Lignin-polyethylene composite adsorbents for gold capture
Bibliographic record
Abstract
We report in this paper the synthesis of lignin-polyethylene composite pellet adsorbents and their adsorption performances toward the capturing of Au(III) species from aqueous solutions. In this adsorbent design, lignin (L) as an abundantly available and low-cost biopolymer is used as the active adsorbing material, while polyethylene (PE) serves as the inert binder maintaining the structural/mechanical integrity of the adsorbents. Systematic characterizations have been undertaken on the composite adsorbents to determine their performances, including the effect of pellet composition, adsorbent dosage, pH, and co-existing ions on the adsorption, as well as the adsorption kinetics, isotherm, and reusability. The highly selective gold capture in the presence of co-existing ions has also been demonstrated with the composite adsorbent L40-PE60. This work shows the strong potential of the lignin-polyethylene composite adsorbents for the low cost and easy processing of industrial gold capturing in gold mining processes. • Lignin-polyethylene composites were developed for efficient Au(III) capture in gold mining. • L40-PE60 composite excelled with high selectivity and rapid gold adsorption. • pH sensitivity was observed with optimal performance under acidic conditions. • Potential for eco-friendly, cost-effective gold recovery in industrial processes.
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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".