Machine learning toward high-performance electrochemical sensors.

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

Renewable Solid Electrodes in Microfluidics: Recovering the Electrochemical Activity without Treating the Surface.

The contamination, passivation, or fouling of the detection electrodes is a serious problem undermining the analytical performance of electroanalytical devices. The methods to regenerate the electrochemical activity of the solid electrodes involve mechanical, physical, or chemical surface treatments that usually add operational time, complexity, chemicals, and further instrumental requirements to the analysis. In this paper, we describe for the first time a reproducible method for renewing solid electrodes whenever their morphology or composition are nonspecifically changed without any surface treatment. These renewable electrodes are the closest analogue to the mercury drop electrodes. Our approach was applied in microfluidics, where the downsides related to nonspecific modifications of the electrode are more critical. The renewal consisted in manually sliding metal-coated microwires across a channel with the sample. For this purpose, the chip was composed of a single piece of polydimethylsiloxane (PDMS) with three parallel channels interconnected to one perpendicular and top channel. The microwires were inserted in each one of the parallel channels acting as working, counter, and pseudoreference electrodes for voltammetry. This assembly allowed the renewal of all the three electrodes by simply pulling the microwires. The absence of any interfaces in the chips and the elastomeric nature of the PDMS allowed us to pull the microwires without the occurrence of leakages for the electrode channels even at harsh flow rates of up to 40.0 mL min-1. We expect this paper can assist the researchers to develop new microfluidic platforms that eliminate any steps of electrode cleaning, representing a powerful alternative for precise and robust analyses to real samples.

Scalable and green formation of graphitic nanolayers produces highly conductive pyrolyzed paper toward sensitive electrochemical sensors.

While pyrolyzed paper (PP) is a green and abundant material that can provide functionalized electrodes with wide detection windows for a plethora of targets, it poses long-standing challenges against sensing assays such as poor electrical conductivity, with resistivities generally higher than 200.0 mΩ cm (e.g., gold and silver show resistivities 1000-fold lower, ∼0.2 mΩ cm). In this regard, the fundamental hypothesis that drives this work is whether a scalable, cost-effective, and eco-friendly strategy is capable of significantly reducing the resistivity of PP electrodes toward the development of sensitive electrochemical sensors, whether faradaic or capacitive. We address this hypothesis by simply annealing PP under an isopropanol atmosphere for 1 h, reaching resistivities as low as 7 mΩ cm. Specifically, the annealing of PP at 800 or 1000 °C under isopropanol vapor leads to the formation of a highly graphitic nanolayer (∼15 nm) on the PP surface, boosting conductivity as the delocalization of π electrons stemming from carbon sp2 is favored. The reduction of carbonyl groups and the deposition of dehydrated isopropanol during the annealing process are hypothesized herein as the dominant PP graphitization mechanisms. Electrochemical analyses demonstrated the capability of the annealed PP to increase the charge-transfer kinetics, with the optimum heterogeneous standard rate constant being roughly 3.6 × 10-3 cm s-1. This value is larger than the constants reported for other carbon electrodes and indium tin oxide. Furthermore, freestanding fingers of the annealed PP were prototyped using a knife plotter to fabricate impedimetric on-leaf electrodes. These wearable sensors ensured the real-time and in situ monitoring of the loss of water content from soy leaves, outperforming non-annealed electrodes in terms of reproducibility and sensitivity. Such an application is of pivotal importance for precision agriculture and development of agricultural inputs. This work addresses the foundations for the achievement of conductive PP in a scalable, low-cost, simple, and eco-friendly way, i.e. without producing any liquid chemical waste, providing new opportunities to translate PP-based sensitive electrochemical devices into practical use.

Real-Time and In Situ Monitoring of the Synthesis of Silica Nanoparticles.

The real-time and in situ monitoring of the synthesis of nanomaterials (NMs) remains a challenging task, which is of pivotal importance by assisting fundamental studies (e.g., synthesis kinetics and colloidal phenomena) and providing optimized quality control. In fact, the lack of reproducibility in the synthesis of NMs is a bottleneck against the translation of nanotechnologies into the market toward daily practice. Here, we address an impedimetric millifluidic sensor with data processing by machine learning (ML) as a sensing platform to monitor silica nanoparticles (SiO2NPs) over a 24 h synthesis from a single measurement. The SiO2NPs were selected as a model NM because of their extensive applications. Impressively, simple ML-fitted descriptors were capable of overcoming interferences derived from SiO2NP adsorption over the signals of polarizable Au interdigitate electrodes to assure the determination of the size and concentration of nanoparticles over synthesis while meeting the trade-off between accuracy and speed/simplicity of computation. The root-mean-square errors were calculated as ∼2.0 nm (size) and 2.6 × 1010 nanoparticles mL-1 (concentration). Further, the robustness of the ML size descriptor was successfully challenged in data obtained along independent syntheses using different devices, with the global average accuracy being 103.7 ± 1.9%. Our work advances the developments required to transform a closed flow system basically encompassing the reactional flask and an impedimetric sensor into a scalable and user-friendly platform to assess the in situ synthesis of SiO2NPs. Since the sensor presents a universal response principle, the method is expected to enable the monitoring of other NMs. Such a platform may help to pave the way for translating "sense-act" systems into practice use in nanotechnology.

Bifunctional Metal Meshes Acting as a Semipermeable Membrane and Electrode for Sensitive Electrochemical Determination of Volatile Compounds.

The monitoring of toxic inorganic gases and volatile organic compounds has brought the development of field-deployable, sensitive, and scalable sensors into focus. Here, we attempted to meet these requirements by using concurrently microhole-structured meshes as (i) a membrane for the gas diffusion extraction of an analyte from a donor sample and (ii) an electrode for the sensitive electrochemical determination of this target with the receptor electrolyte at rest. We used two types of meshes with complementary benefits, i.e., Ni mesh fabricated by robust, scalable, and well-established methods for manufacturing specific designs and stainless steel wire mesh (SSWM), which is commercially available at a low cost. The diffusion of gas (from a donor) was conducted in headspace mode, thus minimizing issues related to mesh fouling. When compared with the conventional polytetrafluoroethylene (PTFE) membrane, both the meshes (40 µm hole diameter) led to a higher amount of vapor collected into the electrolyte for subsequent detection. This inedited fashion produced a kind of reverse diffusion of the analyte dissolved into the electrolyte (receptor), i.e., from the electrode to bulk, which further enabled highly sensitive analyses. Using Ni mesh coated with Ni(OH)2 nanoparticles, the limit of detection reached for ethanol was 24-fold lower than the data attained by a platform with a PTFE membrane and placement of the electrode into electrolyte bulk. This system was applied in the determination of ethanol in complex samples related to the production of ethanol biofuel. It is noteworthy that a simple equation fitted by machine learning was able to provide accurate assays (accuracies from 97 to 102%) by overcoming matrix effect-related interferences on detection performance. Furthermore, preliminary measurements demonstrated the successful coating of the meshes with gold films as an alternative raw electrode material and the monitoring of HCl utilizing Au-coated SSWMs. These strategies extend the applicability of the platform that may help to develop valuable volatile sensing solutions.

Turbulence in microfluidics: Cleanroom-free, fast, solventless, and bondless fabrication and application in high throughput liquid-liquid extraction.

This paper addresses an important breakthrough in the deployment of ultra-high adhesion strength microfluidic technologies to provide turbulence at harsh flow rate conditions. This paper is only, to our knowledge, the second reporting on the generation of high flow rate-assisted turbulence in microchannels. This flow solves a crucial bottleneck in microfluidics: the generation of high throughput homogeneous mixings. We focused on the fabrication of bulky polydimethylsiloxane (PDMS) microchips (without any interfaces) rather than the laborious surface modifications that were employed in the first reporting about turbulence-assisted microfluidics. The fabrication is cleanroom-free, simple, low-cost, fast, solventless, and bondless requiring only a laboratory oven. More specifically, our method relies on the shaping of a nylon scaffold, cure of PDMS with embedded nylon, and removal of this scaffold. The scaffold was obtained by manually wrapping nylon threads. The withdrawing out of the scaffold was completed in few seconds using only a plier. Such microchannels endured flow rates of up to 60.0 mL min(-1) with a strikingly low elastic deformation. The importance in producing turbulence into microscale channels was successfully shown in liquid-liquid extractions. The great energy dissipation rate relative to the turbulence created high throughput and efficient extractions in microfluidics for the first time. The residence time was only 0.01 s at 25.0 mL min(-1) (total flow rate of the immiscible phases). In addition, the partition coefficient determined in a single run was similar to that obtained by the conventional batch shake-flask method that was realized in triplicate.