Gold Nanoparticle Interaction in Algae Enhancing Quantum Efficiency and Power Generation in Microphotosynthetic Power Cells
Bibliographic record
Abstract
The necessity for sustainable energy production has driven the rapid development of technologies to harness solar energy effectively. The microphotosynthetic power cells (μPSC) aim to harness solar energy from living photosynthetic cells. Currently, the power density of the μPSC is low, due to several factors. One of the major impediments and challenges of the μPSC is its lower charge transfer efficiency between the photosynthetic microorganisms and the electrodes. Herein, the proposed strategy explores the interaction of gold nanoparticles (Au NPs) with photosynthetic microorganisms for enhanced power generation from the μPSC. Herein, the intracellular biocompatible, efficient light absorbers in the form of Au NPs are introduced. Translocation of gold colloidal solution of 25 μL of 50 μg mL−1 (253.8 μmol mL−1) concentration into 2 mL whole liquid culture of algal cells (Chlamydomonas reinhardtii: ≈1 million cells mL−1) enhances operational quantum yield (ϕ0) of the algal cells by 30.2% and power generation capability by 15.2% in μPSCs. Internalized Au NPs in the algal cells quench chlorophyll fluorescence, thereby contributing to increased photosynthetic efficiency. With multiple advantages such as light absorption capability, biocompatibility, and ability to transfer the electrons, Au NPs can efficiently harvest sunlight for enhanced power generation from the μPSC.
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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".