Surface Complexation and Packed Bed Mass Transport Models Enable Adsorbent Design for Arsenate and Vanadate Removal
Bibliographic record
Abstract
Co-occurrence of metal oxo-anions (e.g., arsenate) in drinking water poses human health risks. To understand and predict competition and breakthrough for individual or mixtures of oxo-anions in continuous-flow packed bed adsorption systems, we linked equilibrium surface complexation models (SCMs) with a pore surface diffusion model (PSDM). After parametrization, using data for two commercial adsorbents, the SCM and PSDM predicted well the adsorption isotherm data and column breakthrough curves, respectively, for single-solute (arsenate) and bisolute water chemistries (arsenate, vanadate) as well as chromatographic displacement of previously adsorbed arsenate by vanadate. Surface and pore diffusivities for both commercial adsorbents were 3.0 to 3.5 x10 –12 cm 2 /s and 1.1 to 0.8 x10 –6 cm 2 /s, respectively. After validation, SCM + PSDM was used in silico to evaluate adsorbent media characteristics, variable water chemistries, and reactor configurations. When contrasting hypothetical crystalline versus amorphous metal (hydr)oxide adsorbents, increasing surface site density resulted in higher Freundlich isotherm capacity ( K F ) but did not impact 1/ n . Increasing surface binding affinities beneficially impacted both the K F and 1/ n isotherms and would improve the performance of point-of-use (POU) adsorbent system applications. In silico simulation results suggest prioritizing enhancing adsorbent capacity ( q ) through improved surface reactivity in the design of new POU adsorbent materials rather than focusing on reducing mass transport limitations through intraparticle pore design. For municipal-scale adsorption systems, the PSDM simulation of the mass transfer zone shape was evaluated for hypothetical adsorbent pore designs (i.e., intraparticle porosity (ε p ) and tortuosity) and demonstrated that ε p control was a key strategy to improve performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".