Bibliographic record
Abstract
Abstract High-performance conductors are essential for economically and environmentally sustainable ways of electricity transfer in modern infrastructure, manufacturing and transportation, including electric vehicles. This report reviews the aluminum conductors, their fundamentals, classification and utilization markets, focusing on metallurgical characteristics of present commercial solutions and the strategy of future development directions. The inherent features of aluminum, both beneficial and detrimental, for electrical engineering are emphasized along with alloying concepts that provide the accelerated decomposition of matrix solid solution to minimize the electron scattering. Development activities are assessed of new generation of aluminum conductors that in addition to alloying utilize novel processing techniques such as ultra-fast crystallization, severe plastic deformation and complex thermomechanical treatments aiming at grain reduction to nanometer scale, crystallographic texture control and grain boundary engineering. Transition metals and rare earths are considered as the promising alloying candidates for high-strength conductors having superior thermal stability with extra importance given to immiscible systems of Al–Ce, Al–La and Al–Y along with multiply additions, combined to generate the synergy effects. The composites with cladding configuration and particulate reinforcement including via carbon-type strengtheners are discussed as the effective solutions of advanced conductors. A variety of strategies that aim at overcoming the strength–conductivity trade-off in conductor materials are presented throughout the report. Graphical abstract
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".