Polymer indentation with mesoscopic molecular dynamics

Indentation tests are used to determine the hardness of a material, e.g., Rockwell, Vickers, or Knoop. The indentation process is empirically observed in the laboratory during these tests; the mechanics of indentation is insufficiently understood. We have performed first molecular dynamics computer simulations of indentation resistance of polymers with a chain structure similar to that of high density polyethylene (HDPE). A coarse grain model of HDPE is used to simulate how the interconnected segments respond to an external force imposed by an indenter. Results include the time-dependent measurement of penetration depth, recovery depth, and recovery percentage, with respect to indenter force, indenter size, and indentation time parameters. The simulations provide results that are inaccessible experimentally, including continuous evolution of the pertinent tribological parameters during the entire indentation process.

Temperature dependence of the electrical conductivity of vapor grown carbon nanofiber/epoxy composites with different filler dispersion levels

The influence of the dispersion of vapor grown carbon nanofibers (VGCNF) on the electrical properties of VGCNF/epoxy composites has been studied. A homogeneous dispersion of the VGCNF does not imply better electrical properties. The presence of well distributed clusters appears to be a key factor for increasing composite conductivity. It is also shown that the main conduction mechanism has an ionic nature for concentrations below the percolation threshold, while above the percolation threshold it is dominated by hopping between the fillers. Finally, using the granular system theory it is possible to explain the origin of conduction at low temperatures.

Towards the design of efficient nonbeacon-enabled ZigBee networks

This paper presents experimental results of the communication performance evaluation of a prototype ZigBee-based patient monitoring system commissioned in an in-patient floor of a Portuguese hospital (HPG – Hospital Privado de Guimar~aes). Besides, it revisits relevant problems that affect the performance of nonbeacon-enabled ZigBee networks. Initially, the presence of hidden-nodes and the impact of sensor node mobility are discussed. It was observed, for instance, that the message delivery ratio in a star network consisting of six wireless electrocardiogram sensor devices may decrease from 100% when no hidden-nodes are present to 83.96% when half of the sensor devices are unable to detect the transmissions made by the other half. An additional aspect which affects the communication reliability is a deadlock condition that can occur if routers are unable to process incoming packets during the backoff part of the CSMA-CA mechanism. A simple approach to increase the message delivery ratio in this case is proposed and its effectiveness is verified. The discussion and results presented in this paper aim to contribute to the design of efficient networks,and are valid to other scenarios and environments rather than hospitals.

Modeling Carbon Nanotube Electrical Properties in CNT/Polymer Composites

In this work it is demonstrated that the capacitance between two cylinders increases with the rotation angle and it has a fundamental influence on the composite dielectric constant. The dielectric constant is lower for nematic materials than for isotropic ones and this can be attributed to the effect of the filler alignment in the capacitance. The effect of aspect ratio in the conductivity is also studied in this work. Finally, based on previous work and by comparing to results from the literature it is found that the electrical conductivity in this type of composites is due to hopping between nearest fillers resulting in a weak disorder regime that is similar to the single junction expression.

Multi-scale modeling of the mechanical behavior of polymer-based materials

Polymers have become the reference material for high reliability and performance applications. In this work, a multi-scale approach is proposed to investigate the mechanical properties of polymeric based material under strain. To achieve a better understanding of phenomena occurring at the smaller scales, a coupling of a Finite Element Method (FEM) and Molecular Dynamics (MD) modeling in an iterative procedure was employed, enabling the prediction of the macroscopic constitutive response. As the mechanical response can be related to the local microstructure, which in turn depends on the nano-scale structure, the previous described multi-scale method computes the stress-strain relationship at every analysis point of the macro-structure by detailed modeling of the underlying micro- and meso-scale deformation phenomena. The proposed multi-scale approach can enable prediction of properties at the macroscale while taking into consideration phenomena that occur at the mesoscale, thus offering an increased potential accuracy compared to traditional methods.

Modelling the Mechanical Behavior of Polymer-Based Nanocomposites

Molecular dynamics simulations were employed to analyze the mechanical properties of polymer-based nanocomposites with varying nanofiber network parameters. The study was focused on nanofiber aspect ratio, concentration and initial orientation. The reinforcing phase affects the behavior of the polymeric nanocomposite. Simulations have shown that the fiber concentration has a significant effect on the properties, with higher loadings resulting in higher stress levels and higher stiffness, matching the general behavior from experimental knowledge in this field. The results also indicate that, within the studied range, the observed effect of the aspect ratio and initial orientation is smaller than that of the concentration, and that these two parameters are interrelated.

Coupling Mechanical and Electrical Behavior in the Multi-scale Simulation of Polymer-based CNT Nanocomposites

A numeric model has been proposed to investigate the mechanical and electrical properties of a polymeric/carbon nanotube (CNT) composite material subjected to a deformation force. The reinforcing phase affects the behavior of the polymeric matrix and depends on the nanofiber aspect ratio and preferential orientation. The simulations show that the mechanical behavior of a computer generated material (CGM) depends on fiber length and initial orientation in the polymeric matrix. It is also shown how the conductivity of the polymer/CNT composite can be calculated for each time step of applied stress, effectively providing the ability to simulate and predict strain-dependent electrical behavior of CNT nanocomposites.

Applying complex network theory to the understanding of high-aspect-ratio carbon-filled composites

This work demonstrates that the theoretical framework of complex networks typically used to study systems such as social networks or the World Wide Web can be also applied to material science, allowing deeper understanding of fundamental physical relationships. In particular, through the application of the network theory to carbon nanotubes or vapour-grown carbon nanofiber composites, by mapping fillers to vertices and edges to the gap between fillers, the percolation threshold has been predicted and a formula that relates the composite conductance to the network disorder has been obtained. The theoretical arguments are validated by experimental results from the literature.

The influence of matrix mediated hopping conductivity, filler concentration, aspect ratio and orientation on the electrical response of carbon nanotube/polymer nanocomposites

A model to simulate the conductivity of carbon nanotube/polymer nanocomposites is presented. The proposed model is based on hopping between the fillers. A parameter related to the influence of the matrix in the overall composite conductivity is defined. It is demonstrated that increasing the aspect ratio of the fillers will increase the conductivity. Finally, it is demonstrated that the alignment of the filler rods parallel to the measurement direction results in higher conductivity values, in agreement with results from recent experimental work.

Energy harvesting performance of piezoelectric electrospun polymer fibers and polymer/ceramic composites

The energy harvesting efficiency of electrospun poly(vinylidene fluoride), its copolymer vinylidene fluoride-trifluoroethylene and composites of the later with piezoelectric BaTiOon interdigitated electrodes has been investigated. Further, a study of the influence of the electrospinning processing parameters on the size and distribution of the composites fibers has been performed. It is found that the best energy harvesting performance is obtained for the pure poly(vinylidene fluoride) fibers, with power outputs up to 0.03 W and 25 W under low and high mechanical deformation. The copolymer and the composites show reduced power output due to increased mechanical stiffness. The obtained values, among the largest found in the literature, the easy processing and the low cost and robustness of the polymer, demonstrate the applicability of the developed system.

Stress affects theta activity in limbic networks and impairs novelty-induced exploration and familiarization

Exposure to a novel environment triggers the response of several brain areas that regulate emotional behaviors. Here, we studied theta oscillations within the hippocampus (HPC)-amygdala (AMY)-medial prefrontal cortex (mPFC) network in exploration of a novel environment and subsequent familiarization through repeated exposures to that same environment; in addition, we assessed how concomitant stress exposure could disrupt this activity and impair both behavioral processes. Local field potentials were simultaneously recorded from dorsal and ventral hippocampus (dHPC and vHPC respectively), basolateral amygdala (BLA) and mPFC in freely behaving rats while they were exposed to a novel environment, then repeatedly re-exposed over the course of 3 weeks to that same environment and, finally, on re-exposure to a novel unfamiliar environment. A longitudinal analysis of theta activity within this circuit revealed a reduction of vHPC and BLA theta power and vHPC-BLA theta coherence through familiarization which was correlated with a return to normal exploratory behavior in control rats. In contrast, a persistent over-activation of the same brain regions was observed in stressed rats that displayed impairments in novel exploration and familiarization processes. Importantly, we show that stress also affected intra-hippocampal synchrony and heightened the coherence between vHPC and BLA. In summary, we demonstrate that modulatory theta activity in the aforementioned circuit, namely in the vHPC and BLA, is correlated with the expression of anxiety in novelty-induced exploration and familiarization in both normal and pathological conditions.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.