Synthesis and Surface Functionalization of Hydride-Terminated Ge Nanocrystals Obtained from the Thermal Treatment of Ge(OH)<sub>2</sub>
Bibliographic record
Abstract
The synthesis of germanium nanocrystals (GeNCs) with well-defined surface chemistry is of considerable interest because of their potential applications in the optoelectronic, battery, and semiconductor industries. Modifying and tailoring GeNC surface chemistry provides an avenue by which reactivity, environmental compatibility (e.g., solubility, resistance to oxidation), and electronic properties may be tailored. Hydride-terminated GeNCs (H-GeNCs) are of particular interest because the reactivity of surface Ge–H bonds toward alkenes and alkynes via hydrogermylation affords the potential for convenient modification; however, these reactions and their scope have not been widely explored. This report describes a straightforward route for preparing a GeNC/GeO 2 composite via disproportionation of heretofore-unexplored Ge(II) oxide-based precursor from which the H-GeNCs were freed by subsequently chemical etching. The H-GeNCs were derivatized using a series of hydrogermylation approaches (i.e., thermally activated, radical-initiated, and borane-catalyzed). The presented findings indicate surface functionalization occurs under all conditions investigated; however the nature of surface species (i.e., monolayers vs multilayers) and surface coverage varies depending upon the conditions employed.
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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".