Experimental methods in chemical engineering: X‐ray diffraction spectroscopy—<scp>XRD</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract X‐ray diffraction (XRD) analysis identifies the long‐range order (ie, the structure) of crystalline materials and the short‐range order of non‐crystalline materials. From this information we deduce lattice constants and phases, average grain size, degree of crystallinity, and crystal defects. Advanced XRD provides information about strain, texture, crystalline symmetry, and electron density. When radiation impinges upon a solid, coherent scattering of the radiation by periodically spaced atoms results in scattered beams that produce spot patterns from single crystalline samples and ring patterns from polycrystalline samples. The pattern, intensities of the diffraction maxima (peaks or lines), and their position (Bragg angle θ or interplanar spacing d hkl ), correlate to a specific crystal structure. In 2016 and 2017 close to 100 000 articles mention XRD—more than any other analytical technique, and it was the top analytical technique of researchers that published in Can. J. Chem. Eng. A bibliographic analysis based on the Web of Science groups articles referring to XRD into five clusters: the largest cluster includes research on nanoparticles, thin films, and optical properties; composites, electro‐chemistry, and synthesis are topics of the second largest cluster; crystal morphology and catalysis are next; photocatalysis and solar cells comprise the fourth largest cluster; and, waste water is among the topics of the cluster with the least number of occurrences. Researchers publishing in Can. J. Chem. Eng. focus most of the XRD analyses to characterize polymers, nanocomposite materials, and catalysts.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it