CE34 - Contaminants, écosystèmes et santé

SERS hyperspectral imaging for contaminant detection – HYDRAE

SERS hyperspectral imaging for contaminant detection

In Hydrae project, we proposed to develop an innovative detection method based on hyperspectral chemical imaging recorded on several optimized SERS-active nanostructured patterns elaborated on the same substrate and to use a statistical analysis of spectral data (chemometrics) to detect and identify the pollutants.

Detection of diluted substances in complex environments

The constant release of pollutants into the environment and their presence in the food chain pose a threat to the equilibrium state of our ecosystems and human health. Long-term water quality management has become a new ecological and societal issue. Water analysis requires today the development of innovative, specific and sensitive advanced technologies for the detection and quantification of diluted substances in complex environments.

Different nanostructures will be patterned on the same metallic substrates and tested in order to determine which material and types of 3D-architecture can ensure both an efficient and selective exaltation of the RAMAN signal in the visible range [400, 780 nm]. By simulation, we will estimate and predict the variation of the exaltation factor of the electric field for different types of nanostructure induced by the excitation (under illumination) of the plasmons on metallic surfaces. These theoretical results will be then confronted with experimental results acquired by experts at CEA (SPEC, Saclay) by photoemission electron microscopy (PEEM), a unique high spatial resolution mapping technique allowing to reveal the distribution of the near-optical field at the nanoscale. The combination of these two approaches will allow a direct validation of some substrate geometry designs and will constitute an essential support both to optimise our substrates and to better understand the analytical measurements under real conditions of use.

Our nanostructured substrates will then be used to both validate the concept of detection of single model molecules, and quantitatively analyse low concentrated mixtures using hyperspectral chemical imaging and multivariate statistical analysis of spectral data (chemometrics). After validation of the method, our work will focus on verifying the detection of several pollutants (pesticides, emerging pollutants) in mixtures at different concentration ranges using a single SERS sensor. Thanks to the recognized expertise of the LASIR Lab. (Lille University), we will develop an original spectral data processing methodology called multivariate resolution curve analysis in order to extract without a priori the pure spectra and the corresponding relative concentrations for each chemical component present in the considered chemical system.

The optimization of the processing conditions (WP1) allows us to show, on nano-rough supports, an improvement of the Raman scattering. The detection of model molecules diluted to 10-9M is then possible.
Numerical simulations (WP2) performed from an AFM 2D topographic profile allow to calculate (in a few minutes), in the visible range, the spectral responses of our nanostructured supports. We showed that this optical response depends on the spacing between the nanostructures. The SERS signals are in good agreement with the optical near-field responses acquired by photoemission electron microscopy at submicrometric scales (WP2).
From a Raman dataset, chemometric analyzes (WP4) allowed us to locate and spatially separate, 2 model molecules (WP3) present in a diluted mixture (10-8M). Analyzes by MCR (Multivariate Curve resolution) made it possible to find, without prior knowledge of the chemical system, the pure spectra of the molecules. These studies therefore validate the methodology that we will implement on nanostructured supports.

Accordingly, we are convinced that all the experimental and simulation developments and multivariate statistical analysis will help facilitating a fast and cheap on-site analysis from a portable Raman spectrometer. Indeed, the major interest of this technique is specially to reliably provide molecular information on the target of interest in a label-free way.

In progress

The constant release of pollutants into the environment and their presence in the food chain pose a threat to the equilibrium state of our ecosystems and human health. Long-term water quality management has become a new ecological and societal issue. Water analysis requires today the development of innovative, specific and sensitive advanced technologies for the detection and quantification of diluted substances in complex environments. In Hydrae project, we propose to develop an innovative detection method based on hyperspectral chemical imaging recorded on several optimized SERS-active nanostructured patterns elaborated on the same substrate and to use a statistical analysis of spectral data (chemometrics) to detect and identify the pollutants.

L’Institut des Molécules et Matériaux du Mans (IMMM, Le Mans Université) has developed and recently patented an alternative structuring methodology to elaborate SERS substrates based on soft lithography. The discriminating advantage of the methodology is based on the control and reproducibility of the nanostructured patterns as well as the stability over time and under irradiation of the exaltation factors. In Hydrae project, different nanostructures will be patterned on the same metallic substrates and tested in order to determine which material (Au, Ag, Al) and types of 3D-architecture can ensure both an efficient and selective exaltation of the RAMAN signal in the visible range [400, 780 nm]. By simulation, we will estimate and predict the variation of the exaltation factor of the electric field for different types of nanostructure induced by the excitation (under illumination) of the plasmons on metallic surfaces. These theoretical results will be then confronted with experimental results acquired by experts at CEA (SPEC, Saclay) by photoemission electron microscopy (PEEM), a unique high spatial resolution mapping technique allowing to reveal the distribution of the near-optical field at the nanoscale. The combination of these two approaches will allow a direct validation of some substrate geometry designs and will constitute an essential support both to optimise our substrates and to better understand the analytical measurements under real conditions of use.

Our nanostructured substrates will then be used to both validate the concept of detection of single model molecules, and quantitatively analyse low concentrated mixtures using hyperspectral chemical imaging and multivariate statistical analysis of spectral data (chemometrics). After validation of the method, our work will focus on verifying the detection of several pollutants (pesticides, emerging pollutants) in mixtures at different concentration ranges using a single SERS sensor. Thanks to the recognized expertise of the LASIR Lab. (Lille University), we will develop an original spectral data processing methodology called multivariate resolution curve analysis in order to extract without a priori the pure spectra and the corresponding relative concentrations for each chemical component present in the considered chemical system. This concept will be particularly exploited in the analysis of hyperspectral imaging data sets. Accordingly, we are convinced that all the experimental and simulation developments and multivariate statistical analysis will help facilitating a fast and cheap on-site analysis from a portable Raman spectrometer. Indeed, the major interest of this technique is specially to reliably provide molecular information on the target of interest in a label-free way.

Project coordination

Jean-François BARDEAU (INSTITUT DES MOLÉCULES ET MATÉRIAUX DU MANS)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

LASIR Laboratoire de Spectrochimie Infrarouge et Raman
SPEC Service de physique de l'état condensé
IMMM INSTITUT DES MOLÉCULES ET MATÉRIAUX DU MANS

Help of the ANR 365,995 euros
Beginning and duration of the scientific project: October 2019 - 42 Months

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