MétaCan
Menu
Back to cohort
Record W3041448687 · doi:10.1149/ma2019-01/44/2088

(Invited) Solid State Electrochemical Gas Sensors: Fundamentals, Materials and Applications

2019· article· en· W3041448687 on OpenAlexaff
Venkataraman Thangadurai, Suresh Mulmi

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPotentiometric titrationAmperometryResistive touchscreenPotentiometric sensorGalvanic cellAnalytical Chemistry (journal)ElectrochemistrySolid-stateMaterials scienceFast ion conductorElectrochemical gas sensorElectrodeChemistryNanotechnologyElectrical engineeringEnvironmental chemistryPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Solid-state electrochemical gas sensor (SSEGS) typically functions on the principles based on galvanic cells i.e. the output signals (e.g., V, I etc.) provides the information on the gas of interest. Monitoring oxygen in automobiles is a good example of solid-state potentiometric gas sensors, where the potential difference (V) helps translate to determine air-to-fuel ratio.1 The generation of potential could be measured in three different configurations:2 (i) directly measuring mobile species (type I), (ii) indirectly measuring immobile components (type II), and (iii) analyzing other species by employing auxiliary solid-phases (type III). Fast Na+ and Li+ ion conducting Na-β”-alumina, NASICON-type and Li6BaLa2Ta2O12 garnet have been continuously investigated as potentiometric sensor.3,4 Although all these types cover wide range of gaseous concentration, the technique struggles on providing high resolution results at lower gas concentrations. On the other hand, amperometric (I) gas sensors are known to operate over a narrow range of gas concentrations with higher resolutions. However, both solid-state potentiometric and amperometric gas sensors face the challenges of not being able to accurately detect the industrially important gases (e.g., CO2, SOx, H2S, CH4) due to the cross-sensitivities and stability issues, particularly at high temperatures. In other words, these challenges demand new materials with better selectivity, sensitivity and stability under aggressive environments (e.g., high temperature, toxic gases). Unlike potentiometric and amperometric sensors, resistive-type (R) sensors cover large number of materials including ion, electron and mixed conducting semiconductor-based materials (e.g., SnO2, TiO2, CuO). The research on resistive-type sensors has been further extended to various p- and n-type semiconductor-based perovskites owing to their remarkable stability at elevated temperatures as well as excellent structural flexibility to accommodate desired dopants.5 Thus, this emphasizes the importance of fundamental research and improvements on materials’ characteristic properties to overcome the persisting challenges with SSEGS. Here, we attempt to discuss the transition from fast-ion conducting ceramics to semiconductor-based mixed conductors highlighting the necessity of SSEGSs for current industrial applications. References Wagner, C., Über den Mechanismus der elektrischen Stromleitung im Nernststift, Naturwissenschaften 1943, 31, 265-268. Weppner, W., Solid-state electrochemical gas sensors, Sens. Actuators 1987, 12, 107-119. Möbius, H.-H., Galvanic solid electrolyte cells for the measurement of CO2 concentrations, J. Solid State Electrochem. 2004, 8, 94-109. Zhu, Y.; Thangadurai, V.; Weppner, W., Garnet-like solid state electrolyte Li6BaLa2Ta2O12 based potentiometric CO2 gas sensor, Sens. Actuators, B 2013, 176, 284-289. Mulmi, S.; Thangadurai, V., Preparation, Structure and CO2 Sensor Studies of BaCa33Nb0.67−xFexO3−δ, J. Electrochem. Soc. 2013, 160, B95-B101.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.046
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0460.041

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 head0.006
GPT teacher head0.205
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicGas Sensing Nanomaterials and SensorsFrench-language works237,207