Revalorization of graphite fines via carbon nanotube integration for sustainable fast-charging Li-ion battery anodes
Bibliographic record
Abstract
In this study, graphite fines—a by-product from the spheroidization of natural graphite—were revalorized into fast-charging anode materials for lithium-ion batteries. A spray-drying method was employed to agglomerate these fines into spherical particles with the addition of 1 wt% carbon nanotubes (CNTs), followed by pitch coating and carbonization at 1100 °C. The resulting GA-CNT@P material exhibited superior rate performance and reduced lithium plating compared to commercial battery-grade natural graphite. Electrochemical tests showed high reversible capacities (∼350 mAh g −1 ), low polarization, and enhanced charge/discharge capabilities, retaining 96 % capacity at 4C discharge and 40 % at 1C charge. Electrochemical impedance spectroscopy revealed reduced solid electrolyte interphase resistance, charge-transfer resistance and Warburg resistance for GA-CNT@P. Differential open-circuit voltage analysis during fast charging indicated a delayed onset of lithium plating and a lower quantity of plated Li compared to the commercial reference. This work demonstrates a scalable, cost-effective strategy to transform graphite waste into high-performance anodes, promoting sustainable battery manufacturing. • Graphite fines revalorized into fast-charging Li-ion battery anodes. • Spray drying yields spherical agglomerates with tailored particle size. • CNT addition and pitch coating enhance rate performance.
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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".