From failure to function: recycling spent lithium-ion batteries for catalytic applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the large-scale deployment and continuous retirement of lithium-ion batteries (LIBs), the resource utilization of spent LIBs has become a research focus in the field of energy and environmental science. Traditional element recovery strategies contribute to resource conservation and environmental protection but are often constrained by complex procedures, high costs, and low product value, limiting their economic sustainability. Developing high-value regeneration pathways is therefore essential for the sustainable growth of the LIB recycling industry. The cathode materials of LIBs, rich in multivalent transition-metal oxides with abundant oxygen vacancies and redox activity, and the graphite anodes with high conductivity and structural defects, offer promising precursors for catalyst fabrication. Recycling these electrodes into functional materials for electrocatalysis and environmental catalysis provides an effective route for value-added utilization of spent LIBs. This review systematically analyzes the feasibility and recent progress in converting spent LIBs into catalysts, emphasizing their applications in electrocatalysis (OER, ORR, HER), organic pollutant degradation, and multifunctional catalytic systems. The major challenges are summarized, and future research directions are proposed, including the development of green, low-energy synthesis routes, controllable structural and interfacial design, and comprehensive life-cycle and techno-economic assessments. This work aims to provide an integrated understanding and theoretical reference for the high-value recycling of spent LIBs, promoting their deeper integration into green and sustainable development frameworks.
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.
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 it