Enhancement factor distribution around a single SERS Hot-spot and its relation to Single Molecule detection

We provide the theoretical framework to understand the phenomenology and statistics of single-molecule (SM) signals arising in Surface-Enhanced Raman Scattering (SERS) under the presence of so-called electromagnetic hot-spots (HS's). We show that most characteristics of the SM-SERS phenomenon can be tracked down to the presence of tail-like (power law) distribution of enhancements and we propose a specific model for it. We analyze, in the light of this, the phenomenology of SM-SERS and show how the different experimental manifestations of the effect reported in the literature can be analyzed and understood under a unified ``universal'' framework with a minimum set of parameters.Comment: 13 pages, 4 figures, submitted to J. Chem. Phy

A new database generation method combining maximin method and kriging prediction for eddy-current testing

International audienceThe numerical simulation of eddy-current non-destructive testing methods involves high complexity and expensive computational load. However, one needs to reach reliable solutions for these problems in order to be able, in particular, to solve the related inverse problem. To overcome this difficulty a new approach recently appeared. The main idea here is to build a problem-specific database, containing the calculated probe responses (“data”) for well-chosen defect configurations (at certain values of “defect parameters”). Once this database is built, the end-user just has to use this database and a computationally cheap method yields the sought defect parameters from the database, knowing the measured data. However, the construction of such databases is not a simple task. The database should be “optimal” in some sense. Optimality is a rather general property; in our context; it means that the database achieves a prescribed precision when one applies a given interpolation method (based on the stored data points), while the size of the database remains as small as possible. Nowadays, the generation of optimal databases is quite challenging and an intensively studied field in non-destructive evaluation. To the best of our knowledge, the most preferred approach of database generation in this field so far is the application of mesh generation methods (mostly originated from finite-element meshing techniques). The present contribution provides a new methodology of optimal database generation