Exciting clusters, what does off-resonance actually mean?
Bibliographic record
Abstract
, as well as 10 other Au and Ag clusters using femtosecond pump/probe transient absorption spectroscopy (fsTAS). Excited state absorbance (ESA) ubiquitous to thiolated Au and Ag clusters is used herein as an optical signature of two-photon absorbances of the 11 different Au and Ag clusters, which does not require high quantum yields of emission. The large selection of clusters, studied with a single laser system, allows us to draw conclusions on the role of the particular metal, cluster size/structure, and the effects of the ligands on the ability to absorb multiple NIR photons. The use of a laser with a 1028 nm excitation also allows us to investigate the dramatic effect of excitation wavelength and explain why laser wavelength has led to large variances in the non-linear responses reported for clusters to date. We discuss the double resonance mechanism, responsible for giant two photon absorbance cross-sections, helping match properties of metal clusters with experimental conditions for maximizing signal/response in multiphoton applications.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".