An Electrochemical Gelation Method for Patterning Conductive PEDOT:PSS Hydrogels

Due to their high water content and macroscopic connectivity, hydrogels made from the conducting polymer PEDOT:PSS are a promising platform from which to fabricate a wide range of porous conductive materials that are increasingly of interest in applications as varied as bioelectronics, regenerative medicine, and energy storage. Despite the promising properties of PEDOT:PSS‐based porous materials, the ability to pattern PEDOT:PSS hydrogels is still required to enable their integration with multifunctional and multichannel electronic devices. In this work, a novel electrochemical gelation (“electrogelation”) method is presented for rapidly patterning PEDOT:PSS hydrogels on any conductive template, including curved and 3D surfaces. High spatial resolution is achieved through use of a sacrificial metal layer to generate the hydrogel pattern, thereby enabling high‐performance conducting hydrogels and aerogels with desirable material properties to be introduced into increasingly complex device architectures.     

Determining the optimum process parameter for grinding operations using robust process

We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L ₂₇ orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer-aided single-objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates (R _Ra ² =94.99 and R _Vb ² =92.73) that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process.     

Deep dictionary learning application in GPR B-scan images

This paper introduces GPR B-scan database which contains 180 labelled images to facilitate research in developing presentation algorithm for this challenging scenario. Along with GPR B-scan images, there are several other detections of buried objects that are explored in the literature. The next contribution of this research is a novel multilevel deep dictionary learning-based presentation buried object detection algorithm that can discern different kinds of materials. An efficient layer by layer training approach is formulated to learn the deep dictionaries followed by different classifiers as types of shape for buried objects. By changing the number of layers in proposed algorithm, performances in different classifiers are compared. It is possible to integrate the proposed algorithm with real-time systems because it is supervised and has high classification accuracy with 94.4%.     

Enzyme immobilization in a photosensitive conducting polymer bearing azobenzene in the main chain

A new photosensitive and thermosensitive monomer, namely bis(4-(3-thienyl ethylene)-oxycarbonyl)diazobenzene (TDAZO), was synthesized. The photochemical and thermal cis–trans isomerization of the monomer has been investigated. The rate constants of the photoisomerization of TDAZO in ACN and DCM were 0.195 and 0.308 min^?1, respectively. For spectroelectrochemical investigation and enzyme immobilization application, TDAZO copolymerized with thiophene and pyrrole. Electrochemical and spectroelectrochemical properties of P(TDAZO-co-Th) were investigated and invertase was immobilized in P(TDAZO-co-Py) copolymer. Immobilization of enzymes was carried out by the entrapment of the enzyme in conducting polymer matrices during electrochemical polymerization of pyrrole through thiophene moieties of the TDAZO. Optimum conditions for this electrode, such as pH, temperature, kinetic parameters (K _m and V _max) and operational stability were investigated. Kinetic parameters invertase-immobilized in copolymer were smaller than free enzyme. The optimum operational temperature was 10 °C higher for immobilized enzyme than that of the free enzyme. Due to strong interaction between enzyme and diazo group in the polymer main chain, thermal, pH and operational stability of enzyme has been enhanced.     

Full-complex amplitude modulation with binary spatial light modulators

Imperfections and nonrobust behavior of practical multilevel spatial light modulators (SLMs) degrade the performance of many proposed full-complex amplitude modulation schemes. We consider the use of more robust binary SLMs for this purpose. We propose a generic method, by which, out of K binary (or 1 bit) SLMs of size M×N, we effectively create a new 2(K)-level (or K bit) SLM of size M×N. The method is a generalization of the well-known concepts of bit plane representation and decomposition for ordinary gray scale digital images and relies on forming a properly weighted superposition of binary SLMs. When K is sufficiently large, the effective SLM can be regarded as a full-complex one. Our method is as efficient as possible from an information theoretical perspective. A 4f system is discussed as a possible optical implementation. This 4f system also provides a means for eliminating the undesirable higher diffraction orders. The components of the 4f system can easily be customized for different production technologies.     

Effects of dietary yeast autolysate (Saccharomyces cerevisiae) on performance,egg traits,egg cholesterol content,egg yolk fatty acid composition and humoral immune response of laying hens

BACKGROUND: The objective of this study was to determine the effects of dietary yeast autolysate on performance, egg traits, egg cholesterol content, egg yolk fatty acid composition, lipid oxidation of egg yolk, some blood parameters and humoral immune response of laying hens during a 16 week period. A total of 225 Hyline Brown laying hens, 22 weeks of age, were allocated equally to one control group and four treatment groups. Yeast autolysate (Saccharomyces cerevisiae, InteWall) was used at levels of 1, 2, 3 and 4 g kg^?1 in the diets of the first, second, third and fourth treatment groups respectively. RESULTS: Dietary treatments did not significantly affect body weight, feed intake and egg traits. Yeast autolysate supplementation increased egg production (P < 0.001) and egg weight (P < 0.001) and improved feed efficiency (P < 0.05). Yeast autolysate at levels of 2, 3 and 4 g kg^?1 decreased egg yolk cholesterol level as mg g^?1 yolk (P < 0.01) and blood serum levels of cholesterol and triglyceride (P < 0.05) and increased antibody titres to sheep red blood cells (P < 0.01). Total saturated fatty acids and the ratio of saturated/unsaturated fatty acids increased (P < 0.01) and total monounsaturated fatty acids (P < 0.001) decreased with yeast autolysate supplementation. CONCLUSION: Dietary yeast autolysate at levels of 2, 3 and 4 g kg^?1 had beneficial effects on performance, egg cholesterol content and humoral immune response. It is concluded that 2 g kg^?1 yeast autolysate will be enough to have beneficial effects in laying hens. Copyright © 2010 Society of Chemical Industry     

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around $270\text{km h}{}^{-1}$ and $28ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.