Remediation of Wastewater Containing Heavy Metals Using Modified Diatomite
Bibliographic record
Abstract
Diatomite, wood fly ash and their mixture with TiO2 were tested as adsorption substrates for copper and cadmium cations removal from wastewater. Raw diatomite and wood ash were pretreated and stabilized by washing and were comparatively investigated as adsorption substrates for heavy metals. Further on, a composite mix of pretreated diatomite and wood ash was obtained by mixing with nano-structured TiO2, to increase the surface area of the substrate materials. The novel adsorbent was investigated in terms of crystallinity (XRD), surface properties (AFM, SEM, porosity and BET surface) and surface chemistry (EDX, FTIR). Equilibrium studies demonstrated that the novel material has a high capacity for Cu2+ and Cd2+ removal, showing removal efficiencies above 80% for cadmium and of 99% for copper cations, in optimised adsorption experiments. In this paper the kinetic mechanisms of the cation adsorption are discussed and are correlated with the substrates properties.
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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) | 0.001 | 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".