Ligand impact on reactive oxygen species generation of Au10 and Au25 nanoclusters upon one- and two-photon excitation
Bibliographic record
Abstract
Abstract In photodynamic therapy (PDT), light-sensitive photosensitizers produce reactive oxygen species (ROS) after irradiation in the presence of oxygen. Atomically-precise thiolate-protected gold nanoclusters are molecule-like nanostructures with discrete energy levels presenting long lifetimes, surface biofunctionality, and strong near-infrared excitation ideal for ROS generation in PDT. We directly compare thiolate-gold macromolecular complexes (Au10) and atomically-precise gold nanoclusters (Au25), and investigate the influence of ligands on their photoexcitation. With the ability of atomically-precise nanochemistry, we produce Au10SG10, Au10AcCys10, Au25SG18, and Au25AcCys18 (SG: glutathione; AcCys: N-acetyl-cysteine) fully characterized by high-resolution mass spectrometry. Our theoretical investigation reveals key factors (energetics of excited states and structural influence of surface ligands) and their relative importance in singlet oxygen formation upon one- and two-photon excitation. Finally, we explore ROS generation by gold nanoclusters in living cells with one- and two-photon excitation. Our study presents in-depth analyses of events within gold nanoclusters when photo-excited both in the linear and nonlinear optical regimes, and possible biological consequences in cells.
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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".