Recent Developments and Trends in Sustainable and Functional Wood Coatings
Bibliographic record
Abstract
In the last decade, a transformation has occurred in the coating industry. While, in the past, the industry was primarily focused on reducing volatile organic compounds (VOC) and formaldehyde emissions, it is now circularity driving this industry. In this paper, we present several advances that have been made, as well as key trends in the wood coatings industry. Replacing petroleum-based chemicals in coating formulations is at the heart of current research. In recent years, various biosourced molecules from animal and plant sources have been the subject of many studies aiming to incorporate them in coatings. Despite all the progress made in the last few years, coating producers are still facing many challenges regarding the availability and quality of biobased raw materials and balancing performance versus cost. While most of the sustainable coating solutions discussed in this review focus on well-known and widely accepted coating chemistries and technologies (water-based and photopolymerizable polyurethanes (PUs), acrylics, and epoxies), we also present new technologies that are expected to gain significant importance in the next few years such as layer-by-layer (LBL), polyelectrolyte complexes, and isocyanate-free PU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".