Effect of Pyrolysis Temperature on the Performance of 3D-Printed Silicon–Carbon Anodes Derived from Recycled Photovoltaic Silicon
Bibliographic record
Abstract
Silicon has been proposed as a promising alternative to graphite in the anode of lithium-ion batteries as it offers a higher theoretical specific capacity. In this study, 3-D printed silicon–carbon composite anodes were fabricated from crystalline silicon solar photovoltaic cell waste to investigate the effect of pyrolysis temperature on their structural, electrical, and electrochemical properties. The aim was to suppress the formation of electrochemically inactive SiC observed at 1400 °C in previous work, while maintaining high electrical conductivity and stable structure. Printed anodes were pyrolyzed at 800 °C, 1100 °C, and 1200 °C. The results showed that pyrolysis temperature influences both the microstructure and electrochemical performance of the printed anodes. Pyrolysis at 1200 °C produced a highly conductive carbon matrix without formation of electrochemically inactive SiC. XRD analysis confirmed that at 1200 °C, no SiC peaks appeared. The enhanced graphitization observed in Raman spectra at this temperature, coupled with the reduced charge-transfer resistance in electrochemical impedance spectroscopy, indicates that the carbon network achieved high electronic conductivity while preserving the active silicon phase. As a result, the anode had an initial specific capacity of approximately 771 mAh g-1, with the Coulombic efficiency of 99.9%, and a capacity retention of 61% over 120 cycles. The performance achieved here demonstrates an improvement over the earlier SLA-printed PV-silicon anodes pyrolyzed at 1400 °C, which confirms that tuning pyrolysis temperature is a key strategy to obtain the optimal morphology.
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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".