MétaCan
Menu
Back to cohort
Record W4220765361 · doi:10.1038/s41427-022-00370-y

Wearable multifunctional soft sensor and contactless 3D scanner using supersonically sprayed silver nanowires, carbon nanotubes, zinc oxide, and PEDOT:PSS

2022· article· en· W4220765361 on OpenAlexaff
Hong Seok Jo, Chan-Woo Park, Seongpil An, Ali Aldalbahi, Mohamed H. El‐Newehy, Simon S. Park, Alexander L. Yarin, Sam S. Yoon

Bibliographic record

VenueNPG Asia Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Calgary
FundersNational Research Foundation of KoreaKing Saud UniversityNational Research Foundation
KeywordsMaterials sciencePEDOT:PSSPolystyrene sulfonateCarbon nanotubeNanotechnologyOptoelectronicsNanowireLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract The multifunctional soft sensor developed here is capable of simultaneously sensing six stimuli, including pressure, bending strain, temperature, proximity, UV light, and humidity, with high accuracy and without interference among the respective built-in components. The sensor is fabricated via a facile, scalable, and cost-effective supersonic cold-spraying method using silver nanowires (AgNWs), carbon nanotubes (CNTs), zinc oxide (ZnO), and conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). A mask and laser cutter are used in conjunction with the supersonic cold-spraying method to produce miniaturized multifunctional sensors that can be readily installed on various substrates; for example, the production of gloves capable of multifunctional sensing. In particular, the proximity sensor of the multifunctional glove sensor can produce a three-dimensional (3D) image of a scanned object, showing high potential for use in military, medical, and industrial 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.

How this classification was reachedexpand

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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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 teacher head, not a consensus.

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

Citations36
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueNPG Asia MaterialsSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207