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Record W2265551382 · doi:10.1149/06638.0035ecst

Stability of Prussian Blue Films for Sensing H<sub>2</sub>O<sub>2 </sub>in a PEM-fuel Cell Environment

2015· article· en· W2265551382 on OpenAlexaff
Hamed Akbari Khorami, Nadine Jacobs, Peter Wagner, Alexander Dyck, Peter Wild, Ned Djilali

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Victoria
FundersDeutscher Akademischer AustauschdienstCarl von Ossietzky Universität Oldenburg
KeywordsPrussian blueProton exchange membrane fuel cellHydrogen peroxideMaterials scienceFourier transform infrared spectroscopyElectrodeFuel cellsChemical engineeringMembraneDegradation (telecommunications)Analytical Chemistry (journal)ChemistryElectrochemistryChromatographyElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Developing a hydrogen peroxide (H 2 O 2 ) sensor able to measure small concentrations of H 2 O 2 in-situ is crucial to understanding the degradation mechanisms that take place in the Membrane-Electrode-Assembly of a PEM-fuel cell. Fiber optic sensing probes based on Prussian blue (PB) are promising for this application. The PB film is however required to sustain the harsh environment of PEM-fuel cells. In this work, Prussian blue films have been deposited at different synthesis temperatures, and using different precursors. The samples were immersed and left in a Phosphate-Buffer-Solution (PBS) at pH 2 at 80 °C for 21 hours and thereafter at 90 °C for 3 hours. These PB films were characterized using FTIR to analyze their stability following PBS processing at operating temperature and pH corresponding to an operating PEM-fuel cell. The PB film prepared using the single-source-precursor (SSP) at the temperature of 60 °C is found to be the most stable.

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 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 score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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