Linking mechanochemistry with the green chemistry principles: Review article
Bibliographic record
Abstract
The need to explore contemporary alternatives for industrial production has driven the development of innovative techniques that address critical limitations linked to traditional batch mechanochemistry. One particularly promising strategy involves the integration of flow processes with mechanochemistry. Three noteworthy technologies in this domain are single-screw extrusion (SSE) and twin-screw extrusion (TSE) and Impact (Induction) in Continuous-flow Heated Mechanochemistry (ICHeM). These technologies go beyond the industrial production of polymers, extending to the synthesis of active pharmaceutical ingredients, the fabrication of (nano)materials, and the extraction of high-added value products through the valorisation of biomass and waste materials. In accordance with the principles of green chemistry, ball milling processes are generally considered greener compared to conventional solvothermal processes. In fact, ball milling processes require less solvent, enhance reaction rates and reaction conversion by increasing surface area and substituting thermal energy with mechanochemical energy, among others. Special attention will be given to the types of products, reactants, size of the milling balls and reaction conditions, selecting 60 articles after applying a screening methodology during the period 2020-2022. This paper aims to compile and analyze the cutting edge of research in utilizing mechanochemistry for green chemistry applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".