Experimental methods in chemical engineering: Electron paramagnetic resonance spectroscopy‐EPR/ESR
Bibliographic record
Abstract
Summary Electron paramagnetic resonance (EPR) spectroscopy, also known as electron spin resonance spectroscopy (ESR), utilizes absorption of microwave radiation by unpaired electrons in a magnetic field. The interaction between the unpaired electron(s) and nearby magnetic nuclei helps identify paramagnetic species and can provide information about the motion of the molecule and the local polarity, pH, viscosity, concentration, and accessibility to other paramagnetic species. This mini‐review discusses the fundamental underpinnings of EPR needed to correctly interpret EPR spectra. We describe various types of EPR spectra encountered by chemical engineers, and use application examples drawn from the chemical engineering literature to illustrate the information available from the technique. Few chemical engineering departments or even chemistry departments have EPR instruments, which contributes to the significant barrier that prevents this being adopted as a routine measurement technique. However, in 2016 and 2017, Web of Science indexed 7000 articles that applied EPR spectroscopy. A bibliometric map categorized the keywords in four categories based on co‐occurrences: magnetic properties, films, and luminescence; crystal structure, complexes, and ligands; nanoparticles, oxidation, and degradation; and, systems, radicals, and H 2 O 2 .
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".