High-Temperature Anomaly in the Synthesis of Large (<i>d</i> > 100 nm) Silicon Nanoparticles from Hydrogen Silsesquioxane
Bibliographic record
Abstract
Hydrogen silsesquioxane (HSQ) is known to disproportionate at elevated temperatures, resulting in nanoscale elemental silicon inclusions within a matrix of SiO 2 . Previous investigations suggest a continuous and direct relationship in which particle size can be increased by increasing either the processing temperature and/or processing time. Recent attempts at synthesizing large particles ( d > 100 nm) using temperatures above 1400 °C and dwell times greater than 1 h uncovered anomalies in both nanoparticle size, and the composition of the resulting composite materials. Silicon nanoparticle (SiNP) growth occurs as predicted at temperatures up to and including 1400 °C, but prolonged heating between 1500–1600 °C results in SiO 2 being the sole product of the reaction, and no nanoparticles are recovered. While longer processing times are typically associated with larger SiNPs, increasing the dwell time for samples within the anomalous temperature zone results in the opposite effect and particle formation is suppressed. The standard trends regarding particle formation, size, and relative SiNP:SiO 2 composition reemerge beyond this temperature region and are restored at 1700 °C. In addition to quantifying the boundaries of this parameter space, our characterization of the resulting materials suggests that silicon monoxide formation, promoted through the crystallization of cristobalite SiO 2, is responsible for the strange behavior of HSQ at high temperatures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".