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Record W2326775969 · doi:10.1021/acsami.6b00288

Improving the High-Voltage Li<sub>2</sub>FeMn<sub>3</sub>O<sub>8</sub> Cathode by Chlorine Doping

2016· article· en· W2326775969 on OpenAlexfundno aff
Jiaqi Dai, Lihui Zhou, Xiaogang Han, Marcus Carter, Liangbing Hu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersAdvanced Research Projects AgencyAdvanced Research Projects Agency - EnergyUniversity of CalgaryU.S. Department of Energy
KeywordsCathodeMaterials scienceElectrochemistryDopingX-ray photoelectron spectroscopyHigh voltageOxideVoltageOptoelectronicsAnalytical Chemistry (journal)Chemical engineeringElectrical engineeringElectrodePhysical chemistryChemistryMetallurgy

Abstract

fetched live from OpenAlex

High-capacity and high-voltage cathode materials are desirable for high-energy-density lithium ion batteries. Among various cathode materials, Li2FeMn3O8 is attractive due to its high working voltage, low toxicity, and low cost. However, its superior electrochemical properties are significantly limited by the intrinsic defects in the Li2FeMn3O8 cathode, which makes the theoretical working voltage (4.9 V) and capacity (148 mAh/g) hard to reach. In this paper, we demonstrated that Cl doping can effectively increase the capacity and working voltage of the Li2FeMn3O8 cathode. X-ray photoelectron spectroscopy reveals that Cl doping reduced the valence state and increased the electron binding energy in cations and thus increased the voltage and enhanced the capacity of the Li2FeMn3O8 cathode. Our results also indicate that Cl doping can be a promising low-cost method to improve the electrochemical performance of various oxide cathode materials, including LiCoO2 and LiMn2O4.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.202
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations25
Published2016
Admission routes1
Has abstractyes

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