Adsorption of Alkaline Copper Quat Components in Wood-mechanisms and Influencing Factors
Bibliographic record
Abstract
Mechanisms of adsorption of alkaline copper quat (ACQ) components in wood were investigated with emphasis on: copper chemisorption, copper physisorption, and quat adsorption. Various factors were investigated that could affect the adsorption of individual ACQ components in red pine wood. Copper chemisorption in wood was affected by ligand types coordinating with Cu and the stability of the Cu-ligand complexes in solution. For Cu-monoethanolamine (Cu-Mea) system, the prevailing active solvent species at the solution pH, [Cu(Mea)2-H]+ complexes with wood acid sites and loses one Mea molecule through a ligand exchange reaction. The amount of adsorbed Cu was closely related to the cation exchange capacity of wood. An increase in Mea/Cu ratio increased the proportion of the uncharged Cu-Mea complex and resulted in decreased Cu chemisorption in wood. Copper precipitation is also an important Cu fixation mechanisms of Cu-amine treated wood. X-ray diffraction analysis revealed that in vitro precipitated Cu was a mixture of copper carbonates (azurite and malachite) and possibly Cu2O. Higher concentration Cu-amine solutions retarded the Cu precipitation to a lower pH because of higher free amine in the preservative-wood system. The changes in zeta potential of wood in relationship to the quaternary ammonium (alkyldimethylbenzylammonium chloride: ADBAC) adsorption isotherm showed two different adsorption mechanisms for quat in wood: ion exchange reaction at low concentration and additional aggregation form of adsorption by hydrophobic interaction at high concentration. Because of the aggregation effect, when wood was treated with ACQ, high amounts of ADBAC were concentrated near the surface creating a steep gradient with depth. This aggregated ADBAC was easily leached out while the ion exchanged ADBAC had high leaching resistance. Free Mea and Cu of ACQ components appeared to compete with ADBAC for the same bonding sites in wood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".