Development of plasmonic substrates for biosensing
Bibliographic record
Abstract
The transmission of normally incident light through arrays of subwavelength holes (nanoholes) in gold thin films is enhanced at the wavelengths that satisfy the surface plasmon resonance (SPR) condition. Our group has been active on the implementation of schemes for the application of this phenomenon for chemical sensing. For instance, we have shown that the interaction between adsorbates with nanoholes modified the SP resonance conditions, leading to a shift in the wavelength of maximum transmission. The output sensitivity of this substrate was found to be 400 nm RIU-1 (refractive index units), which is comparable to other grating-based surface plasmon resonance devices. The array of nanoholes was also integrated into a microfluidic system and the characteristics of the solution flow and detection systems were evaluated. In this work, we will concentrate on improving the efficiency of the nanohole arrays for applications in chemical in chemical sensing. Attempts to improve the sensitivity of the device will be discussed. In-hole sensing is suggested as an alternative to decrease the number of probe molecules, and enhance sensitivity. A biaxial sensing scheme will also be introduced. The biaxial scheme allows for polarization-modulation detection that can account for background fluctuations. A flow-through approach should lead to an optimized transport situation of the analytes to the immobilized species at the surface, which should significantly improve the time and sensitivity of the analysis. Finally, we will discuss the implementation of multiplexing detection using these arrays. Multiplexing detection in zero-order transmission is simpler to implement than the common multiplexing imaging from angle-resolved SPR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".