Three-dimensional self-assembled hybrid silicon nanostructures for high sensitivity SERS chemical and biosensing applications
Bibliographic record
Abstract
Raman spectroscopy is a powerful tool for detection of chemical and bioanalytes but lacks enhancement required to detect these analytes at the ultrahigh sensitivity needed for many applications. Surface enhanced Raman Scattering is a technique by which an analyte signal can become greatly enhanced and, near single molecule sensitivity, is achievable. Currently, SERS-based detection platforms currently rely on noble metal nanostructures as primary enhancing sources for the detection of chemical and bioanalytes but have significant limitations in terms of reproducibility and biocompatibility. Recent research has shown that semiconductors have the ability to exhibit SERS enhancing characteristics that can potentially supplant the use of noble metals without the limitations associated with noble metal nanomaterials. This thesis presents, the generation of three-dimensional self-assembled hybrid silicon nanostructures though a laser-ion plume formation mechanism. These Si nanostructures exhibit high sensitivity SERS enhancement characteristics which can be applied for chemical and biosensing applications. In this thesis, the Raman enhancing characteristics of the hybrid Si nanostructures are examined and correlated to the unique physical morphology and material chemistry of these nanostructures. These Si nanostructures are shown to be comprised of individual Si nanospheroids that have fused to form a highly 3D nanoweb-like self-assembled nanostructures. It is also shown that these nanospheroids are composed of both amorphous and polycrystalline sub-regions, which can only be generated within an ion-plume formed by a femtosecond pulsed laser. By programming the laser, the nanostructure morphology and hybrid nature can be manipulated and optimized. These Si nanostructures are shown to be highly sensitive as SERS platforms for chemical analytes. In addition, it is shown that with the application of noble metal nanoparticles on the surface of the 3D hybrid silicon nanowebs structures, an additional enhancement boost can be optimized for the detection of chemical molecules. With this, the dual contribution to the SERS sensitivity from both the primary Si nanostructures and the secondary noble metal nanostructures can be used to detect the presence of a biomolecule analyte is shown. To delve deeper into how these hybrid Si nanostructures cause SERS enhancement of bioanalytes, the Si ion interactions within the laser-ion plume were manipulated to induce quantum-scale defects within the hybrid Si nanospheroids. By creating both an inert and oxygenated laser-ion plumes the formation of sub-nanograins within the nanospheroids and sub-nanovoids on the nanospheroid surface is shown to significantly enhance the detection of bioanalyte signal for multiple biomolecules which act as signals for various diseases. Based on the results in this thesis, it has been proven that Si-based nanostructures have the capacity to be used as sole SERS enhancing sources for chemical and biomolecule analytes.
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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 | 0.000 |
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".