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Record W4382134443 · doi:10.3390/ma16134586

The Effect of Modifying Canadian Goldenrod (Solidago canadensis) Biomass with Ammonia and Epichlorohydrin on the Sorption Efficiency of Anionic Dyes from Water Solutions

2023· article· en· W4382134443 on OpenAlexaboutno aff
Karolina Paczyńska, Tomasz Jóźwiak, Urszula Filipkowska

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsnot available
FundersUniwersytet Warmińsko-Mazurski w Olsztynie
KeywordsSolidago canadensisSorptionBiomass (ecology)EpichlorohydrinChemistryAmmoniaEnvironmental chemistryNuclear chemistryBotanyOrganic chemistryAgronomyAdsorptionBiologyInvasive species

Abstract

fetched live from OpenAlex

This study examined the effect of modifying Canadian goldenrod (Solidago canadensis) biomass on its sorption capacity of Reactive Black 5 (RB5) and Reactive Yellow 84 anionic dyes. The scope of the research included the characteristics of sorbents (FTIR, elementary analysis, pHPZC), the effect of pH on dye sorption efficiency, sorption kinetics, and the maximum sorption capacity (describing the data with Langmuir 1 and 2 and Freundlich models). FTIR analyses showed the appearance of amine functional groups in the materials modified with ammonia water, which is indicative of the sorbent amination process. The amination efficiency was higher in the case of materials pre-activated with epichlorohydrin, which was confirmed by elemental analysis and pHPZC values. The sorption efficiency of RB5 and RY84 on the tested sorbents was the highest in the pH range of 2–3. The sorption capacity of the goldenrod biomass pre-activated with epichlorohydrin and then aminated with ammonia water was 71.30 mg/g and 59.29 mg/g in the case of RB5 and RY84, respectively, and was higher by 2970% and 2510%, respectively, compared to the unmodified biomass. Amination of biomass pre-activated with epichlorohydrin can increase its sorption capacity, even by several dozen times.

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)0.001
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 score0.050
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.202
Teacher spread0.191 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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