Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

The development of alternative brain-inspired neuromorphic computing architectures is anticipated to play a key role in addressing the strict requirements of the artificial intelligence era. In order to obtain a high degree of learning accuracy within an artificial neural network (ANN) that operates with the backpropagation algorithm, a highly symmetric synaptic weight distribution is desired. Along these lines, we present here a detailed device engineering approach that enables analog synaptic properties in completely forming free SiO₂-conductive bridge memories. This is achieved by either incorporating a dense layer of Pt nanoparticles as a bottom electrode or fabricating bilayer structures using a second switching layer of VO_x. Interestingly, compared with the reference sample that manifests both threshold and bipolar switching modes, the Pt NC sample exhibits only the threshold switching pattern, whereas the bilayer configuration operates only under the bipolar switching mode, as illustrated by direct current measurements. These characteristics have a direct, while different impact, on the conductance modulation pattern and determine the analog nature of the synaptic weight distribution. Valuable insights regarding the origin of these effects and, in particular, of the symmetric and linear conductance modulation processes are gained through the implementation of a self-consistent numerical model that takes into account both the impact of the electrodes' thermal conductivity on the switching pattern and the different diffusion barriers for silver ion migration. Our approach provides useful guidelines toward the realization of high yield ANNs with biological-like dynamic behavior by controlling the conducting filament growth mechanism.

The implementation of neuromorphic computations within a fully memristive neural network is considered the holy grail of the artificial intelligence era. In order to attain this goal, it is quite important to develop robust and configurable electronic devices capable of emulating spiking neuronal and synaptic plasticity activities. Along these lines, we report here the direct impact of oxygen concentration as well as of the homo-bilayer material configuration of SiO ₂ -conductive bridge memories to the manifestation of tunable threshold and bipolar switching effects. Interestingly, while the bilayer structure of Ag/SiO _x /SiO _y /TiN ( x < y ) exhibits only bipolar switching effect, the respective single-layer structures of Ag/SiO _y /TiN and Ag/SiO _x /TiN operate under either threshold switching or both modes. Insights regarding the impact of oxygen concentration into the conducting filament growth process are provided. The manifestation of the two switching modes permits the emulation of various synaptic effects, such as short-term plasticity and long-term plasticity whereas the modulation of the conductance values allows the synaptic weight tuning by controlling the amplitude or the frequency of the triggering signals. Moreover, arbitrary neuron characteristics were obtained from our volatile memory devices without integrating any other auxiliary circuit. Our approach provides valuable insights towards the realization of artificial neural networks from the same material configuration with biological-like dynamic behavior.

We report arsenic and phosphorus diffusion experiments and activation related phenomena in codoped germanium substrates utilizing conventional thermal annealing. Chemical profiles were obtained by secondary ion mass spectroscopy, sheet resistance was estimated by the Van der Pauw method. Our study covers the temperature range from 600 to 750 °C. We accurately described the dopant profiles with a quadratic dependence of the dopants diffusion coefficient on the free electron concentration. In our simulations we considered the dopant pile-up near the surface and dopant loss owing to outdiffusion during the annealing. Although the double donor codoping technique exhibited no advantage over monodoping with P concerning the level of activation and junction depth, it was interesting to observe the different diffusion behavior of the two dopants. Whereas the diffusion of As indicates a retardation under codoping the diffusion of P remains either unaffected or is slightly enhanced by codoping. The activation level of the codoped samples remains lower compared to the respective monodoped samples, except for the highest annealing temperature.

The development of low-power neurons with an intrinsic degree of stochasticity is considered quite important for the emulation of the respective probabilistic procedures that take place within the biological neural networks. While the majority of the reported artificial neuronal configurations relies on the demonstration of well-defined spiking patterns or the realization of complicated neuromorphic properties, there is an urgent need to devise biological-like neurons with arbitrary firing capabilities. Along these lines, we present a novel threshold switching memristor from SiO ₂ -based bilayer conductive-bridge resistive switching memory (CBRAM) and Pt nanoparticles (NPs) used as a bottom electrode (BE) in order to implement robust stochastic neuron activity accompanied with steep transition slope [~0.75 mV/dec(A)], large OFF-state (~ $10^{11} \Omega $ ), low switching voltage (~200 mV), and ultrafast turn-on speed (~50 ns). Moreover, probabilistic leaky-integrate-and-fire (LIF) neuron properties were attained by employing a simple RC circuit as well as tunable integrate-and-fire (IF) properties from stand-alone threshold switching elements, which are of great significance for the development of artificial spiking neuromorphic networks.