Cu<sub>2−</sub><i><sub>x</sub></i>S<i><sub>x</sub></i> Capped AuCu Nanostars for Efficient Plasmonic Photothermal Tumor Treatment in the Second Near‐Infrared Window
Bibliographic record
Abstract
Abstract Plasmonic nanohybrids are promising photo energy conversion materials in photoelectronics and biomedicine, due to their unique surface plasmon resonance (SPR). Au and Cu2−xSx nanostructures with strong SPR in the near‐infrared (NIR) spectral region are classic plasmonic systems used to convert NIR photons into heat for photothermal therapy (PTT). The rational design of the Au/Cu2−xSx nanohybrids is expected to induce better photothermal conversion; however, the construction of such hybrids via wet‐chemistry methods with a well‐controlled interfacial structure is still challenging. Here, the synthesis of an AuCu Star/Cu2 −xSx nanohybrid is reported, where the Cu2−xSx components are selectively grown on the AuCu nanostar tips to form “caps”. The spatial formation of the Cu2−xSx caps on star tips is mainly governed by surfactant concentration, tip curvature, and experimental manipulation. The nanohybrids show low cytotoxicity and superior photothermal conversion efficiency, enabling robust PTT to kill cancer cells in the second NIR window. Numerical simulation reveals that the coupling of Cu2−xSx on nanostar tips generates strong interfacial electric field, improving photothermal conversion. Moreover, the spatial separation structure favors the continuous flow of hot charge carriers to produce active radicals, further promoting the tumor treatment effect.
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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".