Nonenzymatic electrochemical sensors via Cu native oxides (CuNOx) for sweat glucose monitoring

High performance nonenzymatic electrochemical sensors via thermally grown Cu native oxides (CuNOx) towards sweat glucose monitoring

Diabetes, which is the seventh leading cause of death globally, necessitates real-time blood glucose monitoring, a process that is often invasive. A promising alternative is sweat glucose monitoring, which typically uses transition metals and their oxide nanomaterials as sensors. Despite their excellent surface-to-volume ratio, these materials have some drawbacks, including poor conductivity, structural collapse, and aggregation. As a result, selecting highly electroconductive materials and optimizing their nanostructures is critical. In this work, we developed a high-performance, low-cost, nonenzymatic sensor for sweat glucose detection, using the thermally grown native oxide of copper (CuNOx). By heating Cu foil at 160, 250, and 280 °C, we grew a native oxide layer of approximately 140 nm cupric oxide (CuO), which is excellent for glucose electrocatalysis. Using cyclic voltammetry, we found that our CuNOx sensors prepared at 280 °C exhibited a sensitivity of 1795 μA mM-1 cm-2, a linear range up to the desired limit of 1.00 mM for sweat glucose with excellent linearity (R2 = 0.9844), and a lower limit of detection of 135.39 μM. For glucose sensing, the redox couple Cu(II)/Cu(III) oxidizes glucose to gluconolactone and subsequently to gluconic acid, producing an oxidation current in an alkaline environment. Our sensors showed excellent repeatability and stability (remaining stable for over a year) with a relative standard deviation (RSD) of 2.48% and 4.17%, respectively, for 1 mM glucose. The selectivity, when tested with common interferants found in human sweat and blood, showed an RSD of 4.32%. We hope that the electrocatalytic efficacy of the thermally grown CuNOx sensors for glucose sensing can introduce new avenues in the fabrication of sweat glucose sensors.

Green synthesized gold nanoparticles and CuO-based nonenzymatic sensor for saliva glucose monitoring

Glucose, essential for brain and muscle functions, requires careful monitoring in diabetes and other chronic disease management. While blood glucose monitoring provides precise information about these diseases, it remains an invasive method. Saliva glucose monitoring could offer an alternative approach, but the glucose concentration in saliva is very low. In this work, we report a simple, low-cost, highly sensitive nonenzymatic electrochemical glucose sensor. We developed this sensor using green synthesized gold nanoparticles (AuNPs) and wet chemical synthesized copper oxide (CuO) nanoparticles on a screen-printed carbon electrode (Au/CuO/SPCE). The sensor's high sensitivity results from dual amplification strategies using AuNPs and CuO nanomaterials, each demonstrating catalytic activity towards glucose. This shows promising potential for saliva glucose monitoring. The AuNPs were synthesized using an Au precursor and orange peel extract (OPE), yielding stable colloidal AuNPs with a mean diameter of about 37 nm, thus eliminating the need for additional capping agents. Under optimal conditions, amperometric tests revealed that the sensor responded linearly to glucose concentrations ranging from 2 μM to 397 μM with a sensitivity of 236.70 μA mM-1 cm-2. Furthermore, the sensor demonstrated excellent reproducibility, stability and high selectivity for glucose in the presence of different biomolecules. We validated the sensor's efficacy by measuring glucose in human saliva, showing its potential for noninvasive glucose monitoring. This research advances the development of point-of-care devices, positioning the sensor as a promising tool for noninvasive glucose monitoring and improved diabetes management.

Recent Advances in Skin-Interfaced Wearable Sweat Sensors: Opportunities for Equitable Personalized Medicine and Global Health Diagnostics

Recent advances in skin-interfaced wearable sweat sensors enable the noninvasive, real-time monitoring of biochemical signals associated with health and wellness. These wearable platforms leverage microfluidic channels, biochemical sensors, and flexible electronics to enable the continuous analysis of sweat-based biomarkers such as electrolytes, metabolites, and hormones. As this field continues to mature, the potential of low-cost, continuous personalized health monitoring enabled by such wearable sensors holds significant promise for addressing some of the formidable obstacles to delivering comprehensive medical care in under-resourced settings. This Perspective highlights the transformative potential of wearable sweat sensing for providing equitable access to cutting-edge healthcare diagnostics, especially in remote or geographically isolated areas. It examines the current understanding of sweat composition as well as recent innovations in microfluidic device architectures and sensing strategies by showcasing emerging applications and opportunities for innovation. It concludes with a discussion on expanding the utility of wearable sweat sensors for clinically relevant health applications and opportunities for enabling equitable access to innovation to address existing health disparities.

Nanomaterials-Based Electrochemical Δ9-THC and CBD Sensors for Chronic Pain

Chronic pain is now included in the designation of chronic diseases, such as cancer, diabetes, and cardiovascular disease, which can impair quality of life and are major causes of death and disability worldwide. Pain can be treated using cannabinoids such as Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD) due to their wide range of therapeutic benefits, particularly as sedatives, analgesics, neuroprotective agents, or anti-cancer medicines. While little is known about the pharmacokinetics of these compounds, there is increasing interest in the scientific understanding of the benefits and clinical applications of cannabinoids. In this review, we study the use of nanomaterial-based electrochemical sensing for detecting Δ9-THC and CBD. We investigate how nanomaterials can be functionalized to obtain highly sensitive and selective electrochemical sensors for detecting Δ9-THC and CBD. Additionally, we discuss the impacts of sensor pretreatment at fixed potentials and physiochemical parameters of the sensing medium, such as pH, on the electrochemical performance of Δ9-THC and CBD sensors. We believe this review will serve as a guideline for developing Δ9-THC and CBD electrochemical sensors for point-of-care applications.

Nonenzymatic Electrochemical Glutamate Sensor Using Copper Oxide Nanomaterials and Multiwall Carbon Nanotubes

Glutamate is an important neurotransmitter due to its critical role in physiological and pathological processes. While enzymatic electrochemical sensors can selectively detect glutamate, enzymes cause instability of the sensors, thus necessitating the development of enzyme-free glutamate sensors. In this paper, we developed an ultrahigh sensitive nonenzymatic electrochemical glutamate sensor by synthesizing copper oxide (CuO) nanostructures and physically mixing them with multiwall carbon nanotubes (MWCNTs) onto a screen-printed carbon electrode. We comprehensively investigated the sensing mechanism of glutamate; the optimized sensor showed irreversible oxidation of glutamate involving one electron and one proton, and a linear response from 20 μM to 200 μM at pH 7. The limit of detection and sensitivity of the sensor were about 17.5 μM and 8500 μA·mM^-1·cm^-2, respectively. The enhanced sensing performance is attributed to the synergetic electrochemical activities of CuO nanostructures and MWCNTs. The sensor detected glutamate in whole blood and urine and had minimal interference with common interferents, suggesting its potential for healthcare applications.

Wearable Noninvasive Glucose Sensor Based on CuxO NFs/Cu NPs Nanocomposites

Designing highly active material to fabricate a high-performance noninvasive wearable glucose sensor was of great importance for diabetes monitoring. In this work, we developed Cu_xO nanoflakes (NFs)/Cu nanoparticles (NPs) nanocomposites to serve as the sensing materials for noninvasive sweat-based wearable glucose sensors. We involve CuCl₂ to enhance the oxidation of Cu NPs to generate Cu₂O/CuO NFs on the surface. Due to more active sites endowed by the Cu_xO NFs, the as-prepared sample exhibited high sensitivity (779 μA mM^-1 cm^-2) for noninvasive wearable sweat sensing. Combined with a low detection limit (79.1 nM), high selectivity and the durability of bending and twisting, the Cu_xO NFs/Cu NPs-based sensor can detect the glucose level change of sweat in daily life. Such a high-performance wearable sensor fabricated by a convenient method provides a facile way to design copper oxide nanomaterials for noninvasive wearable glucose sensors.

3D Printed Voltammetric Sensor Modified with an Fe(III)-Cluster for the Enzyme-Free Determination of Glucose in Sweat

In this work, a 3D printed sensor modified with a water-stable complex of Fe(III) basic benzoate is presented for the voltammetric detection of glucose (GLU) in acidic epidermal skin conditions. The GLU sensor was produced by the drop-casting of Fe(III)-cluster ethanolic mixture on the surface of a 3D printed electrode fabricated by a carbon black loaded polylactic acid filament. The oxidation of GLU was electrocatalyzed by Fe(III), which was electrochemically generated in-situ by the Fe(III)-cluster precursor. The GLU determination was carried out by differential pulse voltammetry without the interference from common electroactive metabolites presented in sweat (such as urea, uric acid, and lactic acid), offering a limit of detection of 4.3 μmol L-1. The exceptional electrochemical performance of [Fe3O(PhCO2)6(H2O)3]∙PhCO2 combined with 3D printing technology forms an innovative and low-cost enzyme-free sensor suitable for noninvasive applications, opening the way for integrated 3D printed wearable biodevices.

Synthesis of Copper Nanostructures for Non-Enzymatic Glucose Sensors via Direct-Current Magnetron Sputtering

In this paper, Cu nanocolumnar structure electrodes are synthetized using a clean and easy-to-scale-up direct-current magnetron sputtering (DC-MS) technique for non-enzymatic glucose sensing. The nanocolumnar structure increases the active surface area of the deposit, with the nanocolumns showing a mean size diameter of 121.0 nm ± 27.2 and a length of 2.52 µm ± 0.23. A scanning transmission electron (STEM) analysis shows the presence of Cu and a small amount of Cu₂O. The behavior of the electrodes in alkaline environments and the electrochemical affinity of the Cu nanocolumns (CuNCs) towards the electro-oxidation of glucose are investigated using cyclic voltammetry (CV). After performing CV in NaOH solution, the columnar structures present corrosion products containing Cu₂O, as revealed by STEM and X-ray diffraction (XRD) analyses. The amperometric responses of the CuNCs to the successive addition of glucose show a linear range up to 2 mM and a limit of detection of 5.2 µM. Furthermore, the electrodes are free from chloride poisoning, and they are insensitive to dopamine, uric acid, ascorbic acid, and acetaminophen at their physiological concentrations.

Development of a methodology for evaluating the efficiency level of monitoring agroecosystems using big data technologies

The article discusses the theory of monitoring agroecosystems for the effectiveness of using Big Data technologies. The relationship between the agricultural areas of the Tyumen region, the Big Data sources available in them, and the technologies for working with Big Data obtained from sources are described. The article also developed a methodology that makes it possible to assess the level of effectiveness of monitoring agroecosystems using Big Data technologies, based on the result of which a strategy for development of the region as a whole and its agroecosystems, in particular, is formed in terms of equipment with information technologies. The methodology presented in the article is formed on the basis of an engineering ontology, which in the future is able to lower the degree of the human factor in global and local monitoring of agroecosystems for the effectiveness of using Big Data technologies.