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This article addresses reliability under the sweat of interconnection techniques for the mounting surface mounted device (SMD) components and fully printed humidity sensors onto conductive stretchable textile ribbons. Samples underwent testing for the effect of ageing by artificial sweat on their electrical resistance using both alkaline and acidic artificial sweat. The best results in terms of electrical resistance change were obtained for samples soldered to the conductive fibers interwoven in the ribbon. However, this method can damage the ribbon due to the high temperature during soldering and significantly reduce the mechanical properties and flexibility of the ribbon, which can lead to a limited service life of samples. On the other hand, adhesive bonding is a very interesting alternative, where the above-mentioned properties are preserved, but there is a significant effect of sweat ageing on electrical resistance. The results of fully printed graphene-based humidity sensors show that, for the intended use of these sensors (i.e., detection of changes in moisture on the human body), usage of the samples is possible, and the samples are sufficiently reliable in the case of sweat degradation. In addition, the response of the sensor to humidity is quite high: 98% at a relative humidity of 98%.

Corrosion is an ever-present phenomena of material deterioration that affects all metal structures. Timely and accurate detection of corrosion is required for structural maintenance and effective management of structural components during their life cycle. The usage of aircraft materials has been primarily driven by the need for lighter, stronger, and more robust metal alloys, rather than mitigation of corrosion. As such, the overall cost of corrosion management and aircraft downtime remains high. To illustrate, $5.67 billion or 23.6% of total sustainment costs was spent on aircraft corrosion management, as well as 14.1% of total NAD for the US Air Force aviation and missiles in the fiscal year of 2018. The ability to detect and monitor corrosion will allow for a more efficient and cost-effective corrosion management strategy, and will therefore, minimize maintenance costs and downtime, and to avoid unexpected failure associated with corrosion. Conventional and commercial efforts in corrosion detection on aircrafts have focused on visual and other field detection approaches which are time- and usage-based rather than condition-based; they are also less effective in cases where the corroded area is inaccessible (e.g., fuel tank) or hidden (rivets). The ability to target and detect specific corrosion by-products associated with the metals/metal alloys (chloride ions, fluoride ions, iron oxides, aluminum chlorides etc.), corrosion environment (pH, wetness, temperature), along with conventional approaches for physical detection of corrosion can provide early corrosion detection as well as enhanced reliability of corrosion detection. The paper summarizes the state-of-art of corrosion sensing and measurement technologies for schedule-based inspection or continuous monitoring of physical, environmental and chemical presence associated with corrosion. The challenges are reviewed with regards to current gaps of corrosion detection and the complex task of corrosion management of an aircraft, with a focused overview of the corrosion factors and corrosion forms that are pertinent to the aviation industry. A comprehensive overview of thin film sensing techniques for corrosion detection and monitoring on aircrafts are being conducted. Particular attention is paid to innovative new materials, especially graphene-derived thin film sensors which rely on their ability to be configured as a conductor, semiconductor, or a functionally sensitive layer that responds to corrosion factors. Several thin film sensors have been detailed in this review as highly suited candidates for detecting corrosion through direct sensing of corrosion by-products in conjunction with the aforementioned physical and environmental corrosion parameters. The ability to print/pattern these thin film materials directly onto specific aircraft components, or deposit them onto rigid and flexible sensor surfaces and interfaces (fibre optics, microelectrode structures) makes them highly suited for corrosion monitoring applications.

The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of “Graphene sensors” and “Biomarkers” in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed.

This study presents a novel hardware and software architecture combining capacitive sensors, quantum-inspired algorithms, and deep learning applied to the detection of Essential Tremor. At the core of this architecture are graphene-printed capacitive sensors, which provide a cost-effective and efficient solution for tremor data acquisition. These sensors, known for their flexibility and precision, are specifically calibrated to monitor tremor movements across various fingers. A distinctive feature of this study is the incorporation of quantum-inspired computational filters—namely, Quantvolution and QuantClass —into the deep learning framework. This integration offers improved processing capabilities, facilitating a more nuanced analysis of tremor patterns. Initial findings indicate greater stability in loss variability; however, further research is necessary to confirm these effects across broader datasets and clinical environments. The approach highlights a promising application of quantum-inspired methods within healthcare diagnostics.

This work aims to serve as a comprehensive guide to properly characterize solid-contact ion-selective electrodes (SC-ISEs) for long-term use as they advance toward calibration-free sensors. The lack of well-defined SC-ISE performance criteria limits the ability to compare results and track progress in the field. Laser-induced graphene (LIG) is a rapid and scalable method that, by adjusting the CO 2 laser parameters, can create LIG substrates with tunable surface properties, including wettability, surface chemistry, and morphology. Herein, we fabricate laser-induced graphene (LIG) solid-contact electrodes using different laser settings and subsequently convert them into ion-selective sensors using a potassium-selective membrane. We measure the aforementioned tunable surface properties and correlate them with resultant low-frequency capacitance and water layer formation in an effort to pinpoint their effects on the sensitivity (Nernstian response), reproducibility (E°’ variation), and potential stability of the LIG-based SC-ISEs. More specifically, we demonstrate that the surface wettability of the LIG substrate, which can be tuned by controlling the lasing parameters, can be modified to exhibit hydrophobic (contact angle > 90°) and even highly hydrophobic surfaces (contact angle ≈ 130°) to help reduce sensor drift. Recommendations are also provided to ensure proper and robust characterization of SC-ISEs for long-term and continuous measurements. Ultimately, we believe that a comprehensive understanding of the correlation between LIG tunable surface properties and SC-ISE performance can be used to improve the electrochemical behavior and stability of SC-ISEs designed with a wide range of materials beyond LIG. Graphical Abstract

The importance of nitrite ions has long been recognized due to their extensive use in environmental chemistry and public health. The growing use of nitrogen fertilizers and additives containing nitrite in processed food items has increased exposure and, as a result, generated concerns about potential harmful health consequences. This work presents the development of an electrochemical sensor based on graphene/glassy carbon electrode (EGr/GC) with applicability in trace level detection of nitrite in water samples. According to the structural characterization of the exfoliated material, it appears as a mixture of graphene oxide (GO; 21.53%), few-layers graphene (FLG; 73.25%) and multi-layers graphene (MLG; 5.22%) and exhibits remarkable enhanced sensing response towards nitrite compared to the bare electrode (three orders of magnitude higher). The EGr/GC sensor demonstrated a linear range between 3 × 10−7 and 10−3 M for square wave voltammetry (SWV) and between 3 × 10−7 and 4 × 10−4 M for amperometry (AMP), with a low limit of detection LOD (9.9 × 10−8 M). Excellent operational stability, repeatability and interference-capability were displayed by the modified electrode. Furthermore, the practical applicability of the sensor was tested in commercially available waters with excellent results.

We have pioneered the use of liquid polar organic molecules as alternatives to rigid gate-dielectrics for the fabrication of graphene field-effect transistors. The unique high net dipole moment of various polar organic molecules allows for easy manipulation of graphene’s conductivity due to the formation of an electrical double layer with a high-capacitance at the liquid and graphene interface. Here, we compare the performances of dimethyl sulfoxide (DMSO), acetonitrile, propionamide, and valeramide as polar organic liquid dielectrics in graphene field-effect transistors (GFETs). We demonstrate improved performance for a GFET with a liquid dielectric comprised of DMSO with high electron and hole mobilities of 154.0 cm2/Vs and 154.6 cm2/Vs, respectively, and a Dirac voltage <5 V.

Recently, graphene has gained increased attention because of its unique physical and electrical properties. For example, graphene is characterized by an extremely high conductivity and electron mobility at room temperature as well as robust mechanical properties such as flexibility. Moreover, the atomic thickness of graphene makes it extremely sensitive to changes in the local environment. Therefore, graphene and related materials are ideal for the fabrication of chemical and biosensors. As chemical and biosensors are becoming an indispensable part of our society with wide usage across various fields, including biomedical, chemical processing, clinical, environmental, food, military, pharmaceutical, and security applications, in this chapter we seek to give an overview of the latest developments in the application of graphene‐based materials to chemical and biosensors. In particular, we review graphene‐based electronic, electrochemical, and optical sensors with particular emphasis on their underlying mechanism of action as well as their application to chemical and biosensing for highly selective and sensitive detection.

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, and Random Forest), unsupervised learning models (K-Means Clustering), and neural networks (Multilayer Perceptron) to predict and identify ice formation patterns. The experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real time, optimizes deicing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance.

This study presents the design, numerical modeling, and performance analysis of a tunable graphene-based terahertz (THz) sensor exhibiting multiband resonance behavior. The proposed device leverages the unique electro-optical properties of graphene, where modulation of the chemical potential (V) enables dynamic control over the electromagnetic response. This tunability allows real-time ON/OFF operation and precise frequency reconfiguration, resulting in enhanced adaptability for diverse sensing scenarios. The sensor supports four distinct resonance modes at 0.22, 0.35, 0.70, and 0.88 THz, covering a wide portion of the THz spectrum and making it particularly suitable for multiband detection. Simulation results reveal that increasing the chemical potential shifts all resonance modes toward higher frequencies, a consequence of enhanced graphene conductivity and improved impedance matching between the sensor and the surrounding medium. This frequency agility is critical for applications where detection bands need to be dynamically selected or reconfigured. The proposed design exhibits sharp resonance dips, high spectral selectivity, and elevated sensitivity, leading to an impressive figure of merit (FOM) of up to 5.30 RIU⁻ 1 across all operational modes. For a refractive index range of 1.0–1.1, the device achieves sensitivities of 0.51, 0.85, 1.87, and 0.212 THz/RIU for the first to fourth modes, respectively, all within the 0.1–1.0 THz operational window. Such performance metrics position the sensor as a promising candidate for next-generation THz biosensing, non-invasive medical imaging, and chemical or environmental detection, where rapid, reconfigurable, and label-free measurements are essential. Moreover, the multiband capability and tunable nature of the graphene platform open avenues for integrated lab-on-chip sensing systems with adaptive, high-precision operation.