Graphene/Na Carboxymethyl Cellulose Composite for Li-Ion Batteries Prepared By Microwave Enhanced Liquid Exfoliation
Bibliographic record
Abstract
Graphene and its composites have great potential as anode materials for Li-ion batteries (LIB) since their capacity was reported to exceed that of graphite, currently used as anode in most LIB [1].There are many methods to synthesize graphene, but there are still challenges to produce graphene at a large scale with desired properties that suit its perspective applications. Since there is yet no universal method for graphene production, especially that gives high yield, it would be reasonable to develop methods adjusted to specific applications e.g. electrochemical energy storage. Liquid ultrasonic exfoliation of graphene is very popular technique however satisfactory yield of exfoliation is usually achieved with either toxic organic solvents or in the water solutions of surfactants, the latter results in contaminated final product, which is unacceptable for electrochemical applications. In our work we used sodium carboxymethyl cellulose (NaCMC) as a water soluble “green” exfoliating agent for the high-yield graphene preparation. Since NaCMC is known to be a good binder for active electrode masses there is no need to remove it from as prepared graphene. Such approach allowed us simultaneous synthesis of graphene and preparation of electrode material for LIB. Microwave assisted method for the liquid phase graphite exfoliation was developed in the present work. Addition of microwave heating step to sonication of graphite dispersions in solutions of sodium carboxymethyl cellulose (NaCMC) resulted in formation of concentrated graphene dispersions of up to 4.3 mg/ml after 10 hours of sonication; concentration of graphene was increased for 34% compared to using sonication alone. HRTEM and Raman spectroscopy revealed formation of few-layer graphene (3-4 layers). It was found as well that graphene yield depends on the molecular weight of the NaCMC – the higher molecular weight the higher graphene yield (up to 59% increase of graphene yield was observed for NaCMC with 250000 molecular weight compared to NaCMC with 90000 molecular weight). Drying of prepared dispersions resulted in the graphene/NaCMC composites with graphene content up to 38.65%. Yield and concentration of graphene can further be improved by increasing sonication time and recycling of sediment. Graphene/NaCMC composite was tested as electrochemically active binder for using with Si nanoparticles in LIB electrodes. Si showed up to 51% capacity increase in the presence of graphene/NaCMC binder comparing to conventional NaCMC binder. N. Lavoie et al. in Y. Abu-Lebdeh and I. Davidson (eds) Nanotechnology for Lithium-ion batteries,117 (Springer, NY, 2013). A. Cieselski, P. Samori, Chem. Soc. Rev. 43, 381 (2014).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".