Terahertz-Raman spectroscopy for in situ benchtop monitoring of changes to extended, supramolecular structure in milling mechanochemistry
Bibliographic record
Abstract
Low-frequency Raman, also known as terahertz-Raman (THz-Raman), spectroscopy offers a laboratory benchtop-based alternative to synchrotron X-ray diffraction for real-time, in situ monitoring of ball-milling mechanochemical reactions. Although direct monitoring of the long-range structure of materials during mechanochemical reactions is generally challenging by conventional Raman spectroscopy, and typically requires synchrotron X-ray diffraction, here we use THz-Raman spectroscopy to monitor mechanosynthesis of cocrystals, stoichiomorphs, and polymorphs, detect multi-step sequences, and discover solid-state phases in systems difficult to differentiate using fingerprint-region Raman spectroscopy—all through real-time observation of changes in lattice vibrational models. The methodology is augmented by periodic density functional theory (DFT), which enables structural interpretation of spectroscopic changes, notably the identification of THz-Raman bands associated with halogen bond transformations. Simultaneous monitoring of mechanochemical processes in both the fingerprint and low-frequency Raman regions enables real-time observation of changes to extended as well as molecular structure during milling, in a single laboratory benchtop experiment, without synchrotron radiation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".