Femtosecond electron diffraction: ‘making the molecular movie’
Bibliographic record
Abstract
Femtosecond electron diffraction (FED) has the potential to directly observe transition state processes. The relevant motions for this barrier-crossing event occur on the hundred femtosecond time-scale. Recent advances in the development of high-flux electron pulse sources with the required time resolution and sensitivity to capture barrier-crossing processes are described in the context of attaining atomic level details of such structural dynamics-seeing chemical events as they occur. Initial work focused on the ordered-to-disordered phase transition of Al under strong driving conditions for which melting takes on nm or molecular scale dimensions. This work has been extended to Au, which clearly shows a separation in time-scales for lattice heating and melting. It also demonstrates that superheated face-centred cubic (FCC) metals melt through thermal mechanisms involving homogeneous nucleation to propagate the disordering process. A new concept exploiting electron-electron correlation is introduced for pulse characterization and determination of t=0 to within 100fs as well as for spatial manipulation of the electron beam. Laser-based methods are shown to provide further improvements in time resolution with respect to pulse characterization, absolute t=0 determination, and the potential for electron acceleration to energies optimal for time-resolved diffraction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".