Editorial: Novel SERS-Active Materials and Substrates: Sensing and (Bio)applications
Bibliographic record
Abstract
Novel SERS-active Materials and Substrates: Sensing and (Bio)ApplicationsNearly 50 years have passed since the encounter of the surface-enhanced Raman scattering (SERS) phenomenon, which had a bumpy ride from a misinterpreted discovery to well-planned applications.SERS enhancement factors-defined as the intensity ratio between SERS and conventional Raman scattering signal for a given analyte normalized by the number of molecules probed-can typically achieve 8-10 orders of magnitude for the plasmonic substrates with inter-/intra-particle nanogap, while these values can exceed 10 11 , in case of, to name one example, cascaded nanooptical structures combining refractive and plasmonic optics (Kamp et al., 2020).However, reliable estimation of SERS enhancement factor, as well as fabrication of SERS-active materials and substrates guaranteeing reproducibility of SERS signal, controlled optical properties and interactions with the examined molecules, and viable quantitative analysis employing SERS spectroscopy are still the most challenging issues to overcome the limitations of SERS in order to become a routine analytical technique.Recent years have been extremely advantageous to SERS spectroscopy, which, thanks to the development of nanotechnology and progress towards a higher level of the theory-in tandem with an improved detection sensitivity of Raman instruments and advances in computing power capacities-has grown to a role that goes beyond purely academic applications.All of these, together with an enormous enhancement of the intrinsically weak Raman scattering signal, supported by high selectivity and specificity of the SERS method, offer simple detection and identification of the analyte of interest and use for designed applications.Nowadays, a smart combination of computational approaches and vibrational spectroscopy aids the interpretation of SERS experimental results (Królikowska et al., 2020).On the other hand, a thoughtful design of innovative plasmonic nano-architectures, like those exploiting SERS-active three-dimensional volumetric materials (Szlachetko et al., 2020) or with engineered nanoparticle morphology, tailoring its SERS performance (Puente et al., 2021), boosts their reallife applicability.Properly customized plasmonic nanostructures can be successfully applied as SERS-active pH-sensitive nano-and microprobes (Piotrowski et al., 2014) or selective nanosensors of metabolites in body fluids (Zhang et al., 2020), act as multifunctional materials (Liu et al., 2021), as well as provide strategies for the goals as challenging as SERS-based chiral discrimination and identification (Wang et al., 2020) or point-of-care diagnostics (Clarke et al., 2017).
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".