Enhanced photostability of chlorophyll-a using gold nanoparticles as an efficient photoprotector
Bibliographic record
Abstract
Improving the photostability of chlorophylls is one of the main challenges to facilitate their industrial and biotechnological use. In this regard, we have employed gold nanoparticles (AuNPs) to photoprotect chlorophyll-a (Chla). The results show that the photodegradation of Chla is slowed down in the presence of AuNPs, and an increase of as much as an order of magnitude in half-life time of Chla in the presence of AuNPs has been observed. It is further seen that under in vitro conditions, AuNPs are much better photoprotective agents of Chla than β-carotene or quinones, which are known to be very effective under natural living conditions (plants). The protecting ability of Chla by AuNPs is the result of their efficient binding with Chla at its nitrogen sites even in the dark, thus inhibiting the reaction of reactive oxygen species with Chla, known to cause its degradation during illumination. The same property of AuNPs, i.e., to bind with Chla in the dark, renders them to be a better photoprotectant than carotene or quinones since these agents offer protection to Chla during its illumination.
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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) | 0.000 | 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 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".