Development of a torrefied wood pellet binder from the cross-linking between specified risk materials-derived peptides and epoxidized poly (vinyl alcohol)
Bibliographic record
Abstract
Torrefied wood pellets are being developed as a renewable energy to handle green-house gas issues. To improve their competitiveness, binders have been studied and utilized to increase the energy density, durability, and storage life. Recent studies indicated several potential binders can be used to increase the density and strength of pellets. However, all of them required a more than 10 wt% binder level which doesn’t meet the ISO standard (<4 wt%). To develop a promising binder for the wood industry, SRM-derived peptides were examined, which are recovered from specified risk materials (SRM), an animal waste protein. However, unmodified peptides did not improve the strength of pellets, possibly due to their limited binding strength in such applications. Therefore, they were cross-linked with epoxidized poly (vinyl alcohol) (PVA) to generate PVA-EPC-Peptides, introduced as a wood binder. Based on this study, 3.0 wt% binder level was demonstrated to be enough for PVA-EPC-Peptides to increase the density and strength of pellets, without compromising the hydrophobicity. Moreover, pellets produced with PVA-EPC-Peptides satisfied ISO requirements for I3 class. Thus, this paper demonstrates the feasibility of using SRM-derived peptides as a wood binder, which not only creates value for SRM, but also benefits the fuel industry.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".