Analysis of lignins using <sup>31</sup>P benchtop NMR spectroscopy: quantitative assessment of substructures and comparison to high-field NMR
Bibliographic record
Abstract
Lignin is quickly emerging as a biomass-derived source for the production of some crucial organic chemistry building blocks, typically obtained from unsustainable and non-renewable petroleum feedstocks. As a complex polymer, lignin characterization is often challenging due to its random structure and multitudes of different repeating substructures. Over the last 20 years, advances in our understanding and processing of lignin, as well as important work on its characterization using 31P NMR, have led to numerous publications highlighting the many potential uses of this material. With the emergence of high-resolution benchtop NMR instruments, these types of analyses can now be accessed by many laboratories and industries that have historically not been able to take advantage of NMR due to cost or size constraints. Herein, we demonstrate that benchtop NMR is a viable technique for the 31P NMR analysis of lignin and compare our results to those obtained on a traditional high-field instrument.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".