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Record W4293248186 · doi:10.1149/1945-7111/ac8cb7

Reduction of Energy Consumption in Lithium Electrolytic Cell by Improving Design and Operating Conditions

2022· article· en· W4293248186 on OpenAlexaff
Juan Manuel Meléndez, Martin Désilets, Gaétan Lantagne

Bibliographic record

VenueJournal of The Electrochemical Society · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsElectrolyteDiaphragm (acoustics)AnodeElectrolysisLithium (medication)Materials scienceEnergy consumptionCathodeElectrolytic cellElectrodeNuclear engineeringChemistryMechanicsElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Molten salt electrolysis is an efficient process to obtain metallic lithium but requires a considerable amount of energy. The use of a grooved diaphragm and rotating electrodes were studied using an advanced numerical model representing an experimental lithium electrolytic cell with the finality to reduce the required energy. Simulations were conducted using a turbulent (k-ε) model to solve the two-phase flow coupled to the transient mass transport inside a 2D axisymmetric electrolysis cell. The model also considers the recombination of Li with chlorine gas (Cl 2 ), a backreaction that is detrimental to efficiency and energy consumption. The vertical diaphragm with grooves produces a reduction of 26.7% in energy consumption in comparison with the ungrooved design but increases by four times the amount of recombined lithium in the process. To decrease that recombination, the grooved diaphragm was inclined toward the anode. A vertical angle of 85° helps to reduce the energy consumption by 23.5% with approximately the same recombined lithium mass when compared to the vertical ungrooved design. The use of a rotating cathode with at an angular velocity of 0.25 rad s −1 results in a 40% decrease in energy consumption in addition to a decrease of 87.4% in metallic Li reconversion, in comparison with non-porous ungrooved diaphragm design.

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 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 score0.053
Threshold uncertainty score0.204

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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