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Record W2024356710 · doi:10.1002/jctb.353

Gold adsorption from cyanide solution by chitinous materials

2001· article· en· W2024356710 on OpenAlexaff
Hui Niu, Bohumil Volesky

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsMcGill University
Fundersnot available
KeywordsSorptionChemistryCyanideAdsorptionChitinAmine gas treatingNuclear chemistryOxidizing agentGraftingFourier transform infrared spectroscopyMetalInorganic chemistryOrganic chemistryChitosanChemical engineering

Abstract

fetched live from OpenAlex

Abstract Adsorption of AuCN 2 − by chitinous materials such as acid‐washed crab‐shells burnt crab‐shells, as well as by chitin modified by quaternization of amine was affected by the pH of the sorption system. The maximum AuCN 2 − uptake by acid‐washed crab‐shells occurred at pH 3.7 corresponding to a final Au concentration of 0–0.025 m M . While this material did not bind any AuCN 2 − at pH 10, crab‐shells burnt in a non‐oxidizing atmosphere removed 90% of the metal under these alkaline conditions. SEM with EDXA analysis of the biosorbent showed that the heat treatment changed the ratio of C/O and created a highly porous material structure. FTIR results indicated that phenolic groups were the main sites responsible for AuCN 2 − binding on burnt acid‐washed shells. Chitin, the main component of crab‐shells, was modified by grafting tertiary amine to C‐3 and C‐6 on chitin to create quaternary amine groups. The presence of quaternary amine groups also made AuCN 2 − uptake possible at pH10. The results confirmed that certain chitinous materials are capable of effectively removing and concentrating anionic gold cyanide from both acidic and alkaline solutions if pretreated by appropriate physical or chemical methods. © 2001 Society of Chemical Industry

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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.

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

Citations17
Published2001
Admission routes1
Has abstractyes

Explore more

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