Carbon nanotube and graphene fiber artificial muscles

Actuator materials capable of producing a rotational or tensile motion are rare and, yet, rotary systems are extensively utilized in mechanical systems like electric motors, pumps, turbines and compressors. Rotating elements of such machines can be rather complex and, therefore, difficult to miniaturize. Rotating action at the microscale, or even nanoscale, would benefit from the direct generation of torsion from an actuator material. Herein we discuss the advantages of using carbon nanotube (CNT) yarns and/or graphene (G) fibers as novel artificial muscles that have the ability to be driven by the electrochemical charging of helically wound multiwall carbon nanotubes or graphene fibers as well as elements in the ambient environment such as moisture to generate such rotational action. The torsional strain, torque, speed and lifetime have been evaluated under various electrochemical conditions to provide insight into the actuation mechanism and performance. Here the most recent advances in artificial muscles based on sheath-run artificial muscles (SRAMs) are reviewed. Finally, the rotating motion of the CNT yarn actuator and the humidity-responsive twisted graphene fibers have been coupled to a mixer for use in a prototype microfluidic system, moisture management and a humidity switch respectively

Nanofibers-based piezoelectric energy harvester for self-powered wearable technologies

The demands for wearable technologies continue to grow and novel approaches for powering these devices are being enabled by the advent of new energy materials and novel manufacturing strategies. In addition, decreasing the energy consumption of portable electronic devices has created a huge demand for the development of cost-effective and environment friendly alternate energy sources. Energy harvesting materials including piezoelectric polymer with its special properties make this demand possible. Herein, we develop a flexible and lightweight nanogenerator package based on polyvinyledene fluoride (PVDF)/LiCl electrospun nanofibers. The piezoelectric performance of the developed nanogenator is investigated to evaluate effect of the thickness of the as-spun mat on the output voltage using a vibration and impact test. It is found that the output voltage increases from 1.3 V to 5 V by adding LiCl as additive into the spinning solution compared with pure PVDF. The prepared PVDF/LiCl nanogenerator is able to generate voltage and current output of 3 V and 0.5 µA with a power density output of 0.3 µW cm−2 at the frequency of 200 Hz. It is found also that the developed nanogenerator can be utilized as a sensor to measure temperature changes from 30◦C to 90◦C under static pressure. The developed electrospun temperature sensor showed sensitivity of 0.16%/◦C under 100 Pa pressure and 0.06%/◦C under 220 Pa pressure. The obtained results suggested the developed energy harvesting textiles have promising applications for various wearable self-powered electrical devices and systems

Dynamic mechanical and creep behaviour of meltspun pvdf nanocomposite fibers

Piezoelectric fibers have an important role in wearable technology as energy generators and sensors. A series of hybrid nanocomposite piezoelectric fibers of polyinylidene fluoride (PVDF) loaded with barium–titanium oxide (BT) and reduced graphene oxide (rGO) were prepared via the melt spinning method. Our previous studies show that high-performance fibers with 84% of the electroactive β-phase in the PVDF generated a peak output voltage up to 1.3 V and a power density of 3 W kg−1. Herein, the dynamic mechanical and creep behavior of these fibers were investigated to evaluate their durability and piezoelectric performance. Dynamic mechanical analysis (DMA) was used to provide phenomenological information regarding the viscoelastic properties of the fibers in the longitudinal direction. DSC and SEM were employed to characterize the crystalline structure of the samples. The storage modulus and the loss tangent increased by increasing the frequency over the temperature range (−50 to 150 °C) for all of the fibers. The storage modulus of the PVDF/rGO nanocomposite fibers had a higher value (7.5 GPa) in comparison with other fibers. The creep and creep recovery behavior of the PVDF/nanofillers in the nanocomposite fibers have been explored in the linear viscoelastic region at three different temperatures (10–130 °C). In the PVDF/rGO nanocomposite fibers, strong sheet/matrix interfacial interaction restricted the mobility of the polymer chains, which led to a higher modulus at temperatures 60 and 130 °C

Fast high fidelity quantum non-demolition qubit readout via a non-perturbative cross-Kerr coupling

Qubit readout is an indispensable element of any quantum information processor. In this work, we experimentally demonstrate a non-perturbative cross-Kerr coupling between a transmon and a polariton mode which enables an improved quantum non-demolition (QND) readout for superconducting qubits. The new mechanism uses the same experimental techniques as the standard QND qubit readout in the dispersive approximation, but due to its non-perturbative nature, it maximizes the speed, the single-shot fidelity and the QND properties of the readout. In addition, it minimizes the effect of unwanted decay channels such as the Purcell effect. We observed a single-shot readout fidelity of 97.4% for short 50 ns pulses, and we quantified a QND-ness of 99% for long measurement pulses with repeated single-shot readouts

3D-printed coaxial hydrogel patches with mussel-inspired elements for prolonged release of gemcitabine

With the aim of fabricating drug-loaded implantable patches, a 3D printing technique was employed to produce novel coaxial hydrogel patches. The core-section of these patches contained a dopamine-modified methacrylated alginate hydrogel loaded with a chemotherapeutic drug (Gemcitabine), while their shell section was solely comprised of a methacrylated alginate hydrogel. Subsequently, these patches were further modified with CaCO3 cross linker and a polylactic acid (PLA) coating to facilitate prolonged release of the drug. Consequently, the results showed that addition of CaCO3 to the formula enhanced the mechanical properties of the patches and significantly reduced their swelling ratio as compared to that for patches without CaCO3. Furthermore, addition of PLA coating to CaCO3-containing patches has further reduced their swelling ratio, which then significantly slowed down the release of Gemcitabine, to a point where 4-layered patches could release the drug over a period of 7 days in vitro. Remarkably, it was shown that 3-layered and 4-layered Gemcitabine loaded patches were successful in inhibiting pancreatic cancer cell growth for a period of 14 days when tested in vitro. Lastly, in vivo experiments showed that gemcitabine-loaded 4-layered patches were capable of reducing the tumor growth rate and caused no severe toxicity when tested in mice. Altogether, 3D printed hydrogel patches might be used as biocompatible implants for local delivery of drugs to diseased site, to either shrink the tumor or to prevent the tumor recurrence after resection

Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

Ultrasound elastography estimates the mechanical properties of the tissue from two Radio-Frequency (RF) frames collected before and after tissue deformation due to an external or internal force. This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus. The quality of the strain image depends heavily on the underlying deformation, and even the best strain estimation algorithms cannot estimate a good strain image if the underlying deformation is not suitable. Herein, we introduce a new method for tracking the RF frames and selecting automatically the best possible pair. We achieve this by decomposing the axial displacement image into a linear combination of principal components (which are calculated offline) multiplied by their corresponding weights. We then use the calculated weights as the input feature vector to a multi-layer perceptron (MLP) classifier. The output is a binary decision, either 1 which refers to good frames, or 0 which refers to bad frames. Our MLP model is trained on in-vivo dataset and tested on different datasets of both in-vivo and phantom data. Results show that by using our technique, we would be able to achieve higher quality strain images compared to the traditional methods of picking up pairs that are 1, 2 or 3 frames apart. The training phase of our algorithm is computationally expensive and takes few hours, but it is only done once. The testing phase chooses the optimal pair of frames in only 1.9 ms

Study of one class boundary method classifiers for application in a video-based fall detection system

In this paper, we introduce a video-based robust fall detection system for monitoring an elderly person in a smart room environment. Video features, namely the centroid and orientation of a voxel person, are extracted. The boundary method, which is an example one class classification technique, is then used to determine whether the incoming features lie in the ‘fall region’ of the feature space, and thereby effectively distinguishing a fall from other activities, such as walking, sitting, standing, crouching or lying. Four different types of boundary methods, k-center, k-th nearest neighbor, one class support vector machine and single class minimax probability machine are assessed on representative test datasets. The comparison is made on the following three aspects: 1). True positive rate, false positive rate and geometric means in detection 2). Robustness to noise in the training dataset 3). The computational time for the test phase. From the comparison results, we show that the single class minimax probability machine achieves the best overall performance. By applying one class classification techniques with 3-d features, we can obtain a more efficient fall detection system with acceptable performance, as shown in the experimental part; besides, it can avoid the drawbacks of other traditional fall detection methods

Image-based pore-scale modelling of the effect of wettability on breakthrough capillary pressure in gas diffusion layers

Wettability design is of crucial importance for the optimization of multiphase flow behaviour in gas diffusion layers (GDLs) in fuel cells. The accumulation of electrochemically-generated water in the GDLs will impact fuel cell performance. Hence, it is necessary to understand multiphase displacement to design optimal pore structures and wettability to allow the rapid flow of gases and water in GDLs over a wide saturation range. This work uses high-resolution in situ three-dimensional X-ray imaging combined with a pore network model to investigate the breakthrough capillary pressure and water saturation in GDLs manufactured with different mass fractions of polytetrafluoroethylene coating: 5, 20, 40, and 60%, making them more hydrophobic. We first demonstrate that the pore network extraction method provides representative networks for the fibrous porous media examined. Then, using a pore-network flow model we simulate water invasion into initially gas-filled fibrous media, and analyze the effect of wettability on breakthrough capillary pressure and water saturation. With an appropriate pore-scale characterization of wettability, a pore network model can match experimental results and predict displacement behaviour