Development of a body of knowledge for design for disassembly and recycling of high-tech products: a case study on lithium-ion batteries
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
Demand for electronic products is growing, as is the volume of waste electrical and electronic equipment (WEEE). To reduce their environmental impact, particularly during their end-of-life, it is important to apply eco-design practices such as design for disassembly (DFD) and design for recycling (DFR) from the beginning of their development. However, these strategies are not systematically implemented by manufacturers due to several challenges, such as the complexity of the methods, the uncertainty and variability of the materials and components, and the lack of knowledge on DFD and DFR. This study aims to develop a body of knowledge (BOK) for DFD and DFR of electronic products to fill this gap. A systematic comparison of different BOKs has led to the proposal of a BOK composed of four main parts: Areas of Knowledge, Tools and Techniques, Skills and Abilities, and Terminology. The proposed framework was applied to lithium-ion batteries (LIBs) as an example of electronic products that require high-tech solutions for their efficient and sustainable management. This approach is essential for high-tech products, as they often contain valuable and scarce materials that need to be recovered and reused in a circular economy. The results showed that the BOK was an effective tool in supporting the sustainable development of batteries.
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 | 0.002 | 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