Silicon–Gold–Silica Lamellar Structures for Sample Substrates That Provide an Internal Standard for Raman Microspectroscopy
Bibliographic record
Abstract
Crystalline silicon, widely used in the electronic industry, is also a very popular material for calibrating Raman spectrometry instruments. Silicon chips cut or cleaved from commercially available silicon wafers are low-cost monolithic monocrystalline materials that give a strong Raman line at 521 cm(-1) with almost no background. Such chips have at least one optically flat surface and can be used in place of glass microscope slides as sample substrates that provide an internal calibration standard during Raman measurements. The Raman signal intensity from the silicon can be selectively attenuated by depositing a gold layer on top of the silicon surface with variable thickness such that the far-field silicon Raman signal is comparable with the Raman signal of an investigated material adjacent to this structure. This gold layer provides the additional advantage of increased sensitivity of the spectral signal from the sample due to the reflectivity of the gold surface, which allows forward and backscattered analyte Raman excitation and signal collection. An additional thin encapsulating overlayer of SiO2 provides a protective and biocompatible surface to facilitate Raman microspectroscopic investigation of live cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".