Infrared nanoplasmonic metasurfaces augmented by artificial intelligence for universal biosensing

Nanoplasmonic metasurfaces have shown outstanding light-matter interaction enhancement capabilities, leading to their emergence as powerful platforms for highly sensitive biospectroscopy. Metasurface-enhanced biospectroscopy offers unprecedented opportunities for biological studies, and its full potential remains to be unleashed. Mid-IR metasurfaces, in particular, are very promising because they can act as amplifiers of fingerprint-like molecule vibrations, which are plentiful in this rich spectral range. In this thesis, we develop novel nanoplasmonic designs coupled with custom microfluidics and artificial intelligence-based data analysis models to demonstrate real-time, label-free, chemically-specific, and non-destructive monitoring of biomolecules and their interactions in aqueous media. Our first nanoplasmonic design combines optimized grating order-coupled nanoantenna arrays with protein-accessible nanogaps to enable the high sensitivity monitoring of proteins and their three-dimensional structures in aqueous media. The engineered nanoantennas reach electric field intensity enhancements of up to five orders of magnitude and provide chemically specific detection of proteins and their secondary structures down to picograms and nanograms per milliliter, respectively.In the next part of the thesis, we develop multiresonant metasurfaces to monitor interactions between biomolecules with vibrational fingerprints in different parts of the mid-IR range. Our first effort focuses on developing a nanoplasmonic design for simultaneous monitoring of both proteins and lipid molecules. Lipids are another important class of biomolecules as they are the building blocks of biological membranes, and lipid-protein interactions are at the core of many cellular processes. New analytical tools for their study in water and at the monolayer level are of fundamental importance. Therefore, we introduce a dual-resonant nanoplasmonic design coupled to machine learning-based data analysis to overcome current sensor challenges. We apply our technology to a dynamic system involving synaptic vesicle mimics and demonstrate that we can resolve complex mass-preserving biological interactions in real-time. This is a remarkable feat that traditional non-chemically specific analytical measurement tools such as surface plasmon resonance or quartz crystal microbalance spectroscopy could not achieve. In the final part of the thesis, we develop yet another multiresonant design for broadband coverage of the whole mid-IR range. We couple our sensor to a deep learning model to resolve a dynamic biological system including all major classes of biomolecules simultaneously. Specifically, we resolve the toxic peptide-induced release of carbohydrates and nucleotides from exosome-like bionanoparticles.

Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces

Nadine Aït Bouziad, Hatice Altug, Aurélian Michel John-Herpin, Christopher David Kelly, Hilal Lashuel, Odeta Limaj, Daniel Rodrigo Lopez, Andreas Tittl

A multitude of biological processes are enabled by complex interactions between lipid membranes and proteins. To understand such dynamic processes, it is crucial to differentiate the constituent biomolecular species and track their individual time evolution without invasive labels. Here, we present a label-free mid-infrared biosensor capable of distinguishing multiple analytes in heterogeneous biological samples with high sensitivity. Our technology leverages a multi-resonant metasurface to simultaneously enhance the different vibrational fingerprints of multiple biomolecules. By providing up to 1000-fold near-field intensity enhancement over both amide and methylene bands, our sensor resolves the interactions of lipid membranes with different polypeptides in real time. Significantly, we demonstrate that our label-free chemically specific sensor can analyze peptide-induced neurotransmitter cargo release from synaptic vesicle mimics. Our sensor opens up exciting possibilities for gaining new insights into biological processes such as signaling or transport in basic research as well as provides a valuable toolkit for bioanalytical and pharmaceutical applications.

2018

New support vector algorithms for multicategorical data applied to real-time object recognition

Silvio Borer

Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithms to learn from visual data is a difficult task. More than fifty years ago, Rosenblatt proposed the perceptron algorithm. The perceptron learns from data examples a linear separation, which categorises the data in two classes. The algorithm served as a simple model of neuronal learning. Two further important ideas were added to the perceptron. First, to look for a maximal margin of separation. Second, to separate the data in a possibly high dimensional feature space, related nonlinearly to the initial space of the data, and allowing nonlinear separations. Important is that learning in the feature space can be performed implicitly and hence efficiently with the use of a kernel, a measure of similarity between two data points. The combination of these ideas led to the support vector machine, an efficient algorithm with high performance. In this thesis, we design an algorithm to learn the categorisation of data into multiple classes. This algorithm is applied to a real-time vision task, the recognition of human faces. Our algorithm can be seen as a generalisation of the support vector machine to multiple classes. It is shown how the algorithm can be efficiently implemented. To avoid a large number of small but time consuming updates of the variables limited accuracy computations are used. We prove a bound on the accuracy needed to find a solution. The proof motivates the use of a heuristic, which further increases efficiency. We derive a second implementation using a stochastic gradient descent method. This implementation is appealing as it has a direct interpretation and can be used in an online setting. Conceptually our approach differs from standard support vector approaches because examples can be rejected and are not necessarily attributed to one of the categories. This is natural in the context of a vision task. At any time, the sensory input can be something unseen before and hence cannot be recognised. Our visual data are images acquired with the recently developed adaptive vision sensor from CSEM. The vision sensor has two important features. First, like the human retina, it is locally adaptive to light intensity. Hence, the sensor has a high dynamic range. Second, the image gradient is computed on the sensor chip and is thus available directly from the sensor in real time. The sensor output is time encoded. The information about a strong local contrast is transmitted rst and the weakest contrast information at the end. To recognise faces, possibly moving in front of the camera, the sensor images have to be processed in a robust way. Representing images to exhibit local invariances is a common yet unsolved problem in computer vision. We develop the following representation of the sensor output. The image gradient information is decomposed into local histograms over contrast intensity. The histograms are local in position and direction of the gradient. Hence, the representation has local invariance properties to translation, rotation, and scaling. The histograms can be efficiently computed because the sensor output is already ordered with respect to the local contrast. Our support vector approach for multicategorical data uses the local histogram features to learn the recognition of faces. As recognition is time consuming, a face detection stage is used beforehand. We learn the detection features in an unsupervised manner using a specially designed optimisation procedure. The combined system to detect and recognise faces of a small group of individuals is efficient, robust, and reliable.

EPFL2003

Surface plasmon resonance sensor showing enhanced sensitivity for CO2 detection in the mid-infrared range

Andrea Crottini, Jérôme Faist, Sylvain Herminjard, Hans Peter Herzig, Lorenzo Sirigu

We present the first optical sensor based on Surface Plasmon Resonance (SPR) operating in the mid-infrared range. The experimental setup is based on a Kretschmann geometry with Ti/Au layers deposited on a CaF2 prism where light excitation is provided by a Quantum Cascade Laser (QCL) source. Evidence of SPR is presented and the sensing capability of the system is demonstrated by using CO2 and N2 mixtures as test samples. Due to the absorption of CO2 at this wavelength, it is shown that the sensitivity of this configuration is five times higher than a similar SPR sensor operating in the visible range of the spectrum. © 2008 Optical Society of America OCIS codes: (240.6680) Surface plasmons; (280.4788) Optical sensing and sensors; (040.3060) Infrared; (140.5965) Semiconductor lasers, quantum cascade; (250.5403) Plasmonics

2009

New support vector algorithms for multicategorical data applied to real-time object recognition

Silvio Borer

EPFL2003