Practical analysis of diffuse scattering patterns of inhomogeneous liquids
Bibliographic record
Abstract
A simple analytical model for the analysis of diffuse small-angle scattering data is proposed in this work to characterize the structure of colloidal suspensions of water mixtures of organic solvents and amphiphiles. A fractal structure factor term describing the low q region was incorporated in the analytical function, which includes a particle diameter variable within the power-law decay term of the scattering intensity as a function of the scattering vector. This decay was associated with not only scattering from either surface or mass fractals but could also model the signal decay within a fluid more accurately than the Ornstein–Zernike, Debye–Anderson–Brumberger, or Teubner and Strey analytical models. This low q region also displayed a Guinier component (curvature) arising from the form factor of the scattering objects, considered here as spheres. Patterns also displayed a broad Gaussian peak at higher q associated with the aggregation of micelles within the fluid responsible for the formation of the mass or surface fractals. Each term (power-law, Guinier, and Gaussian) contained a diameter term, which was then shared among all three functions. Parameter sharing stabilized the numerical fits of the model to the data and provided an accurate estimate of the average size of the scattering object or the inhomogeneity in the fluid. Examples of the use of the model in the study of the separation of water-soluble organic solvent contaminants from water are given and used for the purpose of developing strategies for the remediation of polluted groundwater.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".