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Record W4386905303 · doi:10.1016/j.jenvman.2023.118976

Facile and innovative application of surfactant-modified-zeolite from Austrian fly ash for glyphosate removal from water solution

2023· article· en· W4386905303 on OpenAlexafffund
Sarah Haghjoo, Christian L. Lengauer, Hossein Kazemian, Mahmoud Roushani

Bibliographic record

VenueJournal of Environmental Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaBundesministerium für Bildung, Wissenschaft und ForschungBundesministerium für Bildung, Wissenschaft, Forschung und Technologie
KeywordsAdsorptionZeolitePulmonary surfactantFly ashChemistryLeaching (pedology)CrystallinityCation-exchange capacityChemical engineeringCationic polymerizationIon exchangeAmmonium chlorideNuclear chemistryAlkalinityCatalysisOrganic chemistryEnvironmental scienceSoil water

Abstract

fetched live from OpenAlex

This study highlights a pioneering approach in the development of an efficient, affordable, and economically feasible adsorbent specifically tailored for the removal of glyphosate (Gly) from contaminated water. To accomplish this objective, a low-cost and pure NaA Zeolite (NaAZ) was synthesized with 93% crystallinity from Austrian fly ash (AFA) as a precursor for the first-time. Taguchi design was employed to optimize critical parameters such as the SiO2/Al2O3 ratio, alkalinity concentration, time, and temperature. The cation exchange capacity (CEC) and external cation exchange capacity (ECEC) are determined as critical factors for the modification process. Subsequently, the pure NaAZ was modified with hexadecyl trimethyl ammonium chloride (HDTMAC), a cationic surfactant. The utilization of surfactant-modified zeolite (SMZ) for Gly removal demonstrates its innovative application in this field, highlighting its enhanced adsorption capacity and optimized surface properties. The AFA, NaAZ, and SMZ were characterized using analytical techniques including XRD, XRF, FTIR-ATR, SEM, TGA, BET, CHNSO analyzer and ICP-OES. The adsorbent exhibited effective Gly removal through its pH-dependent charge properties (pH 2–10), with an optimized pH 6 facilitating a significant electrostatic interaction between the adsorbent and Gly. SMZ demonstrated remarkable adsorption capacity and removal efficacy, surpassing most reported adsorbents with values of 769.23 mg/g and 98.92% respectively. Our study demonstrates the significant advantage of the SMZ, with a low leaching concentration of only 6 ppm after 60 days, ensuring environmental safety, long-term stability, and public health considerations. The kinetics of the adsorption process was well described by the pseudo-second order and the Freundlich isotherm. Pore diffusion and H-bonding were postulated to be involved in physisorption, whereas electrophilic interactions led to chemisorption type of adsorption. Consequently, SMZ provides a practical significance, broad applicability and promising solution for Gly removal, facilitating sustainable water 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 communication0.0000.000
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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2023
Admission routes2
Has abstractyes

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