Enhanced herbicide removal using an innovative NaP1-Fe3O4-La(OH)3 zeolite: Advances in water treatment and experimental modeling
Bibliographic record
Abstract
This study explores the synthesis of a novel and efficient NaP1 zeolite from Austrian fly ash (AFA), composited with Fe 3 O 4 nanoparticles (NPs) and lanthanum hydroxides [La(OH) 3 ]. The composite's efficacy was tested for simultaneously adsorbing glyphosate (GLY), glufosinate (Glu), and aminomethylphosphonic acid (AMPA) from water solution. The inclusion of Fe 3 O 4 NPs and La(OH) 3 enhanced the nanoadsorbent's rapid and effective separation capabilities. Significantly, an innovative kinetic model, the Film-Pore-[Concentration-Dependent] Surface Diffusion Model (FPCDSD), was developed to analyze adsorption mechanisms, aligning with experimental results and accurately predicting adsorption processes in single and competitive scenarios. The model used detailed calculations to evaluate mass transfer resistances, employing parameters like rotation speed, adsorbent dosage, and initial concentrations to correlate adsorption data under various conditions. The study found that adsorption capacity retained 92 % effectiveness after 10 adsorption-desorption cycles, consistent with previous research. Results indicated that electrostatic interactions, herbicide affinity for La and Fe complexes, hydrogen bonding, and surface and pore diffusion likely drive adsorption mechanisms. Laboratory tests showed that Gly achieved the Maximum Residual Level (MRL) of 0.1 μg/L as per the European directive for drinking water with 99.95 % removal efficiency, suggesting that NaP1-Fe 3 O 4 -La(OH) 3 is a highly effective option for water treatment.
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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) | 0.000 | 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".