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Record W2896447437 · doi:10.5162/imcs2018/p1nm.20

P1NM.20 - Isoprene detection with Ti-doped ZnO nanoparticles

2018· article· en· W2896447437 on OpenAlexaff
David Klein Cerrejon, Andreas T. Güntner, Nicolay J. Pineau, D. Chie, Frank Krumeich, Sotiris E. Pratsinis

Bibliographic record

VenueProceedings IMCS 2018 · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsIsopreneMaterials scienceDopingPyrolysisNanoparticleRelative humidityResistive touchscreenNanotechnologyChemical engineeringChemistryOptoelectronicsOrganic chemistryCopolymerComposite materialComputer science

Abstract

fetched live from OpenAlex

Exhaled isoprene could enable non-invasive real-time monitoring of cholesterol-lowering therapies.Here, we report an isoprene-selective sensor at high relative humidity (RH).It is made of nanostructured, chemo-resistive Ti-doped ZnO nanoparticles produced by flame spray pyrolysis (FSP) and directly deposited onto sensor substrates forming highly porous films.The constituent particles consist of stable Ti-doped ZnO solid solutions for Ti levels up to 10 mol%.Ti doping strongly enhance the isoprene sensitivity (>15 times higher than pure ZnO) and turn ZnO isoprene-selective, while also improving its thermal stability.In fact, at an optimal Ti content of 2.5 mol%, this sensor shows superior isoprene responses compared to acetone, NH3 and ethanol at 90% RH.Most notably, breath-relevant isoprene concentrations are detected accurately down to 5 ppb.As a result, an inexpensive isoprene detector has been developed that could be easily incorporated into a portable breath analyzer for noninvasive monitoring of metabolic disorders (e.g.cholesterol).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.200
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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