Enhancement of syngas production via plastic gasification in low-concentration CO2 by using spent lithium-ion batteries-derived black mass
Bibliographic record
Abstract
This study investigates the thermochemical and co-thermochemical conversion of black mass (BM), derived from spent lithium-ion batteries, with and without polyethylene (PE) under various atmospheric conditions (N 2 , 25 % CO 2 , and 99.999 % CO 2 ) at temperatures ranging from 700 °C to 900 °C. In an inert N 2 environment, gas emissions were minimal, while significant CO production was observed under CO 2 -rich conditions due to the Boudouard and catalytic reactions facilitated by Ni-based components in BM. The addition of PE enhanced the generation of H 2 and CO, particularly under CO 2 environments, through catalytic conversion of pyrolyzed volatiles. Even with low CO 2 concentrations (∼18 mol%), considerable CO 2 -to-CO conversion was achieved. Heating rate and feedstock ratio (BM:PE) notably influenced gas profiles and syngas yield. Reusability tests showed that processed BM retained partial catalytic activity and maintained structural integrity, making it viable for subsequent hydrometallurgical applications. These results suggest the potential of BM as both a catalyst and a valuable resource for CO 2 -assisted syngas production. • Black mass (BM) catalyzes CO 2 -assisted gasification of polyethylene (PE). • Significant syngas production achieved even under low CO 2 concentrations (∼25 %). • Ni-based components in BM promote Boudouard and reverse water-gas shift reactions. • Optimal BM:PE ratio (4:1) yielded 18.6 % CO 2 -to-CO conversion efficiency at 900 °C.
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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) | 0.001 | 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".