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Record W2519461441 · doi:10.1149/ma2016-02/5/758

Understanding the Critical Role of Carbon Nanotube Surface Chemistry Towards the Electrochemical Behavior in Li-O<sub>2</sub> Cells

2016· article· en· W2519461441 on OpenAlexaff
Raymond A. Wong, Keiko Waki, Hye Ryung Byon

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsElectrochemistryElectrolyteLithium (medication)ElectrodeCarbon fibersBattery (electricity)Chemical engineeringChemistryCarbon nanotubeCatalysisMaterials scienceNanotechnologyInorganic chemistryComposite materialPhysical chemistryOrganic chemistryThermodynamicsPower (physics)Composite number

Abstract

fetched live from OpenAlex

The aprotic lithium-oxygen (Li-O2) battery stands alone owing to its highest theoretical specific energy (3.5 kWh/kg) of all battery chemistries.1 The electrochemistry of metallic lithium and gaseous oxygen in the negative and positive electrodes respectively gives the ideal reversible reaction (2Li + O2 ↔ Li2O2, E=2.96 V vs Li/Li+). However, translating the promise of Li-O2 batteries into reality has been stifled by the lack of thorough understanding of the Li2O2 formation and decomposition processes. Additional well-documented challenges relating to electrolyte and electrode instability, low round-trip efficiency, poor reversibility and cycleability, and lower-than-theoretical capacity will need to be tackled before practical Li-O2can be realized. Of note, to achieve the high energy densities desired, it is critical that lightweight materials be used for the positive electrode. Carbon, is the natural choice owing to its numerous advantages including abundance, cost, porosity, conductivity, and weight. Moreover, carbon has been found to be sufficiently active with low kinetic overpotentials for the formation and decomposition of Li2O2 the use of ‘catalysts’ may not be warranted.2 However, several studies have shown side product formation including lithium carbonate and lithium carboxylates originating from carbon-based electrodes, prompting researchers to explore non-carbon electrode materials. The use of non-carbon materials has been met with mixed results with McCloskey et al. demonstrating similar to worse reversibility of nanoporous Au and TiC in comparison to carbon-based electrodes.1 Therefore, provided the management and mitigation of issues relating to instability, carbon remains a promising electrode material. However, surprisingly the influence of the surface characteristics of carbon towards the behavior of Li-O2cells has not been thoroughly investigated. Carbon surfaces contain surface functional groups and vary in morphology with respect to edges, defects and degree of graphitization all of which can have influence on the electrochemical behavior. Here, we highlight the critical role of carbon nanotube surface chemistry towards the behavior of lithium-oxygen (Li-O2) cells by systematically modifying the surface morphology and surface functional groups of multi-walled carbon nanotubes (MWNT). MWNTs are ideal for this study due to their ease of surface modification and ability to fabricate binder-free electrodes. Our study utilizes a variety of techniques including scanning electron microscopy (SEM), x-ray diffraction (XRD), rotating ring disk electrode (RRDE), temperature programmed desorption (TPD) and in situ quantitative gas analysis to understand the differences in discharge capacity, recharge overpotential and reversibility (Figure 1). As seen in Figure 1 discharge capacity is dependent on the degree of disorder of the MWNT surfaces which is found to affect the discharge mechanism related to the adsorption affinity of O2 and soluble Li2O2 intermediates with the MWNT surface. Moreover, the recharge process is found to be dependent on the preceding discharge process, where O-functional groups acts as a promoter in the formation of amorphous Li2O2 which is in intimate contact with the MWNT electrode, enabling the low Li2O2 decomposition potential of 3.0-3.2 V. Our findings have broader implications that can be extended to include the general role of carbon towards capacity, recharge overpotential, and reversibility. Figure 1 – (a) Galvanostatic discharge-recharge profiles performed with 0.5M LiClO4 in tetraglyme at current density 50 mA/g of multi-walled carbon nanotubes with defective edges decorated with O-functional groups (Oxidized CNT), defective CNTs with O-functional groups removed through Ar annealing at 900oC (Oxidized CNT-900), as-received carbon nanotubes (pristine CNT), and lastly pristine CNTs annealed at 2800oC (graphitized CNT) (b) capacity normalized profiles showing trend in recharge overpotential (c) Raman (Id/Ig) ratio versus discharge capacity (d-g) Scanning electron micrographs (SEM) of discharge product morphology following discharge to 2 V. Figure 1

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 score0.004

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.236
Teacher spread0.215 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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