Review of “Molecules in superfluid helium nanodroplets: Spectroscopy, structure, and dynamics,” edited by Alkwin Slenczka and Jan Peter Toennies, Volume 145, Topics in Applied Physics, Springer Publishing Co., New York, 2022
Bibliographic record
Abstract
A brief preface gives the historical background of the field.Cryogenic matrices as molecular sample holders were first introduced in the 1950s by George Pimentel, Herbert Broida, and coworkers for stabilizing reactive species in low-temperature, nonreactive solids and studying them using a variety of techniques, including optical and infrared spectroscopy.In 1977, the seeded beam method was introduced by Lennard Wharton, Donald Levy, and Richard Smalley as an alternative to matrix isolation.In this method, gas phase molecules were cooled to low temperatures by expanding them in an excess of inert gas into a vacuum, and then probed by optical methods, usually with lasers.Then, over a period of several years, studies of helium expansions by mass spectrometry revealed the presence of large clusters of He atoms, the possibility that these clusters might be doped with other species, and the discovery that single molecules could be inserted into droplets where they were free to rotate.A dramatic early discovery was the finding in 1992 by Fröchtenicht and Vilesov in
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".