Low-Temperature Synthesis of Carbon-Rich Nanoparticles with a Clickable Surface for Functionalization
Bibliographic record
Abstract
Carbon nanoparticles (CNPs) are promising materials for optoelectronic and biomedical applications thanks to their optical properties, low production cost, and superior biocompatibility compared to traditional semiconductor quantum dots. The countless synthetic methods reported allow a library of diverse CNP structures and optical properties, guiding their subsequent applications. However, the current drawbacks lie mainly within these synthetic processes, as many of them require harsh conditions preventing control over morphology and often generating chemically inert nanoparticles. Thus, more advances on low temperature and controllable synthetic processes are desirable. In this study, we suggest a new strategy to synthesize CNPs with tunable size, while avoiding the use of harsh conditions and allowing easy surface functionalization. The metastable state of polyyne-containing materials appoints them as ideal precursors for low-temperature preparation of carbon-rich structures. Our approach is to synthesize octatetrayne-containing particles prompt to spontaneous reaction, including topochemical polymerization, followed by aromatization, to avoid harsh carbonization steps. For the particle synthesis, the well-known dispersion polymerization process has been adapted for homocoupling of terminal butadiynes, generating the octatetrayne-containing particles. The method was proven reproducible, scalable, and versatile, as the particles' size can be modulated between 50 and 170 nm. Surface functionalization via thiol-yne click chemistry was completed with a pyrene-modified thiol ligand to provide the CNPs with photoactive properties in the visible range. The functionalized particles exhibit fluorescence at 470 nm arising from excimer formation.
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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".