Ligand impact on reactive oxygen species generation of Au10 and Au25 nanoclusters upon one- and two-photon excitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (Au 10 ) and atomically-precise gold nanoclusters (Au 25 ), and investigate the influence of ligands on their photoexcitation. With the ability of atomically-precise nanochemistry, we produce Au 10 SG 10 , Au 10 AcCys 10 , Au 25 SG 18 , and Au 25 AcCys 18 (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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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