Development of a Green Technology for Mercury Recycling from Spent Compact Fluorescent Lamps Using Iron Oxides Nanoparticles and Electrochemistry
Bibliographic record
Abstract
The widespread use of energy efficient mercury containing lamps and impending regulations on the control of mercury emissions has necessitated the development of green mercury control technologies such as nanosorbent capture and electrolysis regeneration. Herein we describe a two-step green technique to remove and recycle mercury from spent compact fluorescent lamps (CFLs). The first element included the assessment of capture efficiencies of mercury vapor on magnetite (Fe 3 O 4 ) and maghemite (γ-Fe 2 O 3 ), naturally abundant and ubiquitous components of atmospheric dust particles. Around 60 μg of mercury vapor can be removed up to 90% by 1.0 g of magnetite nanoparticles, within a time scale of minutes. The second step included the development of an electrochemical system for the mercury recycling and regeneration of used nanoparticles. Under optimized conditions, up to 85% of mercury was recovered as elemental mercury. Postelectrolysis regenerated iron oxide nanoparticles were used in several sorption–electrolysis cycles without loss of the adsorption capacity, morphology, and surface area. The low energy usage for electrolysis can be supplied by the solar panels. The implications of our results within the context of green technology are herein discussed.
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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 | 0.001 | 0.001 |
| 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 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".