Development of thermal-resistant Al–Zr based conductor alloys via microalloying with Sc and manipulating thermomechanical processing
Why this work is in the frame
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Bibliographic record
Abstract
Thermal-resistant Al-Zr based conductor alloys were developed using microalloying with a low level of Sc (≤ 0.10 wt.%) and two thermomechanical processing routes, in which the cold wire drawing was conducted before and after the aging treatment. The mechanical properties, electrical conductivities, and thermal-resistant properties of several alloys were investigated and evaluated according to the IEC standard of thermal-resistant aluminium conductors. The evolutions of the precipitates and grain structure during processing were also studied. The microalloying with Sc resulted in the precipitation of a large number of fine Al3(Sc,Zr) precipitates, which provided substantially high strength of 188-209 MPa, representing 73-88% improvement compared to the Sc-free base alloy, while maintaining excellent electrical conductivity of 57.4-59.9% IACS. Moreover, the Sc-containing alloys exhibited outstanding thermal-resistant properties, where the maximum strength reduction was limited to ≤ 6.0% after thermal exposures at 310 and 400 ⁰C. The best combinations of mechanical properties and electrical conductivities of the Sc-containing alloys were obtained after aging at 350 ⁰C for 48 h, following solutionizing at 600 ⁰C for 8 h. Both processing routes yielded comparable precipitation strengthening and strain hardening, and consequently comparable mechanical and electrical properties, where the maximum differences in the strength and electrical conductivity between both routes were 12 MPa and 1.4% IACS, respectively. The excellent combinations of mechanical, electrical, and thermal-resistant properties made the developed alloys promising candidate materials for four standard grades of thermal-resistant aluminium conductors, while taking advantage of an affordable material cost and using conventional thermomechanical processes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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