MétaCan
Menu
Back to cohort
Record W2623223676 · doi:10.1021/acssensors.7b00112

An Inexpensive, Single Use Carbon-Based Sensor for the Rapid and Early Detection of Hydrocarbon Leaks

2017· article· en· W2623223676 on OpenAlexafffund
H. Bri Sebastian, Robert M. Mayall, Roberto J. Pilonieta, Steven L. Bryant

Bibliographic record

VenueACS Sensors · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Calgary
FundersCanada Excellence Research Chairs, Government of CanadaAlberta InnovatesAlberta Innovates - Technology FuturesNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsParaffin waxHexaneWaxMaterials scienceHydrocarbonCarbon fibersChemical engineeringSolventNanoparticleEvaporationFabricationCarbon nanotubeElectrical resistivity and conductivityNanotechnologyOrganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

A novel fabrication process for a single use, low-cost organic solvent sensor has been developed. The process is simple, and the materials are readily available. Carbon nanomaterials are self-assembled at a water/hexane interface, where the hexane phase contains dissolved paraffin wax. Upon the controlled evaporation of hexane, the paraffin wax precipitates, trapping the carbon nanoparticles at the surface in a paraffin wax backbone, realizing a carbon-nanoparticle-decorated film. The film is hydrophobic and highly electrically conductive. When exposed to hydrocarbons or a mixture of hydrocarbons, the conductive carbon network deteriorates and an increase in film resistivity is monitored. The rate of change in resistivity is proportional to the concentration and composition of organic molecules in contact with the film.

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)0.001
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 score0.206
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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.

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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueACS SensorsSame topicAnalytical Chemistry and SensorsFrench-language works237,207