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Record W3022803788 · doi:10.1002/admt.202000090

Wearable Devices Using Nanoparticle Chains as Universal Building Blocks with Simple Filtration‐Based Fabrication and Quantum Sensing

2020· article· en· W3022803788 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Materials Technologies · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooCanada Foundation for Innovation
KeywordsFabricationNanotechnologyMaterials scienceQuantum tunnellingWearable computerMicrometerNanoparticleOptoelectronicsWearable technologyQuantum dotSIGNAL (programming language)QuantumBlock (permutation group theory)Computer scienceEngineeringEmbedded systemMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Self‐assembled micrometer long gold nanoparticle chains are used as building block to fabricate a range of flexible devices to monitor human physiological signals by an easy filtration method. The chains serve as the base material for all the devices and their interconnects and contact pads as well. The micrometer long chains are an array of nanoparticles with gaps of 1–2 nm between adjacent particles. The gaps serve as quantum tunneling barrier and their modulation is basis of signal sensing in these devices. Deposited on a flexible membrane, the chains monitor temperature, artery pulsation, and electrocardiograms (ECG) signals with ease. This simple method provides an avenue to fabricate low cost integrated wearable devices based on quantum phenomena.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.822

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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