Liquid-State NMR Analysis of Nanocelluloses
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Recent developments in ionic liquid electrolytes for cellulose or biomass dissolution has also allowed for high-resolution 1 H and 13 C NMR on very high molecular weight cellulose. This permits the development of advanced liquid-state quantitative NMR methods for characterization of unsubstituted and low degree of substitution celluloses, for example, surface-modified nanocelluloses, which are insoluble in all molecular solvents. As such, we present the use of the tetrabutylphosphonium acetate ([P 4444 ][OAc]):DMSO- d 6 electrolyte in the 1D and 2D NMR characterization of poly(methyl methacrylate) (PMMA)-grafted cellulose nanocrystals (CNCs). PMMA- g -CNCs was chosen as a difficult model to study, to illustrate the potential of the technique. The chemical shift range of [P 4444 ][OAc] is completely upfield of the cellulose backbone signals, avoiding signal overlap. In addition, application of diffusion-editing for 1 H and HSQC was shown to be effective in the discrimination between PMMA polymer graft resonances and those from low molecular weight components arising from the solvent system. The bulk ratio of methyl methacrylate monomer to anhydroglucose unit was determined using a combination of HSQC and quantitative 13 C NMR. After detachment and recovery of the PMMA grafts, through methanolysis, DOSY NMR was used to determine the average self-diffusion coefficient and, hence, molecular weight of the grafts compared to self-diffusion coefficients for PMMA GPC standards. This finally led to a calculation of both graft length and graft density using liquid-state NMR techniques. In addition, it was possible to discriminate between triads and tetrads, associated with PMMA tacticity, of the PMMA still attached to the CNCs (before methanolysis). CNC reducing end and sulfate half ester resonances, from sulfuric acid hydrolysis, were also assignable. Furthermore, other biopolymers, such as hemicelluloses and proteins (silk and wool), were found to be soluble in the electrolyte media, allowing for wider application of this method beyond just cellulose analytics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".