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Record W2999782459 · doi:10.1039/d0na00010h

A novel application of nanoporous gold to humidity sensing: a framework for a general volatile compound sensor

2020· article· en· W2999782459 on OpenAlex
Timothy Wong

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

VenueNanoscale Advances · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNanoporous metals and alloys
Canadian institutionsUniversity of TorontoToronto Public Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoporousLeverage (statistics)HumidityMaterials scienceNanotechnologyComputer scienceMeteorologyPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Volatile organic compounds (VOC) are ubiquitous in industrial applications creating a pressing desire for novel transduction pathways to build a broad family of new gas sensors. Nanoporous gold (NPG) is a material with a vast range of untapped potential applications; offering a high surface area found generally in nanomaterials, while also being comparatively simple to fabricate. NPG based sensors can also leverage the unique physics of gold at the nanoscale. In this work, we leverage the multiple unique nanoscale phenomena associated with NPG to demonstrate two novel transduction mechanisms to sense humidity, a model compound. Through direct electrical measurements of NPG, we were able to sense changes in the electronic properties of NPG induced by ambient humidity. We propose two novel transduction mechanisms: chemoresistive changes induced by surface adsorption and electrochemical capacitive changes induced by the electric double layer to detect humidity. To our knowledge this is the first reported application of both these mechanisms for sensing any volatile compounds utilizing NPG.

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 score0.067
Threshold uncertainty score0.728

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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