The use of aluminum nanostructures as platforms for metal enhanced fluorescence of the intrinsic emission of biomolecules in the ultra-violet
Bibliographic record
Abstract
We consider the possibility of using aluminum nanostructures for enhancing the intrinsic emission of biomolecules. We used the finite-difference time-domain (FDTD) method to calculate the effects of aluminum nanoparticles on nearby fluorophores that emit in the ultra-violet (UV). We find that the radiated power of UV fluorophores is significantly increased when they are in close proximity to aluminum nanostructures. We show that there will be increased localized excitation near aluminum particles at wavelengths used to excite intrinsic biomolecule emission. We also examine the effect of excited-state fluorophores on the near-field around the nanoparticles. Finally we present experimental evidence showing that a thin film of amino acids and nucleotides display enhanced emission when in close proximity to aluminum nanostructured surfaces. Our results suggest that biomolecules can be detected and identified using aluminum nanostructures that enhance their intrinsic emission. We hope this study will ignite interest in the broader scientific community to take advantage of the plasmonic properties of aluminum and the potential benefits of its interaction with biomolecules to generate momentum towards implementing fluorescence-based bioassays using their intrinsic emission.
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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".