Effect of amine ligand, copper/amine ratio, and pH on copper adsorption into wood
Bibliographic record
Abstract
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 Cu2+ 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(NH3)(H2O)5]2+ form of Cu might be fixed in wood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".