MétaCan
Menu
Back to cohort
Record W2462707394 · doi:10.1021/jacs.6b04319

Mg(PF<sub>6</sub>)<sub>2</sub>-Based Electrolyte Systems: Understanding Electrolyte–Electrode Interactions for the Development of Mg-Ion Batteries

2016· article· en· W2462707394 on OpenAlexfundno aff
Evan N. Keyzer, Hugh Glass, Zigeng Liu, Paul M. Bayley, Siân E. Dutton, Clare P. Grey, Dominic S. Wright

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilGonville and Caius College, University of CambridgeNatural Sciences and Engineering Research Council of CanadaCambridge Trust
KeywordsChemistryElectrolyteElectrodeIonChemical engineeringInorganic chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Mg(PF6)2-based electrolytes for Mg-ion batteries have not received the same attention as the analogous LiPF6-based electrolytes used in most Li-ion cells owing to the perception that the PF6(-) anion decomposes on and passivates Mg electrodes. No synthesis of the Mg(PF6)2 salt has been reported, nor have its solutions been studied electrochemically. Here, we report the synthesis of the complex Mg(PF6)2(CH3CN)6 and its solution-state electrochemistry. Solutions of Mg(PF6)2(CH3CN)6 in CH3CN and CH3CN/THF mixtures exhibit high conductivities (up to 28 mS·cm(-1)) and electrochemical stability up to at least 4 V vs Mg on Al electrodes. Contrary to established perceptions, Mg electrodes are observed to remain electrochemically active when cycled in the presence of these Mg(PF6)2-based electrolytes, with no fluoride (i.e., MgF2) formed on the Mg surface. Stainless steel electrodes are found to corrode when cycled in the presence of Mg(PF6)2 solutions, but Al electrodes are passivated. The electrolytes have been used in a prototype Mg battery with a Mg anode and Chevrel (Mo3S4)-phase cathode.

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)0.001
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 score0.245
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.246
Teacher spread0.229 · 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

Citations118
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicAdvancements in Battery MaterialsFrench-language works237,207