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Record W3007427942 · doi:10.1149/1945-7111/ab67a9

Editors’ Choice—Review—Solid-State Electrochemical Carbon Dioxide Sensors: Fundamentals, Materials and Applications

2020· article· en· W3007427942 on OpenAlexafffund
Suresh Mulmi, Venkataraman Thangadurai

Bibliographic record

VenueJournal of The Electrochemical Society · 2020
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Calgary
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsFossil fuelSolid-stateGreenhouse gasNanotechnologyElectrochemistryCarbon dioxideElectrochemical gas sensorMaterials scienceProcess engineeringEnvironmental scienceElectrodeEngineering physicsChemistryWaste managementEngineering

Abstract

fetched live from OpenAlex

The recent series of global catastrophic events (e.g., heatwaves, flooding) have again raised the concerns over the greater impact of climate change. The focus has been concentrated towards reducing CO 2 emissions from fossil fuel based energy systems. It is necessary to build a commercially viable CO 2 sensor with high reliability. The gas-sensing field has shifted from using a cumbersome gas-reference electrode to solid-state electrochemical devices because they can be employed to detect CO 2 in real-time overcoming the limits of widely used IR-based CO 2 sensors. Here, solid electrolytes and mixed conducting semiconductor-based gas sensors for various gaseous species including CO 2 are reviewed. The study on semiconducting metal oxides (SMOs) has been pushed forward as a most viable option for commercializing monolithic all-solid-state electrochemical gas sensors. Among SMOs, the perovskite-type metal oxides are considered as one of the promising structures for next-generation greenhouse gas sensors due to their remarkable thermal and chemical stability. This article also includes the fundamental understanding of essential factors that govern the electrical signals in all-solid-state electrochemical gas sensors.

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)0.000
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 score0.010
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations51
Published2020
Admission routes2
Has abstractyes

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