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Record W2079147041 · doi:10.1515/hf.2010.077

Effect of amine ligand, copper/amine ratio, and pH on copper adsorption into wood

2010· article· en· W2079147041 on OpenAlexaff
Myung Jae Lee, Paul Cooper

Bibliographic record

VenueHolzforschung · 2010
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEthanolamineChemistryAdsorptionCopperAmine gas treatingInorganic chemistryChemisorptionEthylenediamineLigand (biochemistry)AmmoniaIon exchangeCation-exchange capacityOrganic chemistryIon

Abstract

fetched live from OpenAlex

Abstract To measure the chemical adsorption capacity of wood for copper (Cu) and amine in Cu-amine solution, Cu was formulated with different ratios of mono- (Mea), di- (Dea), tri- (Tea) ethanolamine, ethylenediamine (En) and ammonia (Am), and the Cu adsorption was compared with the cation exchange capacity (CEC) of red pine ( Pinus resinosa Ait.). The chemisorption capacity of the wood for Cu was highly pH dependant and varied with ligand types investigated in this study. Although wood chemisorption capacity increases with pH, high amine ratio Cu-ethanolamine complexes showed very limited adsorption at high pH owing to competition with free ethanolamine in combination with the formation of uncharged ion complexes. During Cu-Mea treatment, negligible Cu 2+ was converted to Cu + and no significant delignification was detected even at very high Mea ratios. Cu appeared to be adsorbed as if it were singly charged, even though most of the complexes present are 2+ charged. The three ethanolamine ligands generally showed similar adsorption tendencies, although their pH dependencies differed. Cu in En was much less adsorbed at intermediate pH compared with the CEC, but had higher adsorption at high pH. Cu in Am also showed higher adsorption at high pH compared with Mea and the [Cu(NH 3 )(H 2 O) 5 ] 2+ form of Cu might be fixed in wood.

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 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 score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.003
GPT teacher head0.208
Teacher spread0.205 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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