Quantification of mobilized copper(II) levels in micronized copper-treated wood by electron paramagnetic resonance (EPR) spectroscopy
Bibliographic record
Abstract
Abstract Sapwood sawdust from southern pine was treated with micronized copper (MC) under various conditions and the mobilized copper(II) (Cumob) concentrations were determined in the treated wood by electron paramagnetic resonance (EPR) spectroscopy. The spectral parameters for the copper sulfate (CuSO4)-treated sapwood and those of the MC-treated sapwood were very similar. A linear correlation was found between the intensities of copper (Cu) EPR spectra and those of Cu energy-dispersive X-ray fluorescence spectroscopy in a series of CuSO4-treated sapwood reference samples. Thus, the EPR signal intensities could be reliably correlated to the mass of reacted Cu present using this calibration curve. The amount of the Cumob in sawdust treated by MC suspensions increased during the first 2–3 days after the initial treatment and then reached a maximum during the 7-day monitoring period. In the case of the treatment with MC alone or MC azole, an increased MC concentration led to an elevated amount of Cu (to a maximum of ∼0.23% Cu) solubilized by the sapwood. If the wood was treated with MC quat, the Cumob initially increased, but at higher concentrations the Cumob content decreased, due to the interference by the quat cobiocide on the acid reaction between the wood and the basic Cu carbonate. An examination of commercially-treated wood confirmed the laboratory observations.
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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".