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

Carbon nanotube-cellulose ink for rapid solvent identification

In this work, a conductive ink based on microfibrillated cellulose (MFC) and multiwalled carbon nanotubes (MWCNTs) was used to produce transducers for rapid liquid identification. The transducers are simple resistive devices that can be easily fabricated by scalable printing techniques. We monitored the electrical response due to the interaction between a given liquid with the carbon nanotube-cellulose film over time. Using principal component analysis of the electrical response, we were able to extract robust data to differentiate between the liquids. We show that the proposed liquid sensor can classify different liquids, including organic solvents (acetone, chloroform, and different alcohols) and is also able to differentiate low concentrations of glycerin in water (10-100 ppm). We have also investigated the influence of two important properties of the liquids, namely dielectric constant and vapor pressure, on the transduction of the MFC-MWCNT sensors. These results were corroborated by independent heat flow measurements (thermogravimetric analysis). The proposed MFC-MWCNT sensor platform may help paving the way to rapid, inexpensive, and robust liquid analysis and identification.

Fe2O3-modified graphene oxide mitigates nanoplastic toxicity via regulating gas exchange, photosynthesis, and antioxidant system in Triticum aestivum

The ever-increasing plastic pollution in soil and water resources raises concerns about its effects on terrestrial plants and agroecosystems. Although there are many reports about the contamination with nanoplastics on plants, the presence of magneto-assisted nanomaterials enabling the removal of their adverse impacts still remains unclear. Therefore, the purpose of the current study is to evaluate the potential of nanomaterial Fe₂O₃-modified graphene oxide (FGO, 50-250 mg L^-1) to eliminate the adverse effects of nanoplastics in plants. Wheat plants exposed to polystyrene nanoplastics concentrations (PS, 10, 50 and 100 mg L^-1) showed decreased growth, water content and loss of photosynthetic efficiency. PS toxicity negatively altered gas exchange, antenna structure and electron transport in photosystems. Although the antioxidant system was partially activated (only superoxide dismutase (SOD), NADPH oxidase (NOX) and glutathione reductase (GR)) in plants treated with PS, it failed to prevent PS-triggered oxidative damage, as showing lipid peroxidation and hydrogen peroxide (H₂O₂) levels. FGOs eliminated the adverse impacts of PS pollution on growth, water status, gas exchange and oxidative stress markers. In addition, FGOs preserve the biochemical reactions of photosynthesis by actively increasing chlorophyll fluorescence parameters in the stressed-wheat leaves. The activities of all enzymatic antioxidants increased, and the H₂O₂ and TBARS contents decreased. GSH-mediated detoxifying antioxidants such as glutathione S-transferase (GST) and glutathione peroxidase (GPX) were stimulated by FGOs against PS pollution. FGOs also triggered the enzymes and non-enzymes related to the Asada-Halliwell cycle and protected the regeneration of ascorbate (AsA) and glutathione (GSH). Our findings indicated that FGO had the potential to mitigate nanoplastic-induced damage in wheat by regulating water relations, protecting photosynthesis reactions and providing efficient ROS scavenging with high antioxidant capacity. This is the first report on removing PS-induced damage by FGO applications in wheat leaves.

Water Quality Carbon Nanotube-Based Sensors Technological Barriers and Late Research Trends: A Bibliometric Analysis

Water is the key element that defines and individualizes our planet. Relative to body weight, water represents 70% or more for the majority of all species on Earth. Taking care of water as a whole is equivalent with taking care of the entire biodiversity or the whole of humanity itself. Water quality is becoming an increasingly important component of terrestrial life, hence intensive work is being conducted to develop sensors for detecting contaminants and assessing water quality and characteristics. Our bibliometric analysis is focused on water quality sensors based on carbon nanotubes and highlights the most important objectives and achievements of researchers in recent years. Due to important measurement characteristics such as sensitivity and selectivity, or low detection limit and linearity, up to the ability to measure water properties, including detection of heavy metal content or the presence of persistent organic compounds, carbon nanotube (CNT) sensors, taking advantage of available nanotechnologies, are becoming increasingly attractive. The conducted bibliometric analysis creates a visual, more efficient keystones mapping. CNT sensors can be integrated into an inexpensive real-time monitoring data acquisition system as an alternative for classical expensive and time-consuming offline water quality monitoring. The conducted bibliometric analysis reveals all connections and maps all the results in this water quality CNT sensors research field and gives a perspective on the approached methods on this specific type of sensor. Finally, challenges related to integration of other trends that have been used and proven to be valuable in the field of other sensor types and capable to contribute to the development (and outlook) for future new configurations that will undoubtedly emerge are presented.

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.

Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers

Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs (C_V) themselves and carried proteins (C_P). However, simple, high-throughput, and accurate determination of these targets remains a key challenge. Herein, we address the simultaneous monitoring of C_V and C_P from a single impedance spectrum without using recognizing elements by combining a multidimensional sensor and machine learning models. This multidetermination is essential for diagnostic accuracy because of the heterogeneous composition of EVs and their molecular cargoes both within the tumor itself and among patients. Pencil HB cores acting as electric double-layer capacitors were integrated into a scalable microfluidic device, whereas supervised models provided accurate predictions, even from a small number of training samples. User-friendly measurements were performed with sample-to-answer data processing on a smartphone. This new platform further showed the highest throughput when compared with the techniques described in the literature to quantify EVs biomarkers. Our results shed light on a method with the ability to determine multiple EVs biomarkers in a simple and fast way, providing a promising platform to translate biofluid-based diagnostics into clinical workflows.

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

The increasing need for point-of-care diagnosis has sparked the development of label-free sensing platforms, some of which are based on impedance measurements with biological cells. Here, interdigitated electrodes were functionalized with layer-by-layer (LbL) films of hyaluronan (HA) and chitosan (CHI) to detect prostatic tumor cells (PC3 line). The deposition of LbL films was confirmed with atomic force microscopy and polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS), which featured the vibrational modes of the HA top layer capable of interacting specifically with glycoprotein CD44 receptors overexpressed in tumor cells. Though the CHI/HA LbL films cannot be considered as a traditional biosensor due to their limited selectivity, it was possible to distinguish prostate tumor cells in the range from 50 to 600 cells/µL in in vitro experiments with impedance spectroscopy. This was achieved by treating the impedance data with information visualization methods, which confirmed the distinguishing ability of the films by observing the absence of false positives in a series of control experiments. The CD44–HA interactions may, therefore, be exploited in clinical analyses and point-of-care diagnostics for cancer, particularly if computational methods are used to process the data.

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

Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with "epidermal electronics", including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.

Microfluidic Mixer with Automated Electrode Switching for Sensing Applications

An electronic tongue (e-tongue) is a multisensory system usually applied to complex liquid media that uses computational/statistical tools to group information generated by sensing units into recognition patterns, which allow the identification/distinction of samples. Different types of e-tongues have been previously reported, including microfluidic devices. In this context, the integration of passive mixers inside microchannels is of great interest for the study of suppression/enhancement of sensorial/chemical effects in the pharmaceutical, food, and beverage industries. In this study, we present developments using a stereolithography technique to fabricate microfluidic devices using 3D-printed molds for elastomers exploring the staggered herringbone passive mixer geometry. The fabricated devices (microchannels plus mixer) are then integrated into an e-tongue system composed of four sensing units assembled on a single printed circuit board (PCB). Gold-plated electrodes are designed as an integral part of the PCB electronic circuitry for a highly automated platform by enabling faster analysis and increasing the potential for future use in commercial applications. Following previous work, the e-tongue sensing units are built functionalizing gold electrodes with layer-by-layer (LbL) films. Our results show that the system is capable of (i) covering basic tastes below the human gustative perception and (ii) distinguishing different suppression effects coming from the mixture of both strong and weak electrolytes. This setup allows for triplicate measurements in 12 electrodes, which represents four complete sensing units, by automatically switching all electrodes without any physical interaction with the sensor. The result is a fast and reliable data acquisition system, which comprises a suitable solution for monitoring, sequential measurements, and database formation, being less susceptible to human errors.

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

Diagnosis of cancer using electroanalytical methods can be achieved at low cost and in rapid assays, but this may require the combination with data treatment for determining biomarkers in real samples. In this paper, we report an immunomagnetic nanoparticle-based microfluidic sensor (INμ-SPCE) for the amperometric detection of the prostate-specific antigen (PSA) biomarker, the data of which were treated with information visualization methods. The INμ-SPCE consists of eight working electrodes, reference and counter electrodes. On the working electrodes, magnetic nanoparticles with secondary antibodies with the enzyme horseradish peroxidase were immobilized for the indirect detection of PSA in a sandwich-type procedure. Under optimal conditions, the immunosensor could operate within a wide range from 12.5 to 1111 fg·L-1, with a low detection limit of 0.062 fg·L-1. Multidimensional projections combined with feature selection allowed for the distinction of cell lysates with different levels of PSA, in agreement with results from the traditional enzyme-linked immunosorbent assay. The approaches for immunoassays and data processing are generic, and therefore the strategies described here may provide a simple platform for clinical diagnosis of cancers and other types of diseases.

Pencil graphite core for pattern recognition applications

A new concept of pattern sensors based on ready-to-use sensing probes has been designed towards low-cost and rapid sample recognition applications.

A Future with Ubiquitous Sensing and Intelligent Systems

In this paper, we discuss the relevance of sensing and biosensing for the ongoing revolution in science and technology as a product of the merging of machine learning and Big Data into affordable technologies and accessible everyday products. Possible scenarios for the next decades are described with examples of intelligent systems for various areas, most of which will rely on ubiquitous sensing. The technological and societal challenges for developing the full potential of such intelligent systems are also addressed.