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Record W4400594595 · doi:10.1002/cjce.25413

Efficient removal of Pb( <scp>II</scp> ) and Cd( <scp>II</scp> ) ions from wastewater using amidoxime functionalized graphene oxide

2024· article· en· W4400594595 on OpenAlexvenueno aff
Lin Zou, Wan Shang, Tingting Min, Biyu Zhang, Lu Yuan, Xin Peng, Xiangping Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionGrapheneOxideChemistryAqueous solutionWastewaterNuclear chemistryOximeGraphite oxideIonInorganic chemistryOrganic chemistryNanotechnologyMaterials scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract This study aimed to develop iminodiethylamine oxime graphite oxide (IOGO) through the grafting of iminodiethylamine oxime onto graphene oxide (GO) via amidation and oximation. The primary objective was to investigate the adsorption characteristics of Pb(II) and Cd(II) ions on IOGO. Results demonstrated that IOGO exhibited exceptional adsorption capabilities, reaching maximum adsorption capacities of 798.87 mg/g for Pb(II) and 283.50 mg/g for Cd(II). Remarkably, IOGO maintained its high adsorption performance over five consecutive adsorption cycles. Specifically, the adsorption capacity for Pb(II) remained significantly stable at 480.78 mg/g, exhibiting only an 8.77% decrease. Similarly, the Cd(II) adsorption capacity remained robust at 204.13 mg/g, demonstrating a modest reduction of 10.47%. These findings underscore the feasibility of employing IOGO in repeated adsorption processes, thereby showcasing its potential for practical applications in the removal of heavy metals from aqueous solutions.

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 head0.009
GPT teacher head0.188
Teacher spread0.178 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicAdsorption and biosorption for pollutant removalFrench-language works237,207