Dynamic visualisation of a solvent-borne preservative in wood using confocal laser scanning microscopy
Bibliographic record
Abstract
Abstract Recently, a rapid method of protecting mass timber has been developed involving a combination of incising and spraying the incised wood with a solvent-borne preservative. The preservative penetrates the wood longitudinally from incisions and a deep protective envelope is achieved in the species tested to-date. However, many questions remain about the mechanism involved in creating this protective envelope. For example, how long does it take for the envelope to be created? How does wood’s microstructure affect preservative penetration? To answer these questions, we developed a novel confocal laser scanning microscopy (CLSM) system that permits the dynamic visualisation of rate and penetration patterns of a fluorescent solvent-borne preservative into wood. We demonstrate the value of this system by investigating the speed and penetration pathways of the preservative into sugi sapwood. We found that initial axial through-penetration of the preservative to a depth of 10 mm occurs rapidly (< 60 s) via tracheids in the early-to-latewood transition zone and then spreads to adjacent earlywood areas. Complete penetration throughout wood’s microstructure occurred within the holding time for commercial treatment (10 days). In conclusion our CLSM system provides valuable information on the effect of wood’s microstructure on the dynamic longitudinal penetration of a solvent-borne preservative in wood, and it could potentially be used to optimise the treatment of different wood species and mass timber using the newly developed incising/spraying treatment method.
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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".