MétaCan
Menu
← Back to cohort
Record W2267238139 · doi:10.1149/ma2015-01/2/309

Synthesis and Characterization of Li<sub>2</sub>FeSiO<sub>4</sub> As Candidate High-Capacity Li-Ion Battery Cathode Material

2015· article· en· W2267238139 on OpenAlexaff
Huijing Wei, Xia Lu, Hsien‐Chieh Chiu, Zachary Arthur, Ning Chen, Jigang Zhou, Raynald Gauvin, Joel W. Reid, De-Tong Jiang, Pierre Hovington, Abdelbast Guerfi, Karim Zaghib, George P. Demopoulos

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of GuelphHydro-QuébecCanadian Light Source (Canada)McGill University
Fundersnot available
KeywordsMaterials scienceElectrochemistryOrthorhombic crystal systemCathodeX-ray photoelectron spectroscopyRaman spectroscopyChemical engineeringAnnealing (glass)Lithium-ion batteryHydrothermal synthesisHydrothermal circulationNanotechnologyBattery (electricity)ChemistryCrystallographyCrystal structureMetallurgyPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Due to the emerging needs of electrical and hybrid electrical vehicle applications, more research effort has been directed towards the next generation Li-ion storage materials, especially on high-capacity cathode materials. In this context, lithium transition metal silicates Li2 M SiO4 ( M = Fe, Mn, Co, etc.) 1-5 are drawing increasingly more attention thanks to their high theoretical capacity due to the two lithium-ion extraction per formula unit. However lack of phase purity control during synthesis and phase transition reactions during delithiation/lithiation have imposed obstacles to their study and further development. Hereby, we investigate a novel dual step organic-assisted hydrothermal-annealing approach to obtain different lithium iron silicate polymorphs (monoclinic, orthorhombic, and mixed) for subsequent electrochemical evaluation and probing of their phase transition behavior. During the hydrothermal synthesis step, organic additives are used to control nanocrystal growth, which upon annealing lead to in-situ coating with nitrogen-doped carbon. The silicates are characterized by various techniques including XRD, FE-SEM, TEM, XPS, Raman spectroscopy, and XANES and demonstrate different structures and morphologies with various synthesis conditions, including annealing temperature, pH etc., which critically impact on their electrochemical properties. References 1. Nyten, A., Abouimrane, A., Armand, M., Gustafsson, T. & Thomas, J. O. Electrochemical performance of Li2FeSiO4 as a new Li-battery cathode material. Electrochem Commun 7, 156-160, (2005). 2. Nishimura, S. I. et al. Structure of Li2FeSiO4. J Am Chem Soc 130, 13212-13213, (2008). 3.Boulineau, A. et al. Polymorphism and structural defects in Li2FeSiO4. Dalton Trans 39, 6310-6316, (2010). 4. Eames, C., Armstrong, A. R., Bruce, P. G. & Islam, M. S. Insights into Changes in Voltage and Structure of Li2FeSiO4 Polymorphs for Lithium-Ion Batteries. Chem Mater 24, 2155-2161, (2012) 5. R.J. Gummow, Y. He / Journal of Power Sources 253 (2014) 315-331, Recent progress in the development of Li2MnSiO4 cathode materials

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicAdvancements in Battery Materials→French-language works237,207→