Combining Differential Scanning Calorimetry and Cooling-Heating Curve Thermal Analysis to Study the Melting and Solidification Behavior of Al-Ce Binary Alloys
Bibliographic record
Abstract
Two common techniques of thermal analysis, Differential Scanning Calorimetry (DSC) and Cooling/Heating Curve Thermal Analysis (CCTA), based on different signal collected and utilizing samples with a weight difference of three orders of magnitude, were used to assess the solidification and melting behavior of Al-Ce binary alloys, containing from 5 to 20 wt. % Ce. Thermal analysis was accompanied by microscopic observations of solidified structures. For heating/cooling rates of 0.2–0.4 °C/s, temperatures of eutectic transformation L ↔ Al + Al11Ce3 in the Al-10Ce alloy along with additional proeutectic reactions L ↔ Al in the Al-5Ce hypoeutectic alloy and L ↔ Al11Ce3 in Al-15Ce and Al-20Ce hypereutectic alloys, were determined. Although there was a general agreement in major transformations, registered by DSC and CCTA during melting and solidification, differences in the reaction temperature determined exceeded the typical measurement errors for each technique. In addition, DSC and CCTA exhibited differences in detecting some proeutectic reactions and minor non-equilibrium effects, accompanying the eutectic transformation. Some factors that could contribute to differences observed and their implications for engineering practice were discussed.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".