Experimental methods in chemical engineering: X‐ray diffraction spectroscopy—<scp>XRD</scp>
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.034 |
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".