Magnesiothermic Reduction of Thin Films: Towards Semiconducting Chiral Nematic Mesoporous Silicon Carbide and Silicon Structures
Bibliographic record
Abstract
There is a growing demand for new methods to prepare porous Si‐based materials for applications in optoelectronic and microelectronic devices. In this work, the preparation of SiC and Si from magnesiothermic reduction of chiral nematic SiO2/C composites and mesoporous SiO2, respectively, is reported. The SiO2/C composites are prepared by cocondensing SiO2 with cellulose nanocrystals (CNCs) followed by pyrolysis. The magnesiothermic reduction of the composites produces SiC after prolonged solid‐state reaction, with mixed MgC2/SiC intermediates. Iridescent mesoporous tetragonal MgC2/SiC structures that retain the long‐range twisted organization of the starting composites transform to mesoporous cubic SiC with a chiral nematic hierarchical structure, but with some loss of order. On the other hand, the magnesiothermic reduction of the chiral nematic mesoporous SiO2 templated from CNCs affords mesoporous Si materials with a layered hierarchical structure. The structural properties and the conductivity of the products, as well as the reaction pathways by analysis of the materials at intermediate stages, are investigated. These experimental results show that the magnesiothermic reduction is a promising way to obtain new porous semiconducting materials with chiral nematic structures.
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) | 0.000 | 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".