Bibliographic record
Abstract
Highly mismatched alloys (HMAs) have band structures strongly modified due to the introduction of the alloying element. We consider HMAs where the isolated state of the alloying element is near the host conduction band, which causes the conduction band to split into two bands. We determine the bulk plasma frequency when the lower-energy band is partially occupied, as by doping, using a semianalytical method based on a disorder-averaged Green's function. We include the nontrivial effects of interband transitions to the higher-energy band, which limit the plasma frequency to be less than an effective band gap. We show that the distribution of states in the split bands causes plasmons in HMAs to behave differently than plasmons in standard metals and semiconductors. The effective mass of the lower split band ${m}^{*}$ changes with alloy fraction, and we find that the plasmon frequency with small carrier concentration $n$ scales with $\sqrt{n}/{m}^{*}$ rather than the $\sqrt{n/{m}^{*}}$ that is expected in standard materials. We suggest experiments to observe these phenomena. Considering the typical range of material parameters in this group of alloys and taking a realistic example, we suggest that HMAs can serve as highly tunable low-frequency plasmonic materials.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".