Monitoring the Surface Chemistry of Functionalized Nanomaterials with a Microfluidic Electronic Tongue

Abstract

Advances in nanomaterials have led to tremendous progress in different areas with the development of high performance and multifunctional platforms. However, a relevant gap remains in providing the mass-production of these nanomaterials with reproducible surfaces. Accordingly, the monitoring of such materials across their entire life cycle becomes mandatory to both industry and academy. In this paper, we use a microfluidic electronic tongue (e-tongue) as a user-friendly and cost-effective method to classify nanomaterials according to their surface chemistry. The chip relies on a new single response e-tongue with association of capacitors in parallel, which consisted of stainless steel microwires coated with SiO, NiO, AlO, and FeO thin films. Utilizing impedance spectroscopy and a multidimensional projection technique, the chip was sufficiently sensitive to distinguish silica nanoparticles and multiwalled carbon nanotubes dispersed in water in spite of the very small surface modifications induced by distinct functionalization and oxidation extents, respectively. Flow analyses were made acquiring the analytical readouts in a label-free mode. The device also allowed for multiplex monitoring in an unprecedented way to speed up the tests. Our goal is not to replace the traditional techniques of surface analysis, but rather propose the use of libraries from e-tongue data as benchmark for routine screening of modified nanomaterials in industry and academy.

Polysaccharide Multilayer Films in Sensors for Detecting Prostate Tumor Cells Based on Hyaluronan-CD44 Interactions

“…The limitation mentioned here associated with the limited selectivity can be addressed with computational methods, such as the multidimensional projection techniques employed here or machine learning methods for classification [33,45] for even more demanding tasks in which non-specific adsorption is likely to be a problem. Furthermore, one may utilize a sensing array rather than just one sensing unit, e.g., with different numbers of bilayers in the LbL films, as in the concept of electronic tongues that can also be used in biosensing [46,47]. With such strategies, it will be possible to extend the use of HA-containing films to capture other types of tumor cells when there is overexpression of CD44 receptors.…”

Use of data processing for rapid detection of the prostate-specific antigen biomarker using immunomagnetic sandwich-type sensors

“…The distinction of real samples is certainly challenging when various samples are analyzed, and false positives may occur. This has prompted researchers to use statistical and computational methods to treat the sensing data [31], in some cases with conjunction with machine learning approaches [31,59]. Here we employed multidimensional projection techniques based on linear and nonlinear multidimensional scaling (MDS) approaches such as principal component analysis (PCA) [60], Least squares projection (LSP) [61], Sammon’s mapping (SM) [62] and interactive document mapping (IDMAP) [63] implemented in the software as projection explorer sensors (PEx-Sensors) [32,64].…”

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

“…Such an integration makes this approach promising for future eHealth systems with computer-aided diagnosis, where machine learning, big data, and IoT technologies should converge with biosensing capabilities. In particular, biosensing applications are being explored with e-tongues by functionalizing the building nanolayers with biomolecules to control the specificity in the interactions with the analyte under study [68][69][70][71][72][73][74][75][76]. This latter application can be used to collect huge amounts of patient's biological data to feed machine learning algorithms, and then continuously convert them into knowledge (through big data and machine learning methods).…”