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Record W4400965308 · doi:10.1016/j.apsadv.2024.100623

Effect of Au nanoparticles on mitigating the negative impacts of humidity on ZnO gas sensors to detect triethylamine at room temperature

2024· article· en· W4400965308 on OpenAlexafffund
Amirhossein Alaghmandfard, Somayeh Fardindoost, Mina Hoorfar

Bibliographic record

VenueApplied Surface Science Advances · 2024
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTriethylamineHumidityNanoparticleMaterials scienceRelative humidityChemical engineeringEnvironmental scienceNanotechnologyChemistryMeteorologyPhysicsEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The impact of humidity on the efficiency of gas sensors has become highlighted in the realm of gas detection. Due to the complex relationship between humidity and gas sensor performance, the development of gas sensors has recently focused on minimizing humidity-related interference. This research aims to address humidity-related challenges in zinc oxide (ZnO) gas sensors designed to detect triethylamine. The ZnO nanostructures (NSs) were synthesized using thermal decomposition methods at varying temperatures (380 °C, 480 °C, and 580 °C) and annealing times (3 h, 7 h, 12 h, and 21 h). X-ray diffraction (XRD) confirmed the formation of a wurtzite hexagonal close-packed structure in ZnO NSs. Scanning electron microscopy (SEM) images provided insights into the morphologies of ZnO NSs at different annealing temperatures, while energy dispersive spectroscopy (EDS) demonstrated the elemental distribution. Subsequently, gold (Au) nanoparticles were uniformly sputtered onto ZnO sensors with thickness variations (0.1 nm, 0.6 nm, 1 nm, 5 nm, and 10 nm). XPS was employed to analyse the elemental composition and oxygen vacancies of the synthesized sensing materials. The effectiveness of 0.6 nm-thick Au nanoparticles in mitigating humidity effects was observed in ZnO sensors synthesized at 380 °C. The results indicated that ZnO sensors coated with 0.6 nm-thick Au nanoparticles exhibited highly stable responses to ethanol and triethylamine at different humidity levels from 50 % to 90 %. Notably, these sensors demonstrated promising selectivity towards triethylamine (with a response of 17.57) compared to various gas targets at room temperature. The sensor exhibited rapid response and recovery times of 9.8 s and 4.4 s, respectively, toward triethylamine with excellent stability in variable humid environments. The sensor maintained a consistent response over 24 days, demonstrating good stability at high humidity.

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 score0.002

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 integrity0.0000.000
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 head0.005
GPT teacher head0.241
Teacher spread0.235 · 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".

Quick stats

Citations18
Published2024
Admission routes2
Has abstractyes

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