Resolving the discrepancies in reported 13C solid state NMR chemical shifts for native celluloses
Bibliographic record
Abstract
Abstract A survey of the literature reporting solid-state 13C NMR spectra of native celluloses reveals inconsistencies in the reported 13C chemical shifts for cellulose Iα and Iβ allomorphs. With reported chemical shifts varying by up to 2 ppm, it is not clear what the correct chemical shifts actually are. Since reliable experimental data are important to future work, such as quantum chemical calculations of NMR parameters or identification of cellulose phases in complex cellulosic materials, it is important that the correct experimental chemical shifts be established with confidence. Through a process of digitization of previously reported spectra and careful consideration of how chemical shifts were referenced in the past, it has been possible to correct previously reported spectra of cellulose Iα and Iβ, putting them on the same chemical shift scale and establishing a definitive set of correctly referenced 13C chemical shifts for cellulose Iα and Iβ allomorphs. In addition, 1D and 2D 13C NMR experiments were carried out on a cellulose Iα-rich bacterial cellulose sample (with 25% 13C enrichment), providing additional evidence for these 13C chemical shifts and a new peak assignment of the 13C signals to the glucose units in cellulose Iα. This work resolves many of inconsistencies in the cellulose solid-state NMR literature and provides a definitive set of 13C chemical shifts that will be important for future work.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".