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Record W4309835267 · doi:10.1149/ma2022-0211719mtgabs

Development of Corrosion Resistant and Electrically Conductive Coatings on Metallic Bipolar Plates for Applications in PEMFC

2022· article· en· W4309835267 on OpenAlexaff
Yuan-Yuan Hong, Ken Cadien, Jing‐Li Luo

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceCorrosionTinContact resistanceComposite materialOxideTitanium nitrideMetallurgyLayer (electronics)NitrideChemical engineeringFuel cells

Abstract

fetched live from OpenAlex

Proton exchange membrane fuel cell (PEMFC) is considered as one of the most promising alternative energy devices due to its high efficiency, low pollutants emission and possible application in automobiles. However, the application of PEMFC is still hindered by the high cost of some of its components such as bipolar plates (BPP) which are a key component in PEMFC to facilitate electron transfer, gas flow, heat and water removal. To reduce the cost, increase conductivity and durability, metallic bipolar plates, typically made of stainless steel, are normally used but they could suffer from severe corrosion in the acidic environment of PEMFC; the formation of corrosion products on the metal surface could reduce the through-plane electrical conductivity and increase the interfacial contact resistance (ICR) between the bipolar plates and gas diffusion layer, eventually causing high power loss in the fuel cell stack. Therefore, developing corrosion resistant and electrically conductive bipolar plates is of vital importance for the practical applications of PEMFC. Transition nitride coated stainless steel, such as titanium nitride (TiN) coated 316L SS, is considered as a possible solution to improve the performance metallic bipolar plates. In this work, TiN coated 316L SSs with heat treatment at different temperatures were prepared. The corrosion behaviours of the prepared samples were investigated in the simulated working environments of fuel cell. The heat treated samples exhibit the improved corrosion resistance compared with the pristine TiN coated samples and with an increase of the heat treatment temperature, the corrosion resistance tends to increase due to the formation of oxide and oxynitride on the sample surface. The ICR test results indicate that high temperature (450 ℃) heat treatment has detrimental effect on the electrical conductivity of samples due to the formation of a thick oxide dominated layer, while the samples heat treated at 300 ℃ only form the graded layers with suitable oxide amount which endows the coated specimens with a very low ICR value both before and after the corrosion tests. Moreover, TiN coatings with different amounts of Ta addition were also prepared. After the corrosion tests in the H2SO4 solution with pH=3 at 70 ℃, the results reveal that Ti150Ta30N samples exhibit the highest corrosion resistance compared with the pristine TiN coated samples and the samples with different amount of Ta addition. The steady state current density of Ti150Ta30N samples after long term potentiostatic polarization at 0.6 V (vs Ag/AgCl) is 0.02 μA/cm2 which is much lower than that of TiN coated samples. The Ti150Ta30N samples also presents a good electrical conductivity with low ICR values before and after the corrosion tests. These studies suggest that a suitable amount of oxygen or Ta incorporation into TiN is a feasible strategy to improve the corrosion resistance while maintaining considerable electrical conductivity of TiN samples.

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 head0.013
GPT teacher head0.220
Teacher spread0.206 · 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
Published2022
Admission routes1
Has abstractyes

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