Recovery of Silver Using Adsorption Gels Prepared from Microalgal Residue Immobilized with Functional Groups Containing Sulfur or Nitrogen
Bibliographic record
Abstract
Although biodiesel oil extracted from microalgae attracts much attention as one of the most promising green energies, its high production cost is a big problem, impeding its extensive use. In order to lower the production cost, the effective use of microalgal residue after extracting biofuel was investigated as a feed material of functional materials. In the present work, a new adsorbent for silver(I) was prepared by immobilizing functional groups of polyethylene-polyamine or dithiooxamide, which exhibita high affinity for soft Lewis acids such as silver(I) ions. Their adsorption behaviors for silver(I) were investigated from aqueous nitrate and acidothiourea media. The effects of the concentrations of nitrate and thiourea, as well as of sulfuric acid, were qualitatively interpreted. From the study of adsorption isotherms on these gels, they were found to exhibita higher adsorption capacity than the majority of those reported to date.
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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) | 0.001 | 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 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".