Highly Efficient Copper Sulfide‐Based Near‐Infrared Photothermal Agents: Exploring the Limits of Macroscopic Heat Conversion
Why this work is in the frame
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Bibliographic record
Abstract
Among the foreseeable therapeutic approaches at the cellular level, nanoplatform-driven photothermal therapy is a thriving tool for the selective eradication of malignant tissues with minimal side effects to healthy ones. Hence, chemically versatile, near-infrared absorbing plasmonic nanoparticles are distinctly appealing and most sought after as efficient photothermal agents. In this work, a straightforward method to synthesize monodisperse PEGylated copper sulfide nanoparticles of pure covellite (CuS) phase, featuring strong localized surface plasmonic resonance absorption in the near-infrared and flexible surface chemistry, imparted by monomethyl ether polyethylene glycol molecules, is developed and optimized. These nanoparticles show a remarkable photothermal heat conversion efficiency (HCE) of 71.4%, which is among the highest for CuS systems and rivals that of plasmonic noble metal nanostructures. Moreover, through critical evaluation and mathematical modeling of the material's properties and measurement methodology, it is assessed that the calculated HCE values drastically depend on experimental conditions such as wavelength-dependent solvent absorption properties, sol concentration, and optical path. These findings are of paramount relevance to the photothermal community, since they call for a standardization of the procedure for the evaluation of the HCE of proposed photothermal agents, in order to make the reported values universally and reliably comparable.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it