Enabling Photoemission Electron Microscopy in Liquids via Graphene-Capped Microchannel Arrays
Bibliographic record
Abstract
Photoelectron emission microscopy (PEEM) is a powerful tool to spectroscopically image dynamic surface processes at the nanoscale, but it is traditionally limited to ultrahigh or moderate vacuum conditions. Here, we develop a novel graphene-capped multichannel array sample platform that extends the capabilities of photoelectron spectromicroscopy to routine liquid and atmospheric pressure studies with standard PEEM setups. Using this platform, we show that graphene has only a minor influence on the electronic structure of water in the first few layers and thus will allow for the examination of minimally perturbed aqueous-phase interfacial dynamics. Analogous to microarray screening technology in biomedical research, our platform is highly suitable for applications in tandem with large-scale data mining, pattern recognition, and combinatorial methods for spectro-temporal and spatiotemporal analyses at solid-liquid interfaces. Applying Bayesian linear unmixing algorithm to X-ray induced water radiolysis process, we were able to discriminate between different radiolysis scenarios and observe a metastable "wetting" intermediate water layer during the late stages of bubble formation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".