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Record W2137427198 · doi:10.3390/ma8105333

Ultrathin Gas Permeable Oxide Membranes for Chemical Sensing: Nanoporous Ta2O5 Test Study

2015· article· en· W2137427198 on OpenAlexaff
Alexander Luis Imbault, Yue Wang, Peter Kruse, Evgheni Strelcov, Elisabetta Comini, Giorgio Sberveglieri, Andrei Kolmakov

Bibliographic record

VenueMaterials · 2015
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsMcMaster University
FundersDivision of Electrical, Communications and Cyber SystemsNational Science Foundation
KeywordsNanoporousMembraneMaterials scienceOxideChemical engineeringComposite materialNanotechnologyChemistryEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Conductometric gas sensors made of gas permeable metal oxide ultrathin membranes can combine the functions of a selective filter, preconcentrator, and sensing element and thus can be particularly promising for the active sampling of diluted analytes. Here we report a case study of the electron transport and gas sensing properties of such a membrane made of nanoporous Ta₂O₅. These membranes demonstrated a noticeable chemical sensitivity toward ammonia, ethanol, and acetone at high temperatures above 400 °C. Different from traditional thin films, such gas permeable, ultrathin gas sensing elements can be made suspended enabling advanced architectures of ultrasensitive analytical systems operating at high temperatures and in harsh environments.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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.

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

Citations10
Published2015
Admission routes1
Has abstractyes

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