Relationship between copper species in solution and leaching from alkaline copper quat (ACQ) treated wood
Bibliographic record
Abstract
Abstract The effects of pH and Cu:Mea ratio in alkaline copper quat (ACQ) solution formulation on the distribution of copper-monoethanolamine (Cu-Mea) complex species and Cu precipitation, and its influence on copper leaching from treated southern pine samples were investigated. Distribution of Cu in ACQ solution was studied by means of an equilibrium speciation model for aqueous systems (MINTEQA2). Conditions that favored a higher proportion of monovalent cationic complex, [Cu(Mea)2-H]1+, and precipitated copper as CuCO3(s), resulted in higher leach resistance in treated wood compared to conditions that favored the neutral copper complexes, [Cu(Mea)2-2H]0, and higher ligand copper complexes, [Cu(Mea)3]2+, [Cu(Mea)4]2+. Monovalent cationic Cu complex is maximized at a pH close to 9 with a Cu:Mea molar ratio of 1:4. Amounts of Cu leached at pH 9 were low compared to those at pH 12, where most of the Cu is present as neutral complex. Reduction of pH from 9 to 8 did not reduce Cu leaching, because of the lower cation exchange capacity of wood at the lower pH. Changing the Cu:Mea molar ratio to 1:3 at pH 9 significantly reduced the higher ligand complexes in the formulation and resulted in the majority of the copper being distributed as monovalent cationic complexes and some CuCO3(s) precipitation. These changes in 1:3 formulation significantly reduced copper leaching compared to Cu:Mea ratios of 1:4 and 1:10.
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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 | 0.000 |
| 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".