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The piezoresistive response of n-and p-type hydrogenated nanocrystalline silicon thin films, deposited by hot-wire (HW) and plasma-enhanced chemical vapor deposition (PECVD) on thermally oxidized silicon wafers, has been studied using four-point bending tests. The piezoresistive gauge factor (GF) was measured on patterned thin-film micro-resistors rotated by an angle θ with respect to the principal strain axis. Both longitudinal (GF L) and transverse (GF T) GFs, corresponding to θ = 0º and 90º, respectively, are negative for n-type and positive for p-type films. For other values of θ (30º, 45º, 120º and 135º) GFs have the same signal as GF L and GF T and their value is proportional to the normal strain associated with planes rotated by θ relative to the principal strain axis. It is concluded that the films are isotropic in the growth plane since the GF-values follow a Mohr's circle with the principal axes 2 coinciding with those of the strain tensor. The strongest p-type pirezoresistive response (GF L = 41.0, GF T = 2.84) was found in a film deposited by PECVD at a substrate temperature of 250ºC and working pressure of 0.250 Torr, with dark conductivity 1.6 Ω-1 cm-1. The strongest n-type response (GF L =-28.1, GF T =-5.60) was found in a film deposited by PECVD at 150ºC and working pressure of 3 Torr, with dark conductivity 9.7 Ω-1 cm-1. A model for the piezoresistivity of nc-Si is proposed, based on a mean-field approximation for the conductivity of an ensemble of randomly oriented crystallites and neglecting grain boundary effects. The model is able to reproduce the measured GF L values for both n-and p-type films. It fails however to explain the transversal GF T data. Both experimental and theoretical data show that nanocrystalline silicon can have an isotropic piezoresistive effect of the order of 40% of the maximum response of crystalline silicon.

We start by presenting an overview of the various definitions of heat and work found in the literature. Then, we consider several examples for analysis and show that the theory of Thermodynamics requires a precise definition of such quantities. The comparison regarding the Second Law of the system-based and surroundings-based definitions of heat and work is undertaken leading to the conclusion that the definitions of such concepts should be based on variables external to the system.

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We studied through Monte Carlo simulations, the kinetics of the two-species diffusion-limited reaction model with same species excluded volume interaction in substrates embedded on a square lattice ranging in occupancy from a fractal percolating structure to the compact limit.We study the time evolution of the concentration of single-particle species for various values of substrate occupancies, 0.5927460, 0.61, 0.63, 0.65, 0.7, 0.8, and 1, where the first value corresponds to the percolating probability of the square lattice. We show that in the diffusionlimited reaction regime, the kinetics strongly depends on the presence of a bias along a particular square lattice direction, representing the net effect of a driving field. We were able to explain the slow dynamics at high values of the driving field in terms of traps appearing in diluted substrates, particularly at the percolation threshold.

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.