Sustainable Recovery of Rare Earth Metals from Smartphone Display using Nanoengineered Cellulose
Bibliographic record
Abstract
Abstract Recycling rare earth elements (REEs) from electronic waste has gained significant attention over the last decade. A sustainable, fast, and selective extraction technique for rare earth metals hardly exists despite that. This work shows a selective rare earth metal recovery from a mobile phone display using a carboxylate functionalized cellulose (CFC). The nanoengineered CFC is water‐dispersible and prepared from affordable, readily available cellulose precursor. It is shown that the REEs present in the mobile phone display instantaneously form a precipitate with CFC, which is easily separated by centrifugation. As low as 150 ppm, the total concentration of REEs in the leachate is required to form a precipitate. The total removal capacity of the REEs in the leachate is 252 ± 4 mg per gram of CFC. In addition, the precipitate formation occurs within 10 s, which to our knowledge, is the best‐reported removal time so far. It is observed that when the total concentration of the REEs in the leachate is 150 ppm or above, the removal capacity of CFC is quite efficacious and unperturbed by the presence of other metal ions. Solar electrodeposition method is utilized to recover rare earth metal and their oxide from the precipitate.
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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 | 0.001 |
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".