Hierarchical Magnetic Films for High-Performance Plasmonic Sensors
Bibliographic record
Abstract
Hierarchically structured films comprise a growing section of the field of surface-enhanced Raman spectroscopy (SERS). Here, we report a novel, powerfully enhancing hierarchical plasmonic substrate featuring patterned multilayers of magnetic iron oxide nanospheres using an external magnetic field to create sets of radial ridges. This new substrate allows for effective analyte adsorption and significant Raman signal enhancement, thanks to the contribution of both the magnetic and plasmonic components to the electromagnetic hotspots. We demonstrate significant and reliable Raman enhancement for polycyclic aromatic hydrocarbons (PAHs), dilute but persistent environmental pollutants, in a complex and real-world matrix of produced water (PW). The substrate activity for PAHs is validated by gas chromatography-mass spectrometry analysis. An impressive signal-to-noise ratio (SNR) of several dB enables detection of the analyte below 1 ppm. This multilayer magnetic film sensor substrate shows remarkable stability and robustness suitable for real-world applications while boasting simple methods and strong potential to scale up fabrication.
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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 | 0.001 |
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".