Silicon Nanoparticles: Are They Crystalline from the Core to the Surface?
Bibliographic record
Abstract
Silicon nanoparticles (SiNPs) are biologically compatible, metal-free quantum dots that exhibit size and surface tailorable photoluminescence. The nanostructure of these materials influences their optical, chemical, and material properties and hence plays an important role in their future-generation applications in sensors, battery electrodes, optical materials, and contrast agents, among others. In this work, we employ a complement of methods including X-ray photoelectron spectroscopy (XPS), bright-field transmission electron microscopy (TEM), powder X-ray diffraction (XRD), Fourier transform infrared spectroscopy, and 29 Si solid-state nuclear magnetic resonance (NMR) spectroscopy to interrogate the bulk structure of hydride-terminated SiNPs (H-SiNPs) ranging from 3 to 64 nm in diameter and effectively probe their surface. By applying these methods, we have demonstrated that H-SiNPs consist of a size dependent layered structure made up of surface, subsurface, and core silicon regimes. The surface silicon species are manifested by a broad underlying feature in the corresponding 29 Si NMR spectra between −80 to −120 ppm for small nanoparticles (NPs), whereas the sharp resonance at higher frequency (ca. −80.9 ppm, 1 ppm full-width at half-maximum) present in large NPs is attributed to a well-ordered crystalline silicon core. A critical size junction has been identified for 9 nm H-SiNPs, where XPS and NMR show features arising from surface, subsurface, and core silicon species features arising from surface, subsurface, and core silicon species. This structural insight provides essential understanding and potential advancement in the development of SiNP-based applications in photovoltaics, battery anodes, and sensors.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".