Investigation of the effects of phase transformations in micro and nano aluminum powders on kinetics of oxidation using thermogravimetric analysis
Bibliographic record
Abstract
Aluminum micro and nanoparticles are key ingredients in the synthesis of nano energetic materials. Hence it is important to characterize the kinetics and the rate controlling process of their oxidation. The literature shows that the mass diffusion and phase transformation within the aluminum oxide shell are important. However, the description of physical processes regarding simultaneous oxidation and phase transformation is lacking. In this paper, the controlled thermogravimetric (TGA) oxidation of 40-60 nm and 1 µm Al powders is investigated at constant heating rates and under isothermal conditions, respectively, upon varying the partial pressure of oxygen. It is found that the core-shell model of homogenous oxidation is applicable to explain the TGA results when the shell does not undergo phase transformation, which predicts the apparent activation energy in good agreement with the literature data. On the other hand, the simultaneous oxidation and phase transformation is able to be addressed using the JMAK model which reveals key parameters of the rate controlling processes. Mass diffusion is indeed rate determining during the oxidation of Al micro and nanopowders while the kinetics of the reaction is fast. Unlike the micron powders, the particle size distribution has a significant effect on the shape of the oxidation curves of the nanopowders.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".