Tunable conductivity in mesoporous germanium
Bibliographic record
Abstract
Abstract Germanium-based nanostructures have attracted increasing attention due to favourable electrical and optical properties, which are tunable on the nanoscale. High densities of germanium nanocrystals are synthesized via electrochemical etching, making porous germanium an appealing nanostructured material for a variety of applications. In this work, we have demonstrated highly tunable electrical conductivity in mesoporous germanium layers by conducting a systematic study varying crystallite size using thermal annealing, with experimental conductivities ranging from 0.6 to 33 (×10 −3 ) Ω −1 cm −1 . The conductivity of as-prepared mesoporous germanium with 70% porosity and crystallite size between 4 and 10 nm is shown to be ∼0.9 × 10 −3 Ω −1 cm −1 , 5 orders of magnitude smaller than that of bulk p-type germanium. Thermal annealing for 10 min at 400 °C further reduced the conductivity; however, annealing at 450 °C caused a morphological transformation from columnar crystallites to interconnecting granular crystallites and an increase in conductivity by two orders of magnitude relative to as-prepared mesoporous germanium caused by reduced influence of surface states. We developed an electrostatic model relating the carrier concentration and mobility of p-type mesoporous germanium to the nanoscale morphology. Correlation within an order of magnitude was found between modelled and experimental conductivities, limited by variation in sample uniformity and uncertainty in void size and fraction after annealing. Furthermore, theoretical results suggest that mesoporous germanium conductivity could be tuned over four orders of magnitude, leading to optimized hybrid devices.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".