Real-Time Multi-Ion-Monitoring Front-End With Interference Compensation by Multi-Output Support Vector Regressor.

Ion-sensors play a major role in physiology and healthcare monitoring since they are capable of continuously collecting biological data from body fluids. Nevertheless, ion interference from background electrolytes present in the sample is a paramount challenge for a precise multi-ion-monitoring. In this work, we propose the first system combining a battery-powered portable multi-channel electronic front-end, and an embedded Multi-output Support Vector Regressor (M-SVR), that supplies an accurate, continuous, and real-time monitoring of sodium, potassium, ammonium, and calcium ions. These are typical analytes tracked during physical exercise. The front-end interface was characterized through a sensor array built with screen-printed electrodes. Nernstian sensitivity and limit of detection comparable to a bulky laboratory potentiometer were achieved in both water and artificial sweat. The multivariate calibration model was deployed on a Raspberry Pi where the activity of the target ions were locally computed. The M-SVR model was trained, optimized, and tested on an experimental dataset acquired following a design of experiments. We demonstrate that the proposed multivariate regressor is a compact, low-complexity, accurate, and unbiased estimator of sodium and potassium ions activity. A global normalized root mean-squared error improvement of 6.97%, and global mean relative error improvement of 10.26%, were achieved with respect to a standard Multiple Linear Regressor (MLR). Within a real-time multi-ion-monitoring task, the overall system enabled the continuous monitoring and accurate determination of the four target ions activity, with an average accuracy improvement of 27.73% compared to a simple MLR, and a prediction latency of [Formula: see text].

Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier.

We present a new method for electrochemical sensing, which compensates the fouling effect of propofol through machine learning (ML) model. Direct and continuous monitoring of propofol is crucial in the development of automatic systems for control of drug infusion in anaesthesiology. The fouling effect on electrodes discourages the possibility of continuous online monitoring of propofol since polymerization of the surface produces sensor drift. Several approaches have been proposed to limit the phenomenon at the biochemical interface; instead, here, we present a novel ML-based calibration procedure. In this paper, we analyse a dataset of 600 samples acquired through staircase cyclic voltammetry (SCV), resembling the scenario of continuous monitoring of propofol, both in PBS and in undiluted human serum, to demonstrate that ML-based model solves electrode fouling of anaesthetics. The proposed calibration approach is based on Gaussian radial basis function support vector classifier (RBF-SVC) that achieves classification accuracy of 98.9% in PBS, and 100% in undiluted human serum. The results prove the ability of the ML-based model to correctly classify propofol concentration in the therapeutic range between 1µM and 60µM with levels of 10µM, continuously up to ten minutes, with one sample every 30s.

New Approach for Making Standard the Development of Biosensing Devices by a Modular Multi-Purpose Design.

The fast widening of biosensing applications, such as healthcare, drug delivery, food, and military industries, is increasing the need for generality and compatibility among different sensors. To address this challenge, we present here an innovative approach for the fast development of new electronic biosensing systems, linking a custom-designed front-end with a multi-purpose system. We envision an open tool to help designers to focus on the target molecule and related detection method instead of designing each time a dedicated electronic device. The architecture of the proposed system is based on a modular approach, where only the front-end and the software need to be custom re-designed according to the application. Considering current research and applying a rigorous definition of the technical requirements, the core of the system is designed to fit the highest number of biosensing methods. The flexibility of this approach is successfully demonstrated with three different types of biosensors, i.e., amperometric, ion-sensitive, and memristive.

Fast Procedures for the Electrodeposition of Platinum Nanostructures on Miniaturized Electrodes for Improved Ion Sensing.

Nanostructured materials have attracted considerable interest over the last few decades to enhance sensing capabilities thanks to their unique properties and large surface area. In particular, noble metal nanostructures offer several advantages including high stability, non-toxicity and excellent electrochemical behaviour. However, in recent years the great expansion of point-of-care (POC) and wearable systems and the attempt to perform measurements in tiny spaces have also risen the need of increasing sensors miniaturization. Fast constant potential electrodeposition techniques have been proven to be an efficient way to obtain conformal platinum and gold nanostructured layers on macro-electrodes. However, this technique is not effective on micro-electrodes. In this paper, we investigate an alternative one-step deposition technique of platinum nanoflowers on micro-electrodes by linear sweep voltammetry (LSV). The effective deposition of platinum nanoflowers with similar properties to the ones deposited on macro-electrodes is confirmed by morphological analysis and by the similar roughness factor (~200) and capacitance (~18 µ F/mm 2 ). The electrochemical behaviour of the nanostructured layer is then tested in an solid-contact (SC) L i + -selective micro-electrode and compared to the case of macro-electrodes. The sensor offers Nernstian calibration with same response time (~15 s) and a one-order of magnitude smaller limit of detection (LOD) ( 2.6 × 10 - 6 ) with respect to the macro-ion-selective sensors (ISE). Finally, sensor reversibility and stability in both wet and dry conditions is proven.

A novel electrochemical sensor for non-invasive monitoring of lithium levels in mood disorders.

Lithium is the main drug for the treatment of mood disorders. Due to its narrow therapeutic window, Therapeutic Drug Monitoring (TDM) is a norm during therapy in order to avoid adverse effects. Consequently, patients are obliged to frequent check-ups in hospitals to determine their serum concentration and optimize accordingly the drug dose. Wearable sensors have attracted a growing interest in the research community in recent years owing to their promising impact in personalized healthcare. In particular, sweat diagnosis has seen an enormous expansion and is currently entering the market thanks to the large availability and simple collection of this fluid. In this paper a novel approach for non-invasive decentralized monitoring of lithium drug concentration through sweat analysis is proposed for the first time. An all-solid-state Ion- Selective Electrode (ISE) with a nanostructured Solid-Contact (SC) is used to detect lithium ions in sweat. The sensor offers near-Nernstian behaviour (57:6±2:1 mV/decade) in the concentration range of interest. In addition, it shows fast response (15-30 s), good reversibility and small potential drift over time. A wide pH stability window (pH 4-12) is also proved.

Highly-stable Li+ ion-selective electrodes based on noble metal nanostructured layers as solid-contacts.

Nowadays the development of stable and highly efficient Solid-Contact Ion-Selective Electrodes (SC-ISEs) attracts much attention in the research community because of the great expansion of portable analytical devices. In this work, we present highly stable Li+ all-solid-state ISEs exploiting noble metals nanostructures as ion-to-electron transducers. The detection of lithium is essential for therapeutic drug monitoring of bipolar patients. In addition, greater environmental exposure to this ion is occurring due to the large diffusion of lithium-ion batteries. However, only a limited number of SC Li+ ISEs already exists in literature based on Conductive Polymers (CPs) and carbon nanotubes. The use of noble metals for ion-to-electron transduction offers considerable advantages over CPs and carbon materials, including fast and conformal one-step deposition by electrochemical means, non-toxicity and high stability. We investigate for the first time the use of gold nanocorals obtained by means of a one-step electrodeposition process to improve sensor performance and we compare it to all-solid-state ISEs based on electrodeposited platinum nanoflowers. In addition, the effect of substrate electrode material, membrane thickness and conditioning concentration on the potentiometric response is carefully analysed. Scanning Electron Microscopy (SEM) and Current Reversal Chronopotentiometry (CRC) techniques are used to characterize the morphology and the electrochemical behaviour of the different ISEs. The use of nanostructured gold and platinum contacts allows the increase of the SC capacitance by one or two orders of magnitude, respectively, with respect to the flat metal, while the SC resistance is significantly reduced. We show that the microfabricated sensors offer Nernstian behaviour (58.7±0.8 mV/decade) in the activity range from 10-5 to 0.1 M, with short response time (∼15 s) and small potential drift during CRC measurements (dEdt=3×10-5±2×10-5 V/s). The exceptional response stability is verified also when no potential is applied. The sensor shows high selectivity towards all clinically important ions, with values very similar to conventional ISEs. Furthermore, to our knowledge, the selectivity towards Ca+2 is the best ever reported for SC-ISEs. In conclusion, the present study opens up new interesting perspectives towards the development of simple and reproducible fabrication protocols to obtain high-quality and high-stability all-solid-state ISEs.