Consistent elemental leaching profiles from waste smartphones in aquatic systems
Bibliographic record
Abstract
• The release of 51 elements from end-of-life smartphones in river water is consistent. • The leaching of trace elements from newer models is limited because of coating. • Water chemistry, especially dissolved complexing ligands, affects leachability. • The emissions of tin nano- and micro-particles are of particular concern. As the manufacture of electronic devices escalates to meet the demands of an increasingly technology-driven society, the impact of electronic waste (e-waste) on the environment becomes more prominent as a global issue. In particular, end-of-life small electronics (e.g. smartphones, personal computers) that escape e-waste collection and recycling processes frequently become environmentally burdensome. This study assesses the leaching patterns of inorganic elements from smartphones of several generations in natural river waters with different chemistries. We classified 51 elements into seven clusters with consistent behaviours under different testing conditions. Their leaching behaviours were related to their abundance in electronic components and technologies employed on the electronics, such as coatings that temporarily isolate the components from environmental media. Water chemistry, especially the abundance of complexing organic ligands, can also increase elemental mobility and leachability. Additionally, scanning electron microscopy revealed the emission of tin nano- and micro-particles from soldering points. Tin is essential in phasing out lead in soldering technologies. Nevertheless, the emission of tin nanoparticles from modern smartphones is concerning, and further studies are required on their ecotoxicity. In summary, more stringent directives on regulating recycling centers globally are needed to minimize the environmental footprint of electronic waste.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".