Keynote: Mechanics analysis and design of fractal interconnects for stretchable batteries

An important trend in electronics involves the development of materials, mechanical designs and manufacturing strategies that enable the use of unconventional substrates, such as polymer films, metal foils, paper sheets or rubber slabs. The last possibility is particularly challenging because the systems must accommodate not only bending but also stretching. Although several approaches are available for the electronics, a persistent difficulty is in power supplies that have similar mechanical properties, to allow their cointegration with the electronics. Here we introduce a set of materials and design concepts for a rechargeable lithium ion battery technology that exploits thin, low modulus silicone elastomers as substrates, with a segmented design in the active materials, and unusual “self-similar” interconnect structures between them. The result enables reversible levels of stretchability up to 300%, while maintaining capacity densities of ~1.1 mAh cm‑2. Stretchable wireless power transmission systems provide the means to charge these types of batteries, without direct physical contact

Axisymmetric thermo-mechanical analysis of laser-driven noncontact transfer printing

An axisymmetric thermo-mechanical model is developed for laser-driven noncontact transfer printing, which involves laser-induced impulsive heating to initiate separation at the interface between a soft, elastomeric stamp, and hard micro/nanomaterials (i.e., inks) on its surface due to a large mismatch in coefficients of thermal expansion. The result is the active ejection of the inks from the stamp, to a spatially separated receiving substrate, thereby representing the printing step. The model gives analytically the temperature field, and also a scaling law for the energy release rate for delamination at the interface between the stamp and an ink in the form of a rigid plate. The normalized critical laser pulse time for interfacial delamination depends only on the normalized absorbed laser power and width of the ink structure and has been verified by experiments

Keynote: Experiments and viscoelastic analysis of peel test with patterned strips for applications to transfer printing

Transfer printing is an exceptionally sophisticated approach to assembly and micro/nanofabrication that relies on a soft, elastomeric “stamp” to transfer solid, micro/nanoscale materials or device components from one substrate to another, in a large-scale, parallel fashion. The most critical control parameter in transfer printing is the strength of adhesion between the stamp and materials/devices. Conventional peel tests provide effective and robust means for determining the interfacial adhesion strength, or equivalently, the energy release rate, and its dependence on peel speed. The results presented here provide analytic solutions for tests of this type, performed using viscoelastic strips with and without patterns of relief on their surfaces, and validated by systematic experiments. For a flat strip, a simple method enables determination of the energy release rate as a function of the peel speed. Patterned strips can be designed to achieve desired interfacial properties, with either stronger or weaker adhesion than that for a flat strip. The pattern spacing influences the energy release rate, to give values that initially decrease to levels smaller than those for a corresponding flat strip, as the pattern spacing increases. Once the spacing reaches a critical value, the relief self-collapses onto the substrate, thereby significantly increasing the contact area and the strength of adhesion. Analytic solutions capture not only these behaviors, as confirmed by experiment, but also extend to strips with nearly any pattern geometry of cylindrical pillars

Buckling of stiff thin film on a prestrained bilayer substrate

Controlled buckling enables stretchable characteristic for brittle materials by integrating stiff device films on a soft substrate. Being permeable to fluids, the soft substrate, however, cannot encapsulate the device well and the system is also hard to be integrated with liquid components. In addition, the strength of the device system with a soft substrate is unsatisfactory in many biomedical applications. By introducing a bilayer substrate, we are able to provide a robust, high strength system while maintaining the stretchable characteristic, with a soft layer on top of a relatively stiff layer in the substrate. Theoretical investigation shows the design requirement for each substrate layer and it can guide the experimental design for a device to be used in a target application

High performance biodegradable semiconductor devices

A brief review of recent progress and a description of new advances in materials for bioresorbable semiconductor devices capture the current status of a class of a technology known as transient electronics. A summary of options in materials and devices illustrates some of the possibilities. Studies of the kinetics of silicon hydrolysis in various aqueous solutions, taken together with toxicity tests on live animal models, suggest potential for realistic use in biomedical implants and environmental monitors

Medical image retrieval with query-dependent feature fusion based on one-class SVM

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /

A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /