Green and sustainable fabrication of DES-pretreated high-strength densified wood
Bibliographic record
Abstract
Wood is a sustainable, benign, and high-performing green structural material readily available in nature that can be used to replace structural materials. However, insufficient mechanical performance (compared to metals and plastic), moisture sensitivity, and susceptibility to microorganism attack make it challenging to use wood as it is for advanced engineering applications. We here present an efficient approach to fabricating densified wood with minimal time and waste generation, demonstrating high mechanical strength, and decreased water penetration on the surface. Wood slabs were treated with deep eutectic solvents (DESs) to solubilize the lignin, followed by in-situ regeneration of dissolved lignin in the wood. Then, the slabs were densified with heat and pressure, turning the wood into a functionalized densified material. Lignin regeneration and morphological changes were observed via two-photon microscopy and Scanning Electron Microscopy (SEM), respectively. The final product is less susceptible to water absorption on the surface and has enhanced flexural strength (> 50% higher), surface hardness (100% increased), and minimal set recovery compared to natural wood. The improved mechanical performance is due to regenerated lignin which acts as a glue and fills spaces present within the interconnected cellulose network inside the wood, forming a highly dense composite during densification. Such enhancement in the properties of DES-densified wood composite makes it a favorable candidate for advanced structural and engineering applications. Supplementary Information: The online version contains supplementary material available at 10.1007/s00226-024-01594-7.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".