Rate and extent of adsorption of ACQ preservative components in wood
Bibliographic record
Abstract
Abstract The adsorption of copper, [as Cu(II)], monoethanolamine (MEA) and didecyldimethyl ammonium chloride (DDAC) components of CuMEA and alkaline copper quaternary (ACQ) solutions impregnated into wood was followed by measuring the changes in solution concentrations in the wood over time. The rate and extent of copper and MEA adsorption were highly dependent on the solution strength and the conditioning temperature. Both copper and MEA were adsorbed by the wood structure with a rapid initial reaction, with higher relative amounts sorbed from lower concentration solutions. This was followed by a slower adsorption that still had not equilibrated after 7 weeks at 22°C. Generally, the adsorption pattern was similar for copper and MEA, suggesting that they were adsorbed as a copper MEA complex, with an MEA/copper molar ratio close to the theoretical maximum of 4. At a higher conditioning temperature of 50°C the reaction time was greatly reduced, with the adsorption after 1 week higher than after 7 weeks at 22°C, suggesting faster and more complete reaction at higher temperatures. DDAC was adsorbed more quickly and to a higher degree than Cu(II) for all treatment solutions and should be preferentially removed from such solutions, especially if empty-cell treatments are used. There appeared to be higher Cu adsorption from the higher concentration solutions of CuMEA than from corresponding ACQ solutions, likely due to DDAC competition with copper for the same reaction sites.
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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) | 0.002 | 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".