Flexible sample cell for real-time GISAXS, GIWAXS and XRR: design and construction
Bibliographic record
Abstract
Since the properties of functional materials are highly dependent on their specific structure, and since the structural changes, for example during crystallization, induced by coating and annealing processes are significant, the study of structure and its formation is of interest for fundamental and applied science. However, structure analysis is often limited to ex situ determination of final states due to the lack of specialized sample cells that enable real-time investigations. The lack of such cells is mainly due to their fairly complex design and geometrical restrictions defined by the beamline setups. To overcome this obstacle, an advanced sample cell has been designed and constructed; it combines automated doctor blading, solvent vapor annealing and sample hydration with real-time grazing-incidence wide- and small-angle scattering (GIWAXS/GISAXS) and X-ray reflectivity (XRR). The sample cell has limited spatial requirements and is therefore widely usable at beamlines and laboratory-scale instruments. The cell is fully automatized and remains portable, including the necessary electronics. In addition, the cell can be used by interested scientists in cooperation with the Institute for Crystallography and Structural Physics and is expandable with regard to optical secondary probes. Exemplary research studies are presented, in the form of coating of P3HT:PC61PM thin films, solvent vapor annealing of DRCN5T:PC71BM thin films, and hydration of supported phospholipid multilayers, to demonstrate the capabilities of the in situ cell.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".