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Record W2523746476 · doi:10.1149/ma2016-02/1/112

Enhancing the Kinetics of Lithium/Sodium Transition Metal Orthosilicate Cathodes through Tuning the Crystallographic Habits

2016· article· en· W2523746476 on OpenAlexaff
Zhengping Ding, Douglas G. Ivey, Weifeng Wei

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOrthosilicateLithium (medication)ElectrochemistryMaterials scienceTransition metalCathodeSodiumChemical engineeringInorganic chemistryNanotechnologyChemistryTetraethyl orthosilicateElectrodePhysical chemistryCatalysisMetallurgy

Abstract

fetched live from OpenAlex

Lithium transition metal orthosilicates, Li 2 MSiO 4 (M=Mn, Fe, Co), have received great attention because of the theoretical possibility to reversibly deintercalate two Li + ions from the silicate structure. However, the silicates still suffer from low electronic conductivity, sluggish lithium ion diffusion and poor structural integrity upon electrochemical cycling. Recently, sodium transition metal orthosilicates, Na 2 MSiO 4 (M=Mn, Fe, Co), have been reported to be used as a cathode material for sodium secondary battery. In this research, lithium/sodium transition metal orthosilicate A 2 MSiO 4 (A=Li or Na; M= Mn, Fe or Co) nanostructures with preferential exposures of various crystallographic planes on the surface were synthesized via a facile and efficient solvothermal process. Detailed microscopic and spectroscopic analyses are performed to get a better understanding of the electrochemical performance-structure correlations in the crystal habit-tuned A 2 MSiO 4 nanostructures. We anticipate that this novel strategy should shed light on the design and development of a wide range of high capacity intercalated cathode materials for lithium/sodium ion batteries.

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 score0.027
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.238
Teacher spread0.222 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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