Correlating Cellulose Nanocrystal Particle Size and Surface Area
Why this work is in the frame
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Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are negatively charged nanorods that present challenges for characterization of particle size distribution and surface area-two of the common parameters for characterizing nanomaterials. CNC size distributions have been measured by two microscopy methods: atomic force microscopy (AFM) and transmission electron microscopy (TEM). The agreement between the two methods is good for length measurements, after taking into consideration tip-convolution effects for AFM. However, TEM widths are almost twice as large as AFM heights-an effect that we hypothesize is due to counting of a larger fraction of laterally associated CNCs in the TEM images. Overall, the difficulty of selecting individual particles for analysis and possible bias due to selection of a specific particle size during sample deposition are the main limitations associated with the microscopy measurements. The microscopy results were compared to Z-average data from dynamic light scattering, which is a useful method for routine analysis and for examining trends in size as a function of sample treatment. Measurements as a function of sonication energy were used to provide information on the presence of aggregates in the sample. Magic-angle-spinning solid-state NMR was employed to estimate the surface area of CNCs based on the ratio of integrated spectral intensities of resonances stemming from C4 sites at the crystallite surfaces and from all C4 sites. Our approach was adapted from the application of solid-state NMR to characterize larger cellulose microfibers and appears to provide a useful estimate that overcomes the limitations of using the BET method for measuring surface areas of highly aggregated nanomaterials. The solid-state NMR results show that the lateral dimension of the CNCs is consistent with that of elementary cellulose crystallites.
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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 | 0.000 | 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) | 0.000 | 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