Biomass Processing via Mechanochemical Means
Bibliographic record
Abstract
Mechanochemistry can be performed on a wide range of biomass feedstocks using mixer/shaker mills, planetary mills, or extrusion devices. Using a mechanochemical approach to transform renewable feedstocks can lead to significant reductions in solvent and energy use. A brief description of how mechanochemistry works on a molecular level is provided, and further details on a typical mixer mill system are presented. In developing a mechanochemical process, chemical (e.g. type of reaction), technological (e.g. milling material), and process (e.g. frequency of milling) parameters must be taken into account and optimized on a case-by-case basis. Examples of where mechanochemistry has been successfully applied to depolymerization of cellulose, chitin, and lignin are described. Amorphization of chitin and cellulose through grinding can allow new chemistries to be accessed, which are not possible in the solution phase. Modification of cellulose to yield a thermoplastic and deacetylation of chitin to form chitosan has also been successful. Potentially bio-sourced small molecules, such as amino acids and nucleotides, have also been transformed under mechanochemical conditions including the solvent-free production of the sweetener aspartame. Mechanochemistry has been applied to a limited extent to biominerals. The varied range of chemistries and feedstocks that can be transformed through milling or extrusion will lead to continued research in this area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".