Quantitative <sup>31</sup>P NMR Analysis of Lignins and Tannins
Bibliographic record
Abstract
The development of sustainable biorefinery products is confronted, among others, with the challenge of lignin and tannin valorization. These abundant, renewable aromatic biopolymers have not been widely exploited due to their inherent structural complexity and high degrees of variability and species diversity. The lack of a defined primary structure for these polyphenols is further compounded with complex chemical alterations induced during processing, eventually imparting a large variety of structural features of extreme significance for any further utilization efforts. Consequently, a protocol for the rapid, simple, and unequivocal identification and quantification of the various functional groups present in natural polyphenols, is a fundamental prerequisite for understanding and accordingly tailor their reactivity and eventual utility. Quantitative 31P NMR offers the opportunity to rapidly and reliably identify unsubstituted, o-mono substituted, and o-disubstituted phenols, aliphatic OHs, and carboxylic acid moieties in lignins and tannins with broad application potential. The methodology consists of an in situ quantitative lignin or tannin labeling procedure using a suitable 31P containing probe, followed by the acquisition of a quantitative 31P NMR spectrum in the presence of an internal standard. The high natural abundance of the 31P nucleus allows for small amounts of the sample (~30 mg) and short NMR acquisition times (~30-120 min) with well-resolved 31P signals that are highly dependent on the surrounding chemical environment of the labeled OH groups.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".