Experimental Methods in Chemical Engineering: Nuclear Magnetic Resonance
Bibliographic record
Abstract
Abstract Nuclear magnetic resonance (NMR) spectroscopy measures free induction decay (FID) signals that atomic nuclei emit when excited by a radio‐frequency (RF) pulse in a static magnetic field. The Fourier‐transformed spectrum shows chemically shifted peaks, area intensity, and multiplicity, which give information on molecular structure, bonds, functional groups, and purity. Web of Science Core Collection indexed 46 000 articles that mentioned NMR in 2016 and 2017. The VosViewer software grouped the research into 5 clusters: solid‐state analysis including metabolomics; biology with in‐vitro and antibacterial applications; coupled analytical techniques to identify crystal structure for which x‐ray diffraction and density functional theory figure prominently; liquid‐state analysis for polymers, aqueous solutions, nano‐particles, and drug delivery; and chemosensors. Researchers publishing in The Canadian Journal of Chemical Engineering focus most on: liquid‐state NMR to characterize polymers, branching, and monomers; quantify conformation, reaction kinetics, and equilibrium; and assess surfactant stability, ionic liquids, and composition. We introduce the theory behind NMR spectroscopy and common applications in chemistry and material science. We highlight the strength and limitations, sources of error, and the detection limit for this analytical technique, as manufacturers develop massive magnets for high‐resolution spectra (1 GHz), and benchtop NMR for real‐time, in‐situ analysis (80 MHz).
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 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) | 0.000 | 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) | 0.000 | 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".