Self‐Limiting Sub‐5 nm Nanodiamonds by Geochemistry‐Inspired Synthesis
Bibliographic record
Abstract
Controlling diamond structures with nanometer precision is fundamentally challenging owing to their extreme and far-from-equilibrium synthetic conditions. State-of-the-art techniques, including detonation, chemical vapor deposition, mechanical grinding, and high-pressure-high-temperature synthesis, yield nanodiamond particles with a broad distribution of sizes. Despite many efforts, the direct synthesis of nanodiamonds with precisely controlled diameters remains elusive. Here the geochemistry-inspired synthesis of sub-5 nm nanodiamonds with sub-nanometer size deviation is described. High-pressure-high-temperature treatment of uniform iron carbide nanoparticles embedded in iron oxide matrices yields nanodiamonds with tunable diameters down to 2.13 and 0.22 nm standard deviation. A self-limiting, redox-driven, and diffusion-controlled solid-state reaction mechanism is proposed and supported by in situ X-ray diffraction, ex situ characterizations, and computational modeling. This work provides a unique mechanism for the precise control of nanostructured diamonds under extreme conditions and paves the road for the full realization of their potential in emerging technologies.
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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".