Upgrading Agricultural Waste Into Bio‐Based Multifunctional Materials via Selective Oxidation
Bibliographic record
Abstract
Abstract Distillers’ spent grains (DG), a major agricultural byproduct of the traditional fermented food industry, remain underutilized due to the lack of efficient value‐added strategies. This study presents an effective approach to upcycle DG into bio‐based multifunctional materials. By selectively oxidizing the cellulose component in DG using sodium periodate (NaIO 4 ), in situ activation of the raw material is achieved. Subsequent hot‐pressing promotes cross‐linking reactions between the oxidized cellulose and lignin, enabling the efficient fabrication of bulk materials. The resulting materials demonstrate excellent mechanical properties (with a maximum tensile strength of 20.25 MPa, Young's modulus of 20.43 GPa, and hardness of 89 HD), thermal stability, and solvent resistance. Notably, owing to the retained lignin, these materials exhibit outstanding photothermal conversion performance (46.08% photothermal conversion efficiency), making them suitable for solar‐thermal‐electric generators (TEGs) for green energy harvesting. Furthermore, the activated DG powder can function as a solvent‐free, bio‐based wood adhesive, exhibiting strong bonding performance (bonding strength up to 2.28 MPa and adhesion energy of 5.21 kJm −2 ) and solvent resistance. This study not only enhances the resource utilization of bio‐waste but also provides a scalable production pathway for cost‐effective, all‐biomass‐based functional materials.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".