Cryo-Scanning Electron Microscopy Analysis for the Structural Evolution of Cellulose Nanocrystals based Hydrogels
Why this work is in the frame
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Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are highly crystalline spindle-shaped particles that have received much attention due to their remarkable properties such as ease of surface functionalization, mechanical properties, bio-sustainability, bio-renewability, relatively lower production cost, and low cytotoxicity [1–2]. Therefore, many studies have been conducted to use these attractive nanomaterials in the matrix of polymer gels (i.e. hydrogel) for improving the gel properties [3–4]. However, the influence of CNCs on hydrogel structure is not yet fully understood since there are two major challenges when examining the structure of CNC-based hydrogels using electron microscopy (EM) technique. First, high water content materials such as hydrogels should be characterized at high vacuum operating conditions but this approach causes the liquid to evaporate, altering the original hydrogel structures during imaging. Due to this phenomenon, freeze dried methods combined with thin-metal coating have been used widely for hydrogel structure analysis using scanning electron microscopy (SEM). The second challenge is that both of the CNCs and the hydrogel have similar electron densities, and therefore poor contrast between the CNCs, polymer network and water prevents the resolution of the individual components. As result, standard EM technique cannot be applied to the hydrogel composite materials [5–6]. Therefore, in order to observe the changes of hydrogel structure with the distribution of CNCs directly without any pretreatment of samples, it is critical to develop the cryo-SEM characterization technique with new sample preparation methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it