MétaCan
Menu
Back to cohort
Record W4212850813 · doi:10.1007/s42235-022-00159-3

Bionic MEMS for Touching and Hearing Sensations: Recent Progress, Challenges, and Solutions

2022· article· en· W4212850813 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 Bionic Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicroelectromechanical systemsMicrofabricationSurface micromachiningEngineeringComputer scienceElectronic engineeringMaterials scienceNanotechnologyFabrication

Abstract

fetched live from OpenAlex

This paper reviews the recent progress on bionic microelectromechanical systems (MEMS) used for touching and hearing sensations, focusing on the following three types of devices: MEMS tactile sensors, MEMS directional microphones, and MEMS vector hydrophones. After reviewing the electromechanical coupling principles, design, and performance of these MEMS devices, the authors conclude that it is vital for future research efforts in bionic MEMS to focus more on microfabrication technologies. The development of robust microfabrication flows is the basis to implement hybrid electromechanical coupling principles based on novel functional materials. High-quality polymeric micromachining technologies can also significantly enhance the potential of existing bionic MEMS designs for more practical applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.475

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 head0.039
GPT teacher head0.235
Teacher spread0.196 · 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