Removal of Arsenic, Cadmium and Lead from Synthetic Stormwater by Two Low-Cost Adsorbents: A Kinetic and Equilibrium Study
Bibliographic record
Abstract
The adsorption isotherms and kinetics of two low cost adsorbents, Ladybug Sand and Greensand, were determined from multi-solute batch experiments using prepared synthetic stormwater containing arsenic, cadmium and lead. The adsorption equilibrium data were fit to the Langmuir, Freundlich and Henry isotherms using both nonlinear and linear regression techniques. Kinetic data were obtained at two different stormwater concentrations. The kinetic curves were fit to the pseudo-first-order, pseudo-second-order and homogenous surface diffusion model (HSDM). A solution to the HSDM was achieved using the user-oriented numeric solution proposed by Zhang et al. (2009). From the fitted kinetic models the reaction constants, k1 and k2, as well as the surface diffusion coefficient (Ds) were determined. The maximum service lives of adsorbent columns comprised of Ladybug Sand or Greensand were calculated using the equilibrium column model (ECM) to evaluate the feasibility of the adsorbents for use in advanced stormwater treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".