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Record W2145370465 · doi:10.1002/app.42717

Competitive adsorption of Ag (I) and Cu (II) by tripolyphosphate crosslinked chitosan beads

2015· article· en· W2145370465 on OpenAlexafffund
Chunxia Mao, Syed Imtiaz, Yan Zhang

Bibliographic record

VenueJournal of Applied Polymer Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandUniversity of Alberta
KeywordsChitosanAdsorptionAmine gas treatingSorbentBimetallic stripFourier transform infrared spectroscopyMetalNuclear chemistryX-ray photoelectron spectroscopyChemistryLangmuir adsorption modelPolymer chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT Alkalization of chitosan before crosslinking was applied in this study to enhance the adsorption capacity of the modified chitosan. Competitive adsorption of Ag (I) and Cu (II) from bimetallic solutions was studied using the newly synthesized tripolyphosphate crosslinked alkalized chitosan beads. Results indicated that alkalization before crosslinking helps to protect amine group from crosslinking and hence increases the uptake capacity and selectively of the synthesized beads toward Ag (I). The maximum uptakes of Ag (I) and Cu (II) were 82.9 and 15.5 mg g−1, respectively, at room temperature with an initial concentration of each metal being 2.0 mM and the sorbent dosage of 1.0 g L−1. The uptake of Ag (I) and Cu (II) by the beads can be better described by Langmuir isotherm and pseudo‐second rate equation. Analyses from FTIR and XPS confirmed that free amine, hydroxyl, and groups are involved in metal binding with amine and hydroxyl groups more selective to Ag (I). © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42717.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.232
Teacher spread0.222 · 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
Published2015
Admission routes2
Has abstractyes

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