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Versatile waste wood-chitosan composites for 2,4-D and paraquat adsorption: Isotherm modelling and thermodynamic evaluation

2024· article· en· W4405769421 on OpenAlexaff
Hamant E. France, Oliver K.L. Strong, Tyler Roy, Andrew J. Vreugdenhil

Bibliographic record

VenueChemosphere · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsTrent University
Fundersnot available
KeywordsChitosanAdsorptionSurface modificationParaquatRaw materialSpecific surface areaAcetic acidChemistryMaterials scienceChemical engineeringPulp and paper industryNuclear chemistryWaste managementOrganic chemistry

Abstract

fetched live from OpenAlex

2,4-dichlorophenoxy acetic acid (2,4-D) and 1,1-dimethyl-4,4-bipiridinium chloride (paraquat) are among the most widely used herbicides and are known to be toxic. Fabrication of green adsorbents which are capable of removing both herbicides remains a challenge. Here, we fabricate a novel adsorbent from tropical waste wood and use a facile, chitosan-mediated N-heteroatom functionalization technique to augment surface nitrogen and improve specific surface area. The addition of 20 wt% chitosan to the waste wood feedstock prior to activation, increased specific surface area by 300 m 2 /g (∼25%) and nitrogen content by 7-fold. This functionalized material removed 69% of 2,4-D and 82% of paraquat at initial concentrations of 4 ppm and 40 ppm from model solutions at pH 7. It also removed 39% 2,4-D and 93% paraquat from binary mixtures demonstrating its versatility. 2,4-D adsorption increased with chitosan addition suggesting synergistic effects between protonated amine functions and the anionic herbicide form. Paraquat adsorption was negatively correlated with chitosan addition, implying antagonistic interaction between protonated amine functions and quaternary nitrogen atoms on herbicide molecules. Adsorption of both herbicides was spontaneous, entropically favored and exothermic with ΔG ° : 19.2 kJ/mol and −28.8 kJ/mol; ΔS ° : 7.42 and 28.6 J/Kmol and ΔH ° : 17.0 kJ/mol and −20.1 kJ/mol for 2,4-D and paraquat respectively. Chitosan addition therefore provides a facile and green alternative for N-heteroatom functionalization, and these nitrogen-doped materials are promising candidates for the removal of multiple herbicides from aqueous systems. • Optimized chitosan-greenheart AC was obtained by addition of 20 wt% chitosan. • Chitosan doping increased nitrogen content by 7-fold and surface area by 300 m 2 /g. • 2,4-D and paraquat removal efficiencies were 69 % and 82%. • Physisorption enthalpies were −17.0 kJ/mol (2,4-D) and −20.1 kJ/mol (paraquat).

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 score0.005
Threshold uncertainty score0.010

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)0.0010.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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

Citations14
Published2024
Admission routes1
Has abstractyes

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