Tuning the Sensing Performance of Multilayer Plasmonic Core–Satellite Assemblies for Rapid Detection of Targets from Lysed Cells
Bibliographic record
Abstract
Optical sensors based on discrete plasmonic nanostructures are invaluable for probing biomolecular interactions when applied as plasmonic rulers or reconfigurable multinanoparticle assemblies. However, their adaptation as a versatile sensing platform is limited by the research-grade instrumentation required for single-nanostructure imaging and/or spectroscopy and complex data fitting and analysis. Additionally, the dynamic range is often too narrow for the quantitative analysis of targets of interest in biodiagnostics, food safety, or environmental monitoring. Herein we present plasmonic assembly comprising a core nanoparticle surrounded by multiple layers of satellite nanoparticles through aptamer linker. The layer-by-layer assembly of the satellite nanoparticles yields uniform discrete nanoparticle clusters on a substrate with enhanced optical properties. Binding of the model target (adenosine 5'-triphosphate, ATP) induces disassembly and leads to a dramatic decrease in the scattering intensity that can be analyzed readily from darkfield images. We demonstrate that the sensing performance, such as detection limit, dynamic range, and sensitivity, can be tuned by controlling the size of the assembly. The substrate-anchored nanoparticle assemblies are selective to only ATP, and not other adenine-containing compounds. By adapting the methodology to a flexible support, cellular ATP can be directly detected by lysing adherent cells in close contact with the plasmonic assemblies-a process that does not require any sample preparation or purification. Enhancing the optical detection signal via designing and engineering nanoparticle assemblies could enable their use with low-cost portable imaging systems and broaden their applicability beyond the study of biomolecular interaction.
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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".