Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane
Bibliographic record
Abstract
Data set from Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane. - Publication : Cibaka-Ndaya C., O'Connor K., Opeyemi Idowu E., Parker M. A., Lebraud E., Lacomme S., Montero D., Sanz Camacho P., Veinot J. G.-C., Roiban I.-L., Drisko G. L.. Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane. Chemistry of Materials, In press, ⟨10.1021/acs.chemmater.3c01448⟩. ⟨hal-04211253⟩ Dataset production context : Crystalline silicon particles sustaining Mie resonances are readily obtained from the thermal processing of hydrogen silsesquioxane (HSQ). Here, the mechanisms involved in silicon particle formation and growth from HSQ are investigated through real time in situ analysis in an environmental transmission electron microscope and X-ray diffractometer. The nucleation of Si nanodomains are observed starting around 1000 °C. For the first time, a highly mobile intermediate phase is experimentally observed, thus demonstrating a previously unknown growth mechanism. At least two growth processes occur simultaneously: the coalescence of small particles into larger particles and a growth mode by particle displacement through the matrix toward the HSQ grain surface. Post-synthetic characterization by scanning electron microscopy further evidences the latter growth mechanism. The gaseous environment employed during synthesis impacts particle formation and growth under both in situ and ex situ conditions, impacting particle yield and structural homogeneity. Understanding the formation mechanisms of particles provides promising pathways for reducing the energy cost of this synthetic route. For more information see the article.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".