Exploring the potential of whey ultrafiltration permeate in wood modification: Changes in wood hygroscopic and thermal behaviors
Bibliographic record
Abstract
Wood’s hygroscopic nature limits its outdoor applications. Technologies such as wood polyesterification, involving the in situ reaction of alcohols and carboxylic acids, densify the wood cell wall and potentially reduce hydroxyl group activity. Whey ultrafiltration permeate, a co-product of whey protein purification, which is rich in lactose, can be a source of OH groups for wood modification. This study explores lactose’s reactivity with biobased carboxylic acids and evaluates the resulting wood properties post-modification. Spectroscopic analyses confirmed that lactose reacts with carboxylic acids when heated above the melting point, and the Maillard reaction and caramelization may occur due to whey ultrafiltration permeate’s non-protein nitrogen substances and acidic medium combined with high temperatures. Fourier Transform infrared spectroscopy analysis verified that lactose and malic acid react within trembling aspen sawdust, significantly reducing moisture absorption and enhancing thermal stability. This study proposes a novel valorization of whey ultrafiltration permeate and a simple process for improving wood properties.
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 | 0.000 | 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".