How Characterization of Particle Size Distribution Pre- and Post-Reaction Provides Mechanistic Insights into Mineral Carbonation
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
Mineral carbonation is the conversion of carbon dioxide, in gas form or dissolved in water, to solid carbonates. Materials characterization plays an important role in assessing the potential to use these carbonates in commercial applications, and also aids in understanding fundamental phenomena about the reactions. This paper highlights findings of mechanistic nature made on topics related to mineral carbonation, and that were made possible by assessing particle size, particle size distribution, and other morphological characteristics. It is also shown how particle size data can be used to estimate the weathering rate of carbonated minerals. An extension of the carbonation weathering rate approach is presented, whereby using particle size distribution data it becomes possible to predict the particle size below which full carbonation is obtained, and above which partial carbonation occurs. The paper also overviews the most common techniques to determine the particle size distribution, as well as complementary and alternate techniques. In mineral carbonation research, most techniques have been used as ex situ methods, yet tools that can analyze powders during reaction (in situ and real-time) can provide even more insight into mineral carbonation mechanisms, so researchers are encouraged to adopt such advanced techniques.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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