A Hybrid Mineral Battery: Energy Storage and Dissolution Behavior of CuFeS<sub>2</sub> in a Fixed Bed Flow Cell
Bibliographic record
Abstract
Abstract The development of a hybrid system capable of storing energy and the additional benefit of Cu extraction is discussed in this work. A fixed bed flow cell (FBFC) was used in which a composite negative electrode containing CuFeS2 (80 wt %) and carbon black (20 wt %) in graphite felt was separated from a positive (graphite felt) electrode by a proton‐exchange membrane. The anolyte (0.2 m H2SO4) and catholyte (0.5 m Fe2+ in 0.2 m H2SO4 with or without 0.1 m Cu2+) were circulated in the cell. The electrochemical activity of the Fe2+/Fe3+ redox couple over graphite felt significantly improved after the addition of Cu2+ in the catholyte. Ultimately, in the CuFeS2∥Fe2+/Cu2+ (CFeCu) FBFC system, the specific capacity increased continuously to 26.4 mAh g−1 in 500 galvanostatic charge–discharge (GCD) cycles, compared to the CuFeS2∥Fe2+ (CFe) system (13.9 mAh g−1). Interestingly, the specific discharge energy gradually increased to 3.6 Wh kg−1 in 500 GCD cycles for the CFeCu system compared to 3.29 Wh kg−1 for the CFe system in 150 cycles. In addition to energy storage, 10.75 % Cu was also extracted from the mineral, which is an important feature of the CFeCu system as it would allow Cu extraction and recovery through hydrometallurgical methods.
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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".