Influence of Alkaline Copper Quat (ACQ) Solution Parameters on Copper Complex Distribution and Leaching
Bibliographic record
Abstract
The effects of ACQ solution parameters such as copper to quat ratio, pH and copper to ligand ratio on distribution of copper complexes in solution and insoluble precipitates, and on fixation and leaching of copper in treated wood were evaluated. The distribution of ionic complexes, predicted by equilibrium speciation model (MINTEQA2), was related to laboratory fixation and leaching results at controlled ACQ solution parameters. \nA decrease in the relative proportion of copper in the ACQ formulation from a copper oxide (CuO) to didecyldimethylammonium carbonate (DDACb) ratio of 2:1 to 1:1 and 1:2 resulted in lower copper retention in the treated samples and substantially decreased the amount of copper leached per unit area.\nFor monoethanolamine (Mea) based ACQ, solution parameters which favour a higher proportion of monovalent cationic complex, which consume one reactive site in wood, and the presence of insoluble carbonate precipitate of copper in wood during preservative treatment resulted in higher leach resistance compared to the neutral copper complex present at higher pH. Ammonia (NH3) based ACQ can fix more copper at high pH as there is no chelated neutral complex as in Mea based ACQ; however divalent copper-NH3 complexes may consume two sites to fix in wood. Addition of NH3 in Mea based ACQ at Cu:Mea:NH3 ratio of 1:4:6 at pH 10.6 significantly reduced copper leaching compared to 1:4:0 (without ammonia) at pH 9 due to increased divalent copper-ammonia complexes and decreased neutral copper amine complex at elevated pH. Ammonia addition with a lower proportion of Mea (1:2.5:4 at pH 10.5-10.7), significantly reduced copper leaching compared to 1:4:0 at pH 9; no reduction was observed for ammonia addition in ACQ with a higher proportion of Mea (1:4:4 at pH 10.45). The lower copper leaching from 1:2.5:4 resulted from the higher amount of divalent copper-NH3 complexes at higher pH without compromising the amount of copper precipitated at lower pH. The higher percent copper leached from tetramethylethylenediamine (Tmed) based ACQ compared to Mea and NH3 based ACQ suggested that highly stable complexes tend to stay in solution and do not result in leach resistant copper in the 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 | 0.001 | 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 | 0.001 | 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".