrication of graphene-based MEMS devices.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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