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Record W3025776222 · doi:10.1149/ma2020-01211260mtgabs

Hydrogen Depolarised Anode: Proof of Concept and Operational Overpotential Determination

2020· article· en· W3025776222 on OpenAlexaffabout
Nicolas Sacré, Régis Chenitz, Manon Faral, Asmae Mokrini, Jean-Yves Huot, Pierre Bouchard, Thomas Bibienne, Nicolas Laroche, Jean-François Magnan, Mickaël Dollé

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNemaska Lithium (Canada)Centre National en Électrochimie et en Technologies EnvironnementalesNational Research Council CanadaUniversité de Montréal
Fundersnot available
KeywordsOverpotentialElectrolyteAnodeLithium (medication)Lithium hydroxideMaterials scienceChemical engineeringInorganic chemistryNanotechnologyChemistryIon exchangeElectrochemistryElectrodeEngineeringIonOrganic chemistry

Abstract

fetched live from OpenAlex

Lithium hydroxide world demand is growing very fast and will drastically increase in the next years. It could become a critical material in the coming years. This lightning growth is mostly due to lithium-ion batteries development. In particular, the proportion always more important of hybrid and electric cars sell worldwide. This consumer change is mainly driven by a better environmental concern. However, the battery production price and its high environmental cost are breaking the market potential. In order to jointly decreasing these two limitations, Nemaska Lithium is developing a non conventional approach to produce battery-grade lithium hydroxide from his spodumene mine located in the north of Quebec. Its electro-membrane process mainly consumes green electricity and avoid the utilization of huge quantity of chemical products. In a constant improvement ambition, Nemaska Lithium decided to study the opportunity to use a Hydrogen Depolarized Anode (HDA) in order to save energy for the direct electro-membrane synthesis of high purity Lithium Hydroxide. This new approach is inspired from proton exchange membrane fuel cells because the anode reaction is similar (H2 → 2H+ + 2e-). This similarity has oriented the general architecture of the electrode. Thus, it was chosen to supply with hydrogen the back of the HDA and to privilege a porous structure. The other side of the HDA is in close contact with the electrolyte composed of lithium sulfate and sulfuric acid (to enhance the conductivity). The cell porosity orients the reactant to the platinum catalytic sites where the reaction itself is taking place. From this place, produced protons must travel through the Nafion and the electrolyte while electrons circulate on the opposite way across the cell porosity. As if global operation is similar between fuel cells and HDA, a certain number of specificities that do not exist in fuel cell can influence the performance of the HDA. To name just a few main examples, the influence of a liquid phase in contact with the anode and the impact of the lithium sulfate on the kinetic reaction. To reach a most complete possible comprehension of this new technology, a R&D equipment has been specifically developed to support the operation of a 50 cm2 cell. This study will demonstrate the ability of an HDA to oxidize efficiently hydrogen without needing an important overpotential. Two approaches will be developed to isolate the anode overpotential induced during its operation. Following that, the influence of different parameters like salt concentration and Nafion humidity will be quantified. By crossing the cell operation data with different ex-situ characterisations (pH or conductivity measurements, microscopy images, etc...), the influence of the different components will be deconvoluted, leading to a complete understanding of the HDA operation. Following this, an optimized composition will be proposed with a very competitive operational overpotential versus standard used anode.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 routes2
Has abstractyes

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