X-ray Absorption Spectroscopy of Silicon Carbide Thin Films Improved by Nitrogen for All-Silicon Solar Cells
Bibliographic record
Abstract
Synchrotron-based experiments in combination with optical measurements were used to explore the potential of a photovoltaic material based on silicon carbonitride (SiCN) thin films, in particular for the use in space solar cells. The large bandgap, SiCN films were fabricated using electron cyclotron resonance plasma-enhanced chemical vapour deposition (ECR-PECVD) followed by low-temperature annealing processes. X-ray absorption near edge structure (XANES) with excitations at the carbon, nitrogen, and silicon K-edges verifies that the presence of nitrogen tends to disrupt Si–C networks. This results in the enhancement of light absorption and bandgap widening, which is desirable for front emitters in all-silicon tandem solar cells. The ternary structure of SiCN allows bandgap engineering and tuning of the light absorption and refractive index through careful design of the composition. XANES showed that the thermal annealing at a medium temperature (500 °C) using N2 ambient promoted the formation of Si–Si and C–N sp2 bonds before disappearing in higher annealing temperatures. In our opinion unlocking the potential of robust SiC mixed with nitrogen in SiCN matrix has appeal in radiation-resistant solar cells, where it can serve as the top emitter layer in all-silicon tandem solar cells and at the same time benefits the antireflection properties.
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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".