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Record W2337176119 · doi:10.1002/advs.201600044

Prussian Blue MgLi Hybrid Batteries

2016· article· en· W2337176119 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Science · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsPrussian blueMaterials scienceChemical engineeringChemistryElectrodeElectrochemistryEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

The major advantage of Mg batteries relies on their promise of employing an Mg metal negative electrode, which offers much higher energy density compared to graphitic carbon. However, the strong coulombic interaction of Mg 2+ ions with anions leads to their sluggish diffusion in the solid state, which along with a high desolvation energy, hinders the development of positive electrode materials. To circumvent this limitation, Mg metal negative electrodes can be used in hybrid systems by coupling an Li + insertion cathode through a dual salt electrolyte. Two “high voltage” Prussian blue analogues (average 2.3 V vs Mg/Mg 2+ ; 3.0 V vs Li/Li + ) are investigated as cathode materials and the influence of structural water is shown. Their electrochemical profiles, presenting two voltage plateaus, are explained based on the two unique Fe bonding environments. Structural water has a beneficial impact on the cell voltage. Capacities of 125 mAh g −1 are obtained at a current density of 10 mA g −1 (≈C/10), while stable performance up to 300 cycles is demonstrated at 200 mA g −1 (≈2C). The hybrid cell design is a step toward building a safe and high density energy storage system.

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.

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 score0.080
Threshold uncertainty score0.438

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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