Chemistry 2.0: Developing a New, Solvent-Free System of Chemical Synthesis Based on Mechanochemistry
Bibliographic record
Abstract
Mechanochemistry by grinding or milling has grown from a laboratory curiosity to a versatile approach for the synthesis and discovery of molecules, materials and reactivity. Focusing on organic synthesis and the chemistry of organic solids in general, we now provide a snapshot of this exciting, rapidly developing area, with the intention to illustrate its potential in establishing a more efficient and environmentally friendly system of chemical and materials synthesis, based on solid-state transformations rather than conventional, solution-dependent chemistry. 1 What is Chemistry 2.0? 2 Introduction 2.1 Why Mechanochemistry Now? 2.2 What’s in a Mechanochemistry Laboratory? 3 Liquid-Assisted Grinding (LAG): Controlling Mechanochemistry 4 The Solvent-Free Research Laboratory 5 Medicinal Mechanochemistry 6 Exploring Molecular Recognition 7 Some Myths to Dispel 8 Catalytic Reactions by Mechanochemistry 8.1 Catalysis and Reactivity Involving Bulk Metals 8.2 Enzyme Catalysis in Mechanochemistry 8.3 Coupling of Mechanochemistry, Photochemistry and Supramolecular Catalysis 9 Organometallic Mechanochemistry 10 New Opportunities 10.1 Stoichiometric Control 10.2 ‘Impossible’ Molecules 10.3 Reaction Discovery by Mechanochemistry 11 Energetics of Mechanochemistry 12 Mechanistic Understanding 13 Real-Time Reaction Monitoring 14 Conclusions
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".