Bottlebrush Polymer Templates for the Synthesis of Gold Nanostructures and their Applications as Photothermal Agents and SERS Substrates
Bibliographic record
Abstract
Abstract An innovative approach is presented for the synthesis of gold photothermal agents tailored for Near Infrared light NIR‐I and NIR‐II photothermal applications using bottlebrush polymers (BB) as soft templates (BB@Au). Upon exposure to NIR‐I, ( λ ex = 808 nm) and NIR‐II ( λ ex = 1064 nm) light, the photothermal agents (BB@Au) exhibit robust photothermal effects, achieving temperatures up to 58.3 °C under 500 mW cm −2 NIR‐II laser irradiation. This remarkable thermal response enables efficient eradication of cancer cells in both 2D and 3D settings. Furthermore, comprehensive studies demonstrate the biocompatibility of BB@Au, as evidenced by concentration‐dependent and time‐dependent analyses. Studies conducted with zebrafish larvae further confirm their safety, showing no abnormalities in hatching, survival, and histology sections. Aside from their enhanced photothermal effects, the BB@Au significantly enhances the Raman signal of adsorbed analytes. This allows their quantification and broadens the potential applications of the BB@Au particles as substrates for small molecules biosensing. The bottlebrush‐based approach to produce novel gold nanostructures with augmented photothermal capabilities introduces a versatile strategy for developing precise and effective photothermal agents in a one pot process.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".