Fungal Colonization of Weathered Radiata Pine Surfaces Protected with Inorganic Nanoparticles and Coatings
Bibliographic record
Abstract
Photoactive nanoparticles are used to reduce microbial colonization and self-clean surfaces of materials such as glass and ceramics. To test whether such an approach is feasible for wood surfaces, we treated radiata pine samples with TiO2 (rutile and anatase) or ZnO nanoparticles and then coated the samples with different finishes. Coated samples and uncoated controls were exposed outdoors for six months. After exposure, fungi colonizing wood surfaces were identified using molecular techniques and microscopy, and colour changes in the wood samples were also measured. Treatment of uncoated surfaces with nanoparticles reduced the discolouration of wood during weathering but had little effect on colonization of wood by black mould fungi. In contrast, pretreatment of samples with titanium dioxide nanoparticles increased the number and diversity of fungi including basidiomycetes colonizing coated samples, whereas zinc oxide nanoparticles had the opposite effect. Zinc oxide nanoparticles, however, were less effective than rutile titanium dioxide nanoparticles at reducing the discolouration of coated samples exposed to natural weathering. We conclude that none of the photoactive nanoparticles on their own are able to reduce microbial colonization and discoloration of samples. This suggests that it may be difficult to create self-cleaning wood surfaces using photoactive nanoparticles.
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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".