Bibliographic record
Abstract
For the removal of heavy metals from the food cycle, natural processes can be used. The bio-molecules that bind metals in natural systems can make certain types of biomass suitable for metal sequestration in industrial biosorption processes which are described in this chapter. Biosorption can serve as a tool for the recovery of precious metals and the elimination of toxic metals. The term “biosorption” is used to describe the passive accumulation of metals or radioactive elements by biological materials. Usually, dead biomass serves as a basis for a family of biosorbents. In most cases, working with dead biomass offers more advantages and is therefore the object of the majority of more practically oriented biosorption studies. Some authors consider only an exchange of electrostatically bound ions to be ion exchange, and in the chapter the authors adopt a broader definition of this term. The occurrence of the groups (hydroxyl, carboxyl, sulfhydryl, sulfonate, and phosphonate) in different types of biomass is discussed. The influence of the most important parameters on the biosorption equilibrium is described in qualitative terms. The chapter deals with quantitative modeling of the key phenomena, and presents the biosorption equilibrium models. These models are the basis for modeling of dynamic processes, e.g., in columns, that are of greater industrial relevance and are described in detail. Important progress has been made in understanding the mechanism of biosorption and in quantitative modeling of this process under controlled laboratory conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.026 |
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".