Experimental methods in chemical engineering: Mössbauer spectroscopy
Bibliographic record
Abstract
Abstract When a free nucleus absorbs or emits a gamma ray, it recoils to conserve energy, just like a gun recoils after shooting a bullet. Nuclei bound to a crystal lattice conserve energy when they absorb or emit gamma rays from a nuclear transition as they are fixed so their movement is restricted. This restriction is recoilless nuclear resonance fluorescence—the Mössbauer effect. The energy transmitted through a sample reveals its electronic and molecular structure and magnetic properties but only when the atoms in the source and sample are the same isotope— 57 Co/ 57 Fe is the most common couple. So, many of its applications are to identify iron species or how they change as a function of environmental conditions, like corrosion. A bibliometric map identified six major clusters centred around: nanoparticles and magnetite (Fe 3 O 4 ), crystal structure and spectroscopy, oxidation and catalysis, X‐ray diffraction (XRD) and Raman spectroscopy, 57 Fe and cathodes, and Co and thin films. In the last 30 years, the number of articles per year that mention the technique has hovered around 1250. More recently, Mössbauer spectroscopy has experienced a great rediscovery, particularly in the industrial sector for the solution of some problems, but also in space exploration.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".