Morphological and chemical evolution of monocrystalline porous germanium over time in various storage environments
Bibliographic record
Abstract
Mesoporous germanium (MP-Ge) emerges as a very appealing material for many applications such as anode material for Lithium-Ion batteries due to it high specific area and large void spaces or, in optoelectronics as sacrificial layer for III-V materials growth and detachment, allowing notably several uses of a single Ge substrate. These porous nanostructures are distinguished by a large specific surface area and are prone to degradation with time due to exposure to the environment. To understand and be able to reduce this effect, we studied the chemical and morphological evolution of porous germanium layers under various ambient storage conditions for 3 months to identify the main parameters responsible for material degradation. This study demonstrates that the ambient air environment leads to the growth of native oxide, leading to major morphology changes. Scanning electrons microscope (SEM) showed the formation of clusters and the enlargement of the pores after 90 days. These structural modifications are caused by the oxidation of Ge, and more specifically by the creation of GeO2 matrices due to the synergy of dioxygen (O2) and humidity (H2O(g)). The energy brought by light can exacerbate these phenomena and thus accelerate the degradation rate of the pore morphology. Based on these experimental results, we propose efficient solutions to limit the GeO2 proportions and the clusters' appearance, by storing them under a dry neutral atmosphere (Ar) or by adding a hydrogen halide pre-treatment (10s 1% HBr solution).
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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) | 0.001 | 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".