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Record W4285399375 · doi:10.1149/ma2022-01184mtgabs

Examining Sulfur Nucleation and Growth on Carbon Nanomaterials from Aqueous, Elemental Sulfur Sols for Lithium−Sulfur Batteries

2022· article· en· W4285399375 on OpenAlexaff
Gillian F. Hawes, Christian Punckt, Michael A. Pope

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPolysulfideSulfurCarbon fibersLithium (medication)Materials scienceCathodeLithium–sulfur batteryElectrolyteChemistryChemical engineeringInorganic chemistryMetallurgyElectrodeComposite material

Abstract

fetched live from OpenAlex

With decades of engineering, lithium-ion batteries are beginning to reach their fundamental energy density limits. As such, next generation chemistries that can store significantly more energy are essential for electrification of vehicles, improved portable electronics, and increased adoption of renewable energy sources. Lithium-sulfur batteries have attracted significant research attention due to their theoretical full cell capacity (1167 mAh/g) and gravimetric energy density (2500 Wh/kg) which are both an order of magnitude higher than conventional lithium-ion batteries. Furthermore, lithium-sulfur batteries employ elemental sulfur as the cathode material, which is both widely abundant and inexpensive, and also a common waste product of the petroleum industry, making the projected cost of lithium-sulfur cells per kWh much lower than conventional lithium-ion cells that rely on costly lithium transition metal oxide-based cathodes. However, a number of challenges plague the lithium-sulfur chemistry, one of the most critical being that elemental sulfur and its final discharge product, Li2S, are insulating in nature, requiring sulfur to be intimately mixed with a conductive, lightweight carbon material in order to facilitate electron transport. Because sulfur is typically redistributed throughout the cathode during cycling due to dissolution of intermediate polysulfide species in the electrolyte, the uniform coating of sulfur onto carbon materials has not garnered significant research attention. However, uniform distribution of the sulfur within the cathode is critical for several reasons. Large, insulating sulfur particles are not electrochemically converted during cycling, limiting cell capacity, and as sulfur undergoes an 80% volume change during cycling, non-uniform sulfur distribution can result in mechanical failure of the cathode. Moreover, as the field moves toward all-solid-state batteries, in which sulfur redistribution does not occur, the initial sulfur distribution within the cathode is critical for high performance cells. Nanoscale mixing of sulfur and carbon additives is typically achieved via melt imbibition, a lengthy, high-temperature process in which sulfur is melted and then used to coat solid carbon powders. This process typically employs carbon nanomaterials as aggregated solid powders, thereby limiting the accessible surface area that can be coated by sulfur. To access the exceptional surface area of carbon nanomaterials such as graphene (~2630 m2/g), the sulfur coating process needs to occur while the carbon nanomaterials are dispersed in solvent. While this has been demonstrated previously, it is typically done with highly toxic solvents such as CS2 due to sulfur’s limited solubility, or by using more costly and inefficient sulfur precursors, known as hydrophilic sulfur sols, to form elemental sulfur in situ. We demonstrate, for the first time, the use of aqueous, hydrophobic sulfur sols to coat carbon nanomaterials in solution at room temperature. Hydrophobic sulfur sols are sub-micron, metastable sulfur particles which are formed via the rapid dilution of an organic solution of sulfur into large quantities of water. We demonstrate that due to the metastable nature of these hydrophobic sulfur sols in the aqueous system, sulfur dissolves out of the sols and can uniformly coat the surface of dispersed carbon nanomaterials such as reduced graphene oxide (rGO) via a heterogeneous nucleation and growth process. This process is simple, scalable, employs inexpensive elemental sulfur directly, and can be performed at room temperature with an aqueous system, making it an attractive method to prepare sulfur cathodes. We study how the sulfur deposition process is affected by the introduction of a surfactant into the aqueous phase and the use of different organic solvents to prepare the sol, and further demonstrate that heterogeneous sulfur nucleation occurs preferentially with rGO, while a competing, undesirable homogeneous nucleation pathway that forms large insulating sulfur crystals is observed for Ketjen black and graphene oxide. We demonstrate that via this approach, high loading (3-4 mgsulfur/cm2) rGO/sulfur cathodes can be prepared that achieve capacities of 1300 mAh/g (~4.8 mAh/cm2) at 0.1C, and capacities 7-fold higher than cells prepared via traditional melt imbibition approaches at higher C rates of 0.8C and 1C. Moreover, we demonstrate that these cells can be prepared without additional conductive additives or binder and can achieve projected energy densities of ~468 Wh/kg at 0.1C when considering all inactive components and no lithium degradation, indicating the promise of this simple, novel approach for high energy density sulfur cathodes.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.215
Teacher spread0.198 · 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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Citations1
Published2022
Admission routes1
Has abstractyes

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