Effect of ammonia addition to alkaline copper quaternary wood preservative solution on the distribution of copper complexes and leaching
Bibliographic record
Abstract
Abstract The effects of ammonia (NH3) addition to monoethanolamine (Mea)-based alkaline copper quaternary (ACQ) on the predicted distribution of cupric (Cu2+) species in solution and copper leaching from treated wood were investigated. Addition of ammonia in higher proportions reduced the amount of neutral copper-Mea complex at high pH and increased the amounts of divalent copper-NH3 complexes. At lower pH, the amounts of high-ligand copper-Mea complexes were reduced. These effects should promote better fixation and diminish copper leaching. However, ammonia addition also reduced the monovalent copper-Mea complex and the amount of copper fixed by precipitation at lower pH. This effect could, potentially, reduce fixation and leach resistance. Ammonia addition to the ACQ formulation from a Cu:Mea:NH3 ratio of 1:4:0 (normal Mea formulation, pH=9) to 1:4:2 (pH 10–10.2) and 1:4:4 (pH 10.4–10.5) did not significantly affect the amount of copper leached. However, a further increase in ammonia to 1:4:6 (pH 10.6–10.7) resulted in a significant reduction in copper leaching. At pH 9, an increase in the proportional amount of ammonia increased the amount of copper leached by reducing the amount of monovalent copper-Mea complex and increasing the divalent copper-NH3 complexes. Ammonia addition with a lower amount of Mea, 1:2.5:4 formulation (pH 10.5–10.7), significantly reduced copper leaching as a result of higher amounts of divalent copper-NH3 complexes adsorbed at higher pH (higher cation exchange capacity) without compromising the amount of copper precipitated at lower pH.
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 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".