Cross-linked polymer film on copper stubs as a reusable substrate enabling imaging and quantitative analyses of aluminosilicate particles
Bibliographic record
Abstract
Access issues to potable waters around the planet is the motivation for research in desalination technologies. One class of materials that is a research focus for desalination membranes are zeolites that are comprised of silicon (Si), aluminum (Al), and oxygen, and have structures that include regular pores of varying sizes dependent on the type of zeolite. A motivation for this study was to enable characterization of non-conductive materials containing Si or Al (e.g., zeolites) using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) techniques. To avoid significant background signals directly overlapping with these samples, common sample supports and preparation protocols involving Al or Si were precluded. Cross-linked polymer coatings applied via spin coating onto polished copper (Cu) stubs are shown to be durable for reuse even with the use of aggressive cleaning techniques between samples. SEM and EDS analyses of Cu stubs were performed before and after applying the polymer coating, after drop-cast application of zeolite particles and after their subsequent removal by sonication-based techniques. The data from those trials confirmed there was no background signal from Si or Al and no cross-contamination between samples during these analyses, enabling quantitation of Al and Si in the samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".