Mandresy Ivan Ny Hanitra

Biographie

Ivan received in 2013 a bachelor degree in Physics and Chemistry from Grenoble Institute of Technology (France), after two years of intensive training at Lycée Pierre de Fermat (France). He currently holds a master degree in Micro and Nanotechnologies for Integrated Systems, a joint degree between Politecnico di Torino (Italy), Grenoble Institute of Technology (France), and EPFL. During this international training, he acquired knowledge in Semiconductor physics and technology, Microtechnology (cleanroom practice), Microelectronics (analog/digital circuits design), Nanoelectronics (state-of-the art of emerging electron devices), Micro-nano systems modelling, VLSI design and testing, Embedded systems, and Optoelectronics. He pursued his master thesis at EPFL, in Electronics Laboratory (Prof. Kayal), developing low-power systems for photoplethysmographic-based heart rate monitoring. Ivan joined the Integrated Systems Laboratory (Prof. De Micheli), in October 2016, where he designed and realized in cleanroom multi-sensing platforms for electrochemical sensing applications.

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https://people.epfl.ch/ivan.nyhanitra?lang=fr

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All-Solid-State Ion-Selective Electrodes: A Tutorial for Correct Practice

Sandro Carrara, Francesca Criscuolo, Giovanni De Micheli, Mandresy Ivan Ny Hanitra, Irene Taurino

The research on ion-measuring devices has seen a rapid expansion in the last years as a consequence of the growing interest in wearable sensors for biofluids analysis and in portable devices for remote or in-line water and food quality monitoring. As a result, an increasing number of researchers is approaching the field of ion-selective sensors. Despite the apparently simple transduction principle, the theory behind their working mechanism is far from trivial and a correct understanding is necessary for an optimal exploitation of the technology. In current literature, imprecise characterization procedures lead to the definition of misleading sensors parameters, which cannot be effectively used for comparisons. In fact, some unique definitions and procedures applies to this category of sensors, which significantly differ from the traditional ones applied in sensing research. This tutorial aims at highlighting the basic thermodynamic theory and the correct experimental practice for the accurate and reproducible characterization of potentiometric ion-sensors. The most important requirements and design considerations on hardware and software interfaces will also be discussed to give a complete overview of the various aspects of current technology to people approaching this promising area of sensing.

2021

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Hardware and Software Interfaces Design for Multi-Panel Electrochemical Sensors

Mandresy Ivan Ny Hanitra

The exponential growth of wearable healthcare devices market is fostered by the internetof-things (IoT) era. Connected smart biosensors enable a decentralized healthcare thatdoes not constrain the user to be in a medical facility to get a real-time insight on hishealth status and a medical diagnosis from a doctor. Moreover, remote physiologicalmonitoring is appealing in sport applications where athletes need real-time feedback ontheir level of dehydration and muscle fatigue in order to optimize their performances.Electrochemical sensors play a crucial role in physiology and healthcare monitoring sincethey provide information at molecular level, where the biosensor is in direct contactwith bodily fluids such as sweat. A comprehensive healthcare diagnosis is achieved bycontinuously monitoring several types of biomarkers because of correlations betweenbiological compounds. Namely, endogenous metabolites such as lactate, or potassium andammonium ions, enable the quantification of muscle fatigue, hence, preventing musclecramping. In therapeutic drug monitoring, exogenous compounds are continuously trackedso that the drug is maintained in its therapeutic range, in order to always be effectiveand not toxic for the patient. Besides multi-sensing and general-purpose capabilities,electrochemical platforms need to be correlated to the health and physiological statusof the user, where the large amount of measured biological data must be accuratelyprocessed and interpreted by smart data analytic tools.This thesis covers the design, implementation, characterization, and validation of hardwareand software interfaces for multi-panel electrochemical sensing platforms.A multi-mode hardware front-end enabling voltammetric and potentiometric measurementsis designed to provide a continuous and concurrent monitoring of endogenousmetabolites, drugs, and electrolytes. This versatile and multi-sensing platform offers aportable solution for remote and comprehensive healthcare monitoring.Moreover, a multi-ion-sensing front-end is designed for accurate physiology in sweatsensingapplications. The hardware is proposed as a solution for multiple electrolytedetection in artificial sweat samples. In such complex media, multi-ion-sensors are subjectto interference from background electrolytes that considerably distorts sensor response.Therefore, a compact and analytical model of ion-sensing transduction mechanism isproposed to understand both qualitatively and quantitatively the non-linearity inducedby these artifacts. The ion-sensor model is implemented at the core of an emulator ofsynthetic datasets that is built to simulate ion-sensor responses in artificial sweat samples.The emulator addresses the expensive time and chemical resources needed to acquire largedatabase for training multivariate calibration models. Thus, the emulated data is used forthe training and optimization of a multi-output support vector regressor that is proposedas an accurate, unbiased, robust, compact, low-complexity, and low-latency estimatorfor the multivariate calibration of multi-ion-sensors. Then, the multi-ion-sensing array,the analog front-end interface, and the chemometric model deployed on a RaspberryPi, are seamlessly co-integrated for the monitoring of sodium, potassium, ammonium,and calcium ions in artificial sweat, within an IoT framework for real-time and accuratephysiology.

EPFL2021

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One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental data is time and resource consuming in practice, necessitating new paradigms and efcient models for these data-limited frameworks. Therefore, a novel approach is presented in this work, where a multi-ion-sensing emulator is designed to explain the response of an ion-sensing array in a mixed-ion environment. A case study is performed emulating the concurrent monitoring of sodium, potassium, lithium, and lead ions, in a medium representative of sweat samples. These analytes are relevant examples of sweat ion-sensing applications for physiology, therapeutic drug monitoring, and heavy metal contamination. It is demonstrated that calibration datasets output by the emulator explain accurately the experimental response of polymeric solid-contact ion-selective electrodes, where root-meansquared error of 1:37, 1:44, 1:78, 2mV are obtained, respectively, for NaC, KC, LiC, Pb2C sensor calibration in articial sweat. Besides, synthetic datasets of custom size are generated to train, validate, and evaluate different types of multivariate regressors. A Multi-Output Support Vector Regressor (M-SVR) is proposed as a compact, accurate, robust, and efcient multivariate calibration model. It features 13:22% normalized root mean squares, and 20:29% mean root squares improvement compared to a simple linear regression model. It is an unbiased estimator for medium to large datasets, and its average generalization error is of 3:22%. Besides, M-SVR models have a lower computational complexity than single-output SVR or neural network models, making them a suitable solution for memory and energy-constrained edge devices used for continuous and real-time multi-ion monitoring.

2021

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Hardware and Software Interfaces Design for Multi-Panel Electrochemical Sensors

Mandresy Ivan Ny Hanitra

EPFL2021