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Enhanced herbicide removal using an innovative NaP1-Fe3O4-La(OH)3 zeolite: Advances in water treatment and experimental modeling

2024· article· en· W4405847499 on OpenAlexaff
Sarah Haghjoo, Mohammad Kavand, Christian L. Lengauer, Hossein Kazemian, Mahmoud Roushani

Bibliographic record

VenueMicroporous and Mesoporous Materials · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Northern British Columbia
FundersBundesministerium für Bildung, Wissenschaft und ForschungBundesministerium für Bildung, Wissenschaft, Forschung und TechnologieUniversität Wien
KeywordsZeoliteChemical engineeringChemistryNuclear chemistryMaterials scienceCatalysisOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

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. Software : Blender, Avogadro, ChemDraw/Chem3D Concept : The idea, design, and visualization of this figure were entirely developed by me. Explanation : The graphical abstract visually represents the adsorption of herbicides (Glufosinate, AMPA, and Glyphosate) onto a composite material synthesized from Zeolite, Lanthanum hydroxide [ La(OH)₃], and Iron oxide nanoparticles ( Fe₃O₄). In this illustration, La(OH)₃ is symbolized by the diamond-shaped structures, and Fe₃O₄ is depicted as spherical balls. The image highlights the selective adsorption mechanism of these herbicides onto the composite surface, demonstrating the interaction between the adsorbent components and the pollutants. This visual complements the detailed explanation provided in section 4.3 of manuscript , showcasing how the composite material effectively reduces herbicide pollution through its advanced adsorptive properties. • An innovative, facile, efficient and economically feasible adsorbent is developed. • The effect of NaP1-Fe 3 O 4 -La(OH) 3 adsorbent was explored for removal of Herbicides. • A novel FPCDSD model is applied to examine the mechanism of adsorption. • Gly was reduced to meet the European Union's Maximum Residual Limit of 0.1 μg/L

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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