Recent Development and Applications of Stretchable SERS Substrates
Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS) is a cutting-edge technique for highly sensitive analysis of chemicals and molecules. Traditional SERS-active nanostructures are constructed on rigid substrates where the nanogaps providing hot-spots of Raman signals are fixed, and sample loading is unsatisfactory due to the unconformable attachment of substrates on irregular sample surfaces. A flexible SERS substrate enables conformable sample loading and, thus, highly sensitive Raman detection but still with limited detection capabilities. Stretchable SERS substrates with flexible sample loading structures and controllable hot-spot size provide a new strategy for improving the sample loading efficiency and SERS detection sensitivity. This review summarizes and discusses recent development and applications of the newly conceptual stretchable SERS substrates. A roadmap of the development of SERS substrates is reviewed, and fabrication techniques of stretchable SERS substrates are summarized, followed by an exhibition of the applications of these stretchable SERS substrates. Finally, challenges and perspectives of the stretchable SERS substrates are presented. This review provides an overview of the development of SERS substrates and sheds light on the design, fabrication, and application of stretchable SERS systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".