Search

Despite the potentialities of electrochemical sensors, these devices still encounter challenges in devising high-throughput and accurate drug susceptibility testing. The lack of platforms for providing these analyses over the preclinical trials of drug candidates remains a significant barrier to developing medicines. In this way, ultradense electrochemical chips are combined with machine learning (ML) to enable high-throughput, user-friendly, and accurate determination of the viability of 2D tumor cells (breast and colorectal) aiming at drug susceptibility assays. The effect of doxorubicin (anticancer drug model) was assessed through cell detachment electrochemical assays by interrogating Ru(NH 3 ) 6 3+ with square wave voltammetry (SWV). This positive probe is presumed to imply sensitive monitoring of the on-sensor cellular death because of its electrostatic preconcentration in the so-called nanogap zone between the electrode surface and adherent cells. High-throughput assays were obtained by merging fast individual SWV measurements (9 s) with the ability of chips to yield analyses of Ru(NH 3 ) 6 3+ in series. The approach's applicability was demonstrated across two analysis formats, drop-casting and microfluidic assays. One should also mention that fitting a multivariate descriptor from selected input data via ML proved to be essential to providing accurate determinations (98 to 104%) of cell viability and half-maximal lethal concentration of the drug. The achieved results underscore the potential of the method in steering electrochemical sensors toward enabling high-throughput drug screening in practical applications.

High-throughput sensors are valuable tools for enabling massive, fast, and accurate diagnostics. To yield this type of electrochemical device in a simple and low-cost way, high-density arrays of vertical gold thin-film microelectrode-based sensors are demonstrated, leading to the rapid and serial interrogation of dozens of samples (10 μL droplets). Based on 16 working ultramicroelectrodes (UMEs) and 3 quasi-reference electrodes (QREs), a total of 48 sensors were engineered in a 3D crossbar arrangement that devised a low number of conductive lines. By exploiting this design, a compact chip (75 × 35 mm) can enable performing 16 sequential analyses without intersensor interferences by dropping one sample per UME finger. In practice, the electrical connection to the sensors was achieved by simply switching the contact among WE adjacent fingers. Importantly, a short analysis time was ensured by interrogating the UMEs with chronoamperometry or square wave voltammetry using a low-cost and hand-held one-channel potentiostat. As a proof of concept, the detection of Staphylococcus aureus in 15 samples was performed within 14 min (20 min incubation and 225 s reading). Additionally, the implementation of peptide-tethered immunosensors in these chips allowed the screening of COVID-19 from patient serum samples with 100% accuracy. Our experiments also revealed that dispensing additional droplets on the array (in certain patterns) results in the overestimation of the faradaic current signals, a phenomenon referred to as crosstalk. To address this interference, a set of analyses was conducted to design a corrective strategy that boosted the testing capacity by allowing using all on-chip sensors to address subsequent analyses (i.e., 48 samples simultaneously dispensed on the chip). This strategy only required grounding the unused rows of QRE and can be broadly adopted to develop high-throughput UME-based sensors. In practice, we could analyze 48 droplets (with [Fe(CN) 6 ] 4 - ) within ∼8 min using amperometry.

The sensing field has shed light on an urgent necessity for field-deployable, user-friendly, sensitive, and scalable platforms that are able to translate solutions into the real world. Here, we attempt to meet these requests by addressing a simple, low-cost, and fast electrochemical approach to provide sensitive assays that consist of dropping a small volume (0.5 μL) of off-the-shelf alcohols on pyrolyzed paper-based electrodes before adding the sample (150 μL). This method was applied in the detection of phosphate after the formation of the phosphomolybdate complex (250-860 nm in size). Prior drops of isopropanol allow for the fast penetration of the sample through pores of this hydrophobic paper, delivering hindrance-free redox reactions across increasing active areas and ultimately improving the detection performance. The sensitivity (-1.9 10 -6 mA cm -2 ppb -1 ) and limit of detection (1.1 ppb) were improved, respectively, by factors of 33 and 99 over the data achieved without the addition of isopropanol, listing among the lowest values when compared with those results reported in the literature for phosphate (expressed in terms of the concentration of phosphorus). The approach enabled the quantification of this analyte in real samples with accuracies ranging from 87 to 103%. Furthermore, preliminary measurements demonstrated the successful performance of the electrodes with prior addition of other widely used alcohols, that is, methanol and ethanol. These results may extend the applicability of the method. In special, the scalability and eco-friendly character of the electrode fabrication combined with the sensitivity and simplicity of the analyses make the developed platform a promising alternative that may help to pave the way for a new generation of disposable sensors toward the daily monitoring of phosphate in water samples, thus contributing to prevent ecological side effects.

This work describes the development of an electronic nose (e-nose) based on carbon nanocomposites to detect clove essential oil (CEO), eugenol (EUG), and eugenyl acetate (EUG.ACET). Our e-nose system comprises an array of six sensing units modified with nanocomposites of poly(aniline), graphene oxide, and multiwalled carbon nanotubes doped with different acids, dodecyl benzene sulfonic acid, camphorsulfonic acid, and hydrochloric acid. The e-nose presented an excellent analytical performance to the detected analytes (CEO, EUG, and EUG.ACET) with high sensitivity and reversibility. The limit of detection was lower than 1.045 ppb, with response time (<13.26 s) and recovery time (<106.29 s) and low hysteresis. Information visualization methods (PCA and IDMAP) demonstrated that the e-nose was efficient to discriminate the different concentrations of analyte volatile oil compounds. PM-IRRAS measurements suggest that the doping mechanism of molecular architectures is composed of a change in the oscillation energy of the characteristic dipoles and changes in the molecular orientation dipoles C═C and C═O at 1615 and 1740 cm -1 , respectively. The experimental results indicate that our e-nose system is promising for a rapid analysis method to monitor the quality of essential oils.

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 2 , NiO 2 , Al 2 O 3 , and Fe 2 O 3 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.

Electronic tongues (e-tongues) are promising analytical devices for a variety of applications to address the challenges of quality control in water monitoring and industries of foods, beverages, and pharmaceuticals. A crucial drawback in the current e-tongues is the need to recalibrate the device when one or more sensing units (usually with modified surface) are replaced. Another downside is the necessity to perform subsequent surface modifications and analyses to each of the diverse sensing units, undermining the simplicity and velocity of the method. These features have prevented widespread commercial use of the e-tongues. In this paper, we introduce a microfluidic e-tongue that overcomes all such limitations. The key principle of global selectivity of the e-tongue was achieved by recording only a single response, namely, the equivalent admittance spectrum of an association of resistors in parallel. Such resistors consisted of five nonfunctionalized stainless steel microwires (sensing units), which were short-circuited and coated with gold, platinum, nickel, iron, and aluminum oxide films. The microwires were inserted in a chip composed of a single piece of polydimethylsiloxane (PDMS). Using impedance spectroscopy, the e-tongue was successfully applied in classification of basic tastes at a concentration below the threshold for the human tongue. In addition, our chip allowed the distinction of various chemicals used in oil industry. Finally, our cleanroom-free prototyping allows the mass production of chips with easily replaceable and reproducible sensing units. Hence, one can now envisage the widespread dissemination of e-tongues with fast and reproducible data.