MétaCan
Menu
Back to cohort
Record W3025810149 · doi:10.1149/ma2020-01282155mtgabs

Sub-ppb Sensing Level Hydrogen Sulphide at Room Temperature Using Doped Indium Oxide Gas Sensors

2020· article· en· W3025810149 on OpenAlexaff
Ahmad Al Shboul, Ricardo Izquierdo

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsIndiumHydrogen sulfide sensorMaterials scienceHydrogen sulfideOperating temperatureOxideMetalCopperHeterojunctionHydrogenDopingAnalytical Chemistry (journal)ConductivityInorganic chemistryOptoelectronicsChemistryElectrical engineeringMetallurgy

Abstract

fetched live from OpenAlex

Hydrogen sulfide (H2S) is a highly corrosive, harmful, and toxic gas. Whereas 130 ppb is the threshold for maximum safety exposure level. Thus, it has become mandatory to develop highly sensitive sensors operating at room temperature to detect low levels of H2S. Sensors based on electrochemical measurement principles, semiconducting metal oxides or p–n heterojunctions have showed promising results to detect H2S gas. To date, drawbacks have boon reported due to their extended response times, high operating temperatures, limited selectivity to the gas and/or sensitivity to humidity changes. Efforts have been made to develop sensors based on metal oxides operable at room-temperature for H2S detection. Here, we describe novel flexible H2S gas sensors based on optimized composite mixture of indium oxide (In2O3), copper acetate (CuAc), graphite (Gt) and polystyrene (PS). While we employed Gt in sensor’s structure to reduce sensor’s resistance to ~ 0.5 MΩ, we used PS and enhance sensor’s integrity as well as the adhesiveness of the sensing layer on the top of carbon electrodes. Whereas we applied In2O3 as the active material in sensors due to its promising performance to detect ~ 100 ppb gas concentration. We used CuAc as activator when it converts to the form of copper sulfide (CuS) due to its high conductivity (10 S/cm). Hypothetically, upon exposing sensors to the H2S gas, In2O3 converts spontaneously to the metallic form In2S3 (eq.1). Alternately, CuAc plays two critical roles in the sensor composition. First, CuS formation drops sensor’s resistance significantly due its high conductivity (eq.2). At this point, In2S3 and CuS can form a continues metallic layer, thus it increases sensor’s conductivity. Second, CuS may play a similar role as same as noble metals, which can cause the formation the structure of noble-doped metal oxide and ease oxygen molecules adsorption/ionization on sensor’s surface (eq.3,4). This may facilitate oxidizing and/or adsorption H2S on the sensor’s surface (eq.5,6), which consists of releasing electrons to sensors and reduce sensors resistance dramatically. The developed sensors have showed high selectivity toward H2S gas, high sensitivity to H2S concentration of less than 100 ppb, fast detection time less than 60 seconds, and high resistivity to humidity changes. Figure 1

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 head0.027
GPT teacher head0.214
Teacher spread0.187 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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