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Record W2346969837 · doi:10.1149/ma2016-03/2/541

Chemical Delithiation of Lithium Excess Cathode Materials: Potentiometric Control Using Organic Oxidants

2016· article· en· W2346969837 on OpenAlexaboutno aff
Daniel C. O’Hanlon, Michael J. Murphy, Mahalingam Balasubramanian, Jason R. Croy

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
Fundersnot available
KeywordsElectrochemistryCathodeLithium (medication)Potentiometric titrationBattery (electricity)Chemical reactionChemistryRedoxInorganic chemistryIonStoichiometryMaterials scienceElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Chemical delithiation of lithium-ion battery cathode materials produces new materials analogous to those generated in lithium-ion battery cathodes upon electrochemical cycling. However, the degrees to which chemically delithiated materials resemble their electrochemical counterparts are not well understood. Chemical delithiation enables the production of pure materials, uncontaminated by binders or conductive carbon that may confound spectroscopic studies. Chemical delithiation most often involves NO x + -type chemical oxidants with very high potentials (> 4.6 V vs Li). However, NO x + -type oxidants are non-innocent, requiring large excesses of oxidant that engage in side reactions not necessarily encountered during electrochemical cycling. These issues complicate attempts to tune the resulting material’s potential via stoichiometric control. In contrast, reversible organic oxidants with more moderate oxidation potentials enable potentiometric control of the delithiation reaction. Most of these organic oxidants are stable under standard reaction conditions; titration of the residual oxidant may be used to quantify the amount of oxidant consumed, and therefore the amount of lithium extracted. By tuning the potential of the applied chemical oxidant, the potential of the resulting material may be tuned to achieve the desired chemical state. This study investigates several chemical oxidants of varying oxidation potential and their effects on lithium-ion battery cathode materials that exhibit multi-step oxidations. Half-cells fabricated from these chemically delithiated cathode materials exhibit initial open-circuit voltages and initial discharge capacities that correlate with the measured oxidation potentials of the chemical oxidants. Recently, several cathode materials have been reported that exhibit reversible capacities beyond those attributable to their redox-active transition metals alone; the implication is that partial oxidation of the oxygen sublattice occurs at high potentials.[1-3] Direct observation of the oxygen sublattice using synchrotron-based spectroscopic methods is impeded by the presence of electrolyte or carbon residue. Chemically delithiated samples of these cathode materials lack both electrolyte and carbon, and have been used to examine the oxygen sublattice at multiple potentials. [1] Yabuuchi, Naoaki, et al. "High-capacity electrode materials for rechargeable lithium batteries: Li 3 NbO 4 -based system with cation-disordered rocksalt structure." Proceedings of the National Academy of Sciences , 112 , 7650 (2015). [2] McCalla, Eric, et al. "Visualization of O-O peroxo-like dimers in high-capacity layered oxides for Li-ion batteries." Science , 350 , 1516 (2015). [3] Urban, Alexander, and Gerbrand Ceder. "A disordered rock-salt Li-excess cathode material with high capacity and substantial oxygen redox activity: Li 1.25 Nb 0.25 Mn 0.5 O 2 ." Electrochemistry Communications , 60 , 70 (2015). Acknowledgment Support for this work from the Office of Vehicle Technologies of the U.S. Department of Energy, in particular, David Howell and Peter Faguy, is gratefully acknowledged. 'Sector 20 facilities at the Advanced Photon Source of Argonne National Laboratory, and research at these facilities, are supported by the U.S. DOE, Basic Energy Sciences, and National Sciences and Engineering Research Council of Canada and its founding institutions. The submitted abstract has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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)0.001
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 score0.009
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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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