Plasma-Assisted and Green Routes to Silicon and Carbon Quantum Dots: A Critical Comparison of Synthesis–Structure–Property Relationships

2026

Полный текст Открытый доступ: Gold Метаданные (CSL-JSON)
DOI 10.3390/nano16191273
Достоверность данных 70 средняя
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Аннотация

Silicon quantum dots (SiQDs) and carbon quantum dots (CQDs) are often discussed as related heavy-metal-free luminescent nanomaterials, although the mechanisms governing their formation and emission differ fundamentally. Rather than combining two parallel bodies of literature, this review uses SiQDs and CQDs as contrasting model systems to determine which synthesis-structure-property relationships are transferable and which remain material-specific. We critically compare nonthermal gas-phase plasma, plasma-liquid, and solution-phase synthesis, including green routes, with respect to precursor activation, nucleation and growth, crystallinity, surface chemistry, purification, photoluminescence, reproducibility, and scalability. Low-pressure continuous-flow radio-frequency capacitively coupled plasma (RF-CCP) is treated as the reference plasma architecture for free-standing Si nanocrystals, while atmospheric-pressure and plasma-liquid approaches are discussed according to their distinct product forms, formation mechanisms, and technological maturity. For crystalline SiQDs, quantum confinement provides a physically defined framework for interpreting size-dependent optical behavior, although oxidation, ligands, and interfacial defects remain critical. In CQDs, emission may arise from carbon-core states, surface or defect states, and molecular fluorophores, making structural assignment and fraction-resolved characterization essential. Across both material families, reported record photoluminescence efficiencies are less informative for technological translation than reproducibility, purified product yield, long-term stability, and application-specific performance. Hybrid plasma-liquid processing, operando diagnostics, standardized optical characterization, and data-guided process optimization are identified as promising directions toward scalable and reproducible quantum-dot manufacturing.