Spectral Editing of Organic Mixtures into Pure Components Using NMR Spectroscopy and Ultraviscous Solvents
Bibliographic record
Abstract
A general technique is described that permits the extraction of a complete 1H NMR spectrum for components in organosoluble mixtures. The approach should find a wide range of applications considering that pure component spectra can be generated without the need for physical separation. This technique is especially significant for synthetic organic chemistry and the pharmaceutical industry due to the potential to isolate a product spectrum even in the presence of overlapping starting materials, byproducts, or degradation products. A viscous oil-based solvent system that can be temperature-manipulated from essentially a solid at one extreme to a freely flowing liquid at the other is employed. The system contains no protons and is miscible with common organic solvents. Through careful control of the temperature and thus solvent viscosity, the behavior of small molecules moves from the positive to the extreme of the negative NOE regime. Under such conditions, all protons in a molecule correlate with all other protons as propagation by spin diffusion becomes highly efficient, behavior normally only observed with rigid macromolecules in conventional solvents. Therefore, as long as one proton (or carbon signal in hybrid experiments) is resolved for a component in a mixture, the entire proton spectrum for that molecule can be cleanly extracted from a 2D NOESY spectrum (or from selective 1D NOE-based analogues). Preliminary results are highly encouraging, indicating that the approach may be feasible for a wide range of molecules and mixtures; however, in practice the exact types of structures, combinations of structures, and range of concentrations that can be cleanly extracted will become evident as the technique becomes better established.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".