Silver nanoparticles self assembly as SERS substrates with near single molecule detection limit
Bibliographic record
Abstract
Highly sensitive SERS substrates with a limit of detection in the zeptomole (for Nile blue A and oxazine 720) range were fabricated through a bottom-up strategy. Ag nanoparticles (Ag NPs) were self-assembled onto glass slides by using 3-mercaptopropyltrimethoxysilane (MPTMS) sol-gel as linker. The substrates were characterized by UV-Vis and AFM after each deposition of Ag NPs. It was found that the glass slide presented just a few Ag NPs aggregates scattered throughout the surface after just one deposition. The glass surface was gradually covered by a homogeneous distribution of Ag NPs aggregates as the deposition number increased. Surface-enhanced Raman scattering (SERS) of the substrates was examined at different numbers of Ag NPs deposition using nile blue A and oxazine 720 as probe molecules and two laser excitations (632.8 nm and 785 nm). Optimum SERS was observed after six depositions of Ag NPs. SERS mapping indicated that at lower deposition numbers (less than 3 Ag NPs depositions) the substrates presented a few SERS "hot-spots" randomly distributed at the surface. After 7 Ag NPs depositions, spatial distribution of the SERS signal followed a Gaussian statistics, with a percent relative standard deviation (RSD%) of approximately 19%. In addition, the sample-to-sample reproducibility of the SERS intensities under both laser excitations was lower than 20%. It was also found that these substrates can provide giant Raman signal enhancement. At optimum conditions and with a 632.8 nm laser, the signal from an estimated of only approximately 44 probe molecules (100x objective) can still be detected.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".