Low-cost ionic liquid improved fidelity of scanning election microscopy on wood-based and -derived materials
Bibliographic record
Abstract
Electrically non-conducting wood-based and -derived materials often suffer from charging artifacts in scanning electron microscopy (SEM). Ionic liquids (ILs) can dissipate the excess charges but have not replaced conventional coatings. In this study, we addressed the lack of low-cost options and attention to true nanoscale features by applying 1-ethyl-3-methylimidazolium methane sulfonate ([EMI][MeSO 3 ]) to different wood-based and -derived materials. Aqueous IL treatment of microcrystalline cellulose (MCC) improved the visibility of the particles and surface microfibrils and simplified sample preparation compared to gold and carbon coatings. Tailored IL solutions restored topographic contrast without significantly altering the surface morphology of sugar maple sawdust and pinecone scales. Imaging at 1 kV on treated samples revealed more nanoscale features than at the standard 5 kV, enabled by the thin surface layer of IL. The IL treatment outperformed variable-pressure SEM in spatial resolution and signal-to-noise ratio. This convenient and affordable method can benefit rapid, high-resolution, and high-fidelity SEM imaging of non-conducting materials.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".