Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time-space evolution require information about characteristic parameters such as the ash granulometry. Typically, such information is gained by spot direct observation of the ash at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of the ash granulometry is presented. A dedicated image processing paradigm has been developed and implemented in LabVIEW™. The methodology has been validated experi-mentally using digital images and the accuracy of the image processing paradigm has been estimated.

Recently, Graphene has attracted intensive attention in the gas sensing field due to its high electrical conductivity as well as large specific surface areas. Lots of graphene-based gas sensor have been reported with excellent gas sensing performance. However, the sensing element materials for most of the above sensors are actually consisted of reduced graphene oxide (GO) derivative rather than pristine graphene, like rGO, rGO/metal particle, rGO/polymers etc. Complex chemical oxidation and reduction are usually involved for the preparation of reduced graphene oxide derivatives. Even though there are some pristine graphene-based gas sensor synthesizing with the approaches of chemical vapor deposition (CVD) or mechanical cleavage, the high cost of the set-up or the low productivity cannot decrease the cost of the practical sensors. In this work, we develop pristine graphene-based gas sensors utilizing flavin monocleotide sodium salt (FMNS) towards ultra-sensitive ammonia detection. The sensor has 3% response upon exposure to 10 ppm NH₃ and a limit of detection of 1.6 ppm at room temperature and shows a good recovery. Raman, UV-vis, FT-IR spectra, as well as SEM measurements are employed to characterized the quality of the graphene flakes, indicating a good structural quality of graphene with few defects. The effects of concentration of graphene dispersion functionalized by FMNS on sensing performance towards ammonia sensing were also investigated. The process is very mild, environmentally friendly, and low cost. We believe this work may pave a path to design high performance gas sensor with low cost and boost the application of graphene for sensing.

Heat-exposed work activities or prolonged sport sessions suppose a continuous nutrient loss through sweating, leading to long-term health issues. Among prevention steps, the use of miniaturized sensors for real time monitoring of micronutrient presence directly in sweat can be of great interest. Here, we propose a flexible sensor for detection of Vitamin C (ascorbic acid), based on a very simple process of electrode modification via electrodeposition of a membrane containing CuO nanoparticles. The reductive effect of ascorbic acid on the nanoparticles produces a shift of the redox peaks in cyclic voltammetry analysis, which can be measured at nearly zero volts as a current increase by amperometry. The detection is performed efficiently at the micromolar ascorbic acid levels found naturally in sweat and works at ultra-low potential (−5 mV), showing no interferences with other typical molecules found in the samples. In combination with sensors for other nutrients, this can be a promising approach for preventive healthcare applications.

Dexterous robotic manipulation in unstructured environments is still challenging, despite the increasing number of robots entering human settings each day. Even though robotic manipulation has complete solutions in factories and industries, it still lacks essential techniques, displaying clumsy or limited operation in unstructured environments. Daily objects typically aim at the human hand, and the human somatosensory system is responsible for solving all the complex calculations required for dexterous manipulations in unstructured settings. Borrowing concepts of the human visuotactile system can improve dexterous manipulation and increase robotics usage in unstructured environments. In humans, required finger and wrist joint adjustments occur after fast identification of the object in the initial stages of manipulation. Fast object identification during those phases may increase robotic dexterous manipulation performance. The present paper explores human-inspired concepts such as haptic glance to develop robotic single-grasp object identification. This concept can assist early phases of robotic manipulation, helping automated decision-making, such as type of grasp and joint position during manipulation tasks. The main stages developed here are detecting sensor activation and sample collection using signal-to-noise and z-score filtering on tactile data. This procedure automates touch detection and reduces the sensor space for classification. Experiments on a daily objects dataset presented compelling results that will assist later stages of early phases of robotic grasping.

CMOS based Electrostatically-Formed Nanowire (EFN) sensor is based on a silicon nanowire field-effect transistor (FET) with a nanowire that is electrostatically formed and controlled by post fabrication. The EFN-FET is composed of doped silicon region surrounded by three gates: bottom gate and two lateral junction gates. Appropriate biasing at the gates induces depletion regions at the gate-silicon interfaces and an un-depleted silicon region which is electrostatically shaped into a wire of several nm in diameter is now available for conduction. Target gas molecules get adsorbed on the SiO₂ surface and via field-effect modifies the current conduction through the nanowire. Further, surface functionalization of these EFN sensors by metal nanoparticles could be an effective approach to achieve selectivity towards gases. For example, Pd nanoparticles (1 nm) decorated EFN (Pd-EFN) sensor shows promising results towards hydrogen gas. It shows excellent sensor responses at all concentrations ranging from 0.2 to 2.56% with quick response and recovery times. Also, the responses are linear over the entire concentration range and shows good repeatability. A low detection limit of 200 ppm (with a sensor response of 500%) is achieved which is much lower than the lower explosive limit of hydrogen gas which is 4%. The sensor retains good performances even in humid conditions with 80% RH. The sensor performances can further be tuned through application of different gate biases. A comparison of the performance metrics with the state of the art hydrogen sensors show that Pd-EFN proves to be a promising hydrogen sensor.

With the development of smartphones, cameras based on ultra-small, high-definition, and open platforms has been mass-produced. In this paper, we built an emulation system to verify the bio-imaging performance of bulky and expensive high-performance cameras previously used in bio-imaging devices, and various smartphone cameras. Four types of cameras were tested in the emulator, and the gel image analysis results were compared by selecting three cameras with more linear changes in slope, which matched the performance evaluation in the emulator.

Chemiresistive gas sensors are an important tool for monitoring air quality in cities and large areas due to their low cost, low power and, hence, the ability to densely distribute them. Unfortunately, such sensor systems are prone to defects and faults over time such as sensitivity loss of the sensing material, less effective heating of the surface due to battery loss, or random output errors in the sensor electronics, which can lead to signal jumps or sensor stopping. Although these defects usually can be compensated, either algorithmically or physically, this requires an accurate screening of the entire sensor system for such defects. In order to properly develop, test, and benchmark corresponding screening algorithms, however, methods for simulating gas sensor networks and their defects are essential. In this work, we propose such a simulation method based on a stochastic sensor model for chemiresistive sensor systems. The proposed method rests on the idea of simulating the defect-causing processes directly on the sensor surface as a stochastic process and is capable of simulating various defects which can occur in low-cost sensor technologies. The work aims to show the scope and principles of the proposed simulator as well as to demonstrate its applicability using exemplary use cases.

Lung cancer is one of the most common causes of cancer-related death worldwide. It is usually detected by CT or MRI and removed through thoracoscopic surgery. However, during the surgery, the lung collapses and a new determination of the position of the pulmonary nodule is necessary which is particularly challenging in the case of minimally invasive surgeries when palpation is not possible. In this contribution, ultra-wideband (UWB) radio technology, which employs a short burst of high-frequency electromagnetic waves, is studied to localize the pulmonary nodule. In short, an antenna in close proximity with the lung surface produces a signal and the echo coming from discontinuities in the lung tissue, i.e., the pulmonary nodule, is used for the localization. A similar approach was already proposed for breast cancer. Simulations were used to explore the relationship between frequency range and penetration depth and showed that shallow nodules, below 2 cm in depth, are difficult to resolve because the echo directly interferes with the propagating signal. On the other hand, given the strong electromagnetic attenuation of lung tissue, echo coming from near organs is suppressed and frequency-band tuning can be employed to range the depth of the investigation. Ultimately, this contribution shows how to employ and design UWB technology to localize deep pulmonary nodules through a minimally invasive approach.

Carbon Dots (CDs) can be defined as carbon nanoparticles with a size from 1-10 nm, with absorption and photoluminescence activity in the UV-vis range. CDs can have crystalline or amorphous structure, this wide range of structures opens up the possibility to design different CDs. The luminescence properties of CDs change not only according to the type of structure, but also according to the type of chemical group that covers the surface, such as: amino groups, carboxylic or ester groups and others. In this work we synthesized CDs with a bottom up approach and we obtained two different CDs graphitic and diamond like, they were purified with Size Exclusion Chromatography (SEC) in order to obtain fairly pure CDs for sensing tests. They have been characterized to study the optical properties with spectroscopic techniques like absorption and fluorescence spectroscopic and with Raman spectroscopic and Atomic Force Microscopy (AFM) we studied the different structure and the morphology of the CDs. Finally with fluorescence we evaluated the interactions between CDs and pollutants.

In recent years, soft pneumatic actuators have come in the spotlight because of their simple con-trol and the wide range of complex motions. To monitor the deformation of soft robotic systems, elastomer-based sensors are being used. However, embedding of sensors into soft actuator mod-ules by polymer casting is time consuming and difficult to upscale. In this study, it is shown how a pneumatic bending actuator with an integrated sensing element can be produced using extru-sion-based additive manufacturing method, e.g. fused deposition modeling (FDM). The advantage of FDM against direct printing or robocasting is the significantly higher resolution and the ability to print large objectives in short time. New, commercial launched pellet-based FDM printers are able to 3D print thermoplastic elastomers of low shore hardness that are required for soft robotic applications, to avoid high pressure for activation. A soft pneumatic actuator with the in-situ in-tegrated piezoresistive sensor element was successfully printed using a commercial styrene-based thermoplastic elastomer (TPS) and a developed TPS/carbon black (CB) sensor composite. It has been demonstrated that the integrated sensing elements could monitor the deformation of the pneumatic soft robotic actuator. The findings of this study contribute to extending the applicabil-ity of additive manufacturing for integrated soft sensors in large soft robotic systems.