A Computer Simulation Study of Thermal and Mechanical Properties of Poly(Ionic Liquid)s.

Thermal and mechanical properties of poly(ionic liquid)s (PILs), an epoxidized ionic liquid-amine network, are studied via molecular dynamics simulations. The poly(ionic liquid)s are designed with two different ionic liquid monomers, 3-[2-(Oxiran-2-yl)ethyl]-1-{4-[(2-oxiran-2-yl)ethoxy]phenyl}imidazolium (EIM2) and 1-{4-[2-(Oxiran-2-yl)ethyl]phenyl}-3-{4-[2-(oxiran-2-yl)ethoxy]benzyl}imidazolium (EIM1), each of which is networked with tris(2-aminoethyl)amine, paired with different anions, bis(trifluoromethanesulfonyl)imide (TFSI-) and chloride (Cl-). We investigate how ionic liquid monomers with high ionic strength affect structures of the cross-linked polymer networks and their thermomechanical properties such as glass transition temperature (Tg) and elastic moduli, varying the degree of cross-linking. Strong electrostatic interactions between the cationic polymer backbone and anions build up their strong structures of which the strength depends on their molecular structures and anion size. As the anion size decreases from TFSI- to Cl-, both Tg and elastic moduli of the PIL increase due to stronger electrostatic interactions present between their ionic moieties, making it favorable for the PIL to organize with stronger bindings. Compared to the EIM2 monomer, the EIM1 monomers and TFSI- ions generate a PIL with higher Tg and elastic moduli. This attributes to the less flexible structure of the EIM1 monomer for the chain rotation, in which steric hindrance by ring moieties in the EIM1-based PIL enhances their structural rigidity. The π-π stacking structures between the rings are found to increase in EIM1-based PIL compared to the EIM2-based one, which becomes stronger with smaller Cl- ion rather than TFSI-. The effect of the degree of the cross-linking on thermal and mechanical properties is also examined. As the degree of cross-linking decreases from 100% to 60%, Tg also decreases by a factor of 10-20%, where the difference among the given PILs becomes decreased with a lower degree of cross-linking. Both the Young's (E) and shear (G) moduli of all the PILs decrease with degree of cross-linking, which the reduction is more significant for the PIL generated with EIM2 monomers. Transport properties of anions in PILs are also studied. Anions are almost immobilized globally with very small structural fluctuations, in which Cl- presents lower diffusivity by a factor of ~2 compared to TFSI- due to their stronger binding to the cationic polymer backbone.

Simulator acceleration and inverse design of fin field-effect transistors using machine learning.

The simulation and design of electronic devices such as transistors is vital for the semiconductor industry. Conventionally, a device is intuitively designed and simulated using model equations, which is a time-consuming and expensive process. However, recent machine learning approaches provide an unprecedented opportunity to improve these tasks by training the underlying relationships between the device design and the specifications derived from the extensively accumulated simulation data. This study implements various machine learning approaches for the simulation acceleration and inverse-design problems of fin field-effect transistors. In comparison to traditional simulators, the proposed neural network model demonstrated almost equivalent results (R2 = 0.99) and was more than 122,000 times faster in simulation. Moreover, the proposed inverse-design model successfully generated design parameters that satisfied the desired target specifications with high accuracies (R2 = 0.96). Overall, the results demonstrated that the proposed machine learning models aided in achieving efficient solutions for the simulation and design problems pertaining to electronic devices. Thus, the proposed approach can be further extended to more complex devices and other vital processes in the semiconductor industry.

Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network.

We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (T g), melting temperature (T m), density (ρ), and elastic modulus (E) with substantial dependence on the dataset, which is the best for T g (R 2 ∼ 0.9) and worst for E (R 2 ∼ 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with T g, as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property.

Polarization characteristics of semipolar (112̄2) InGaN/GaN quantum well structures grown on relaxed InGaN buffer layers and comparison with experiment.

Partial strain relaxation effects on polarization ratio of semipolar (112̄2) InxGa1−xN/GaN quantum well (QW) structures grown on relaxed InGaN buffers were investigated using the multiband effective-mass theory. The absolute value of the polarization ratio gradually decreases with increasing In composition in InGaN buffer layer when the strain relaxation ratio (ε0y'y'−εy'y')/ε0y'y' along y'-axis is assumed to be linearly proportional to the difference of lattice constants between the well and the buffer layer. Also, it changes its sign for the QW structure grown on InGaN buffer layer with a relatively larger In composition (x > 0.07). These results are in good agreement with the experiment. This can be explained by the fact that, with increasing In composition in the InGaN subsrate, the spontaneous emission rate for the y'-polarization gradually increases while that for x'-polarization decreases due to the decrease in a matrix element at the band-edge (k‖ = 0).

Highly stretchable resistive pressure sensors using a conductive elastomeric composite on a micropyramid array.

A stretchable resistive pressure sensor is achieved by coating a compressible substrate with a highly stretchable electrode. The substrate contains an array of microscale pyramidal features, and the electrode comprises a polymer composite. When the pressure-induced geometrical change experienced by the electrode is maximized at 40% elongation, a sensitivity of 10.3 kPa(-1) is achieved.

Highly stretchable electric circuits from a composite material of silver nanoparticles and elastomeric fibres.

Conductive electrodes and electric circuits that can remain active and electrically stable under large mechanical deformations are highly desirable for applications such as flexible displays, field-effect transistors, energy-related devices, smart clothing and actuators. However, high conductivity and stretchability seem to be mutually exclusive parameters. The most promising solution to this problem has been to use one-dimensional nanostructures such as carbon nanotubes and metal nanowires coated on a stretchable fabric, metal stripes with a wavy geometry, composite elastomers embedding conductive fillers and interpenetrating networks of a liquid metal and rubber. At present, the conductivity values at large strains remain too low to satisfy requirements for practical applications. Moreover, the ability to make arbitrary patterns over large areas is also desirable. Here, we introduce a conductive composite mat of silver nanoparticles and rubber fibres that allows the formation of highly stretchable circuits through a fabrication process that is compatible with any substrate and scalable for large-area applications. A silver nanoparticle precursor is absorbed in electrospun poly (styrene-block-butadiene-block-styrene) (SBS) rubber fibres and then converted into silver nanoparticles directly in the fibre mat. Percolation of the silver nanoparticles inside the fibres leads to a high bulk conductivity, which is preserved at large deformations (σ ≈ 2,200 S cm(-1) at 100% strain for a 150-µm-thick mat). We design electric circuits directly on the electrospun fibre mat by nozzle printing, inkjet printing and spray printing of the precursor solution and fabricate a highly stretchable antenna, a strain sensor and a highly stretchable light-emitting diode as examples of applications.

Cost- and time-effective three-dimensional bone-shape reconstruction from X-ray images.

BACKGROUND: Three-dimensional (3D) bone shapes need to be created for visualization and pre-operative surgery planning. Conventionally such shape data is extracted from volumetric data sets, obtained by three-dimensional sensors, such as computerized tomography (CT) and magnetic resonance imaging (MRI). This conventional method is highly labor intensive and time consuming. METHODS: This paper presents a cost- and time-effective computational method for generating a 3D bone shape from multiple X-ray images. Starting with a predefined 3D template bone shape that is clinically normal and scaled to an average size, our method scales and deforms the template shape until the deformed shape gives an image similar to an input X-ray image when projected onto a two-dimensional (2D) plane. The hierarchical freeform deformation method is used to scale and deform the template bone. The problem of finding the 3D shape of the bond is reduced to a sequence of optimization problems. The objective of this optimization is to minimize the error between the input X-ray image and the projected image of the deformed template shape. The sequential quadratic programming (SQP) is used to solve this multi-dimentional optimization problem. RESULTS: The proposed X-ray image-based shape reconstruction is more computationally efficient, cost-effective and portable compared to the conventional CT- or MRI-based methods. Within a couple of minutes with a standard personal computer, the proposed method generates a 3D bone shape that is sufficiently accurate for many applications, such as (a) making a 3D physical mock-up for training and (b) importing into, and using in, a computer-aided planning system for orthopedic surgery, including bone distraction and open/closed wedge osteotomy. CONCLUSIONS: Because the proposed method requires only a small number of X-ray images and a minimum input from the user, the method can serve as a cost- and time-effective 3D bone shape reconstruction method for various medical applications.