MétaCan
Menu
Back to cohort
Record W4404060327 · doi:10.1002/elan.202400246

VOC Detection with Zinc Oxide Gas Sensors: A Review of Fabrication, Performance, and Emerging Applications

2024· review· en· W4404060327 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.

Bibliographic record

VenueElectroanalysis · 2024
Typereview
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsToronto Metropolitan University
FundersKing Faisal University
KeywordsFabricationZincMaterials scienceNanotechnologyOxideEnvironmental chemistryChemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Energy‐efficient, high‐specificity gas sensors provide practical suitability for stability and response factors. The recognition of ignitable gases (methane (CH 4 ), propane (C 3 H 8 ), and hydrogen (H 2 ) and harmful gases (carbon oxide (CO) and hydrogen sulfide (H 2 S)) in an enclosed and out‐of‐door space are essential to safeguard the human lives and infrastructural spaces. One of the crucial conductive‐type metal oxide semiconductor (MOS) gas sensors yielding wide applications is zinc oxide (ZnO). This study highlights the various types of ZnO gas sensors, their fabrication techniques, and specific vital characterizations. The devices based on MOS are utilized to sense various target gases through redox reactions. The variation in oxide surface with target gas interactions is transduced to a change of sensor conductance. This review also provides insight into integrating ZnO gas sensors with technologies such as materials engineering, the Internet of things and big data. Moreover, this review addresses ZnO gas sensors’ challenges and future directions.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.008
GPT teacher head0.236
Teacher spread0.229 · 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