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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".