Liquid Metal Droplet and Graphene Co‐Fillers for Electrically Conductive Flexible Composites

Colloidal liquid metal alloys of gallium, with melting points below room temperature, are potential candidates for creating electrically conductive and flexible composites. However, inclusion of liquid metal micro‐ and nanodroplets into soft polymeric matrices requires a harsh auxiliary mechanical pressing to rupture the droplets to establish continuous pathways for high electrical conductivity. However, such a destructive strategy reduces the integrity of the composites. Here, this problem is solved by incorporating small loading of nonfunctionalized graphene flakes into the composites. The flakes introduce cavities that are filled with liquid metal after only relatively mild press‐rolling (<0.1 MPa) to form electrically conductive continuous pathways within the polymeric matrix, while maintaining the integrity and flexibility of the composites. The composites are characterized to show that even very low graphene loadings (≈0.6 wt%) can achieve high electrical conductivity. The electrical conductance remains nearly constant, with changes less than 0.5%, even under a relatively high applied pressure of >30 kPa. The composites are used for forming flexible electrically‐conductive tracks in electronic circuits with a self‐healing property. The demonstrated application of co‐fillers, together with liquid metal droplets, can be used for establishing electrically‐conductive printable‐composite tracks for future large‐area flexible electronics.     

Low complexity LMS-type adaptive algorithm with selective coefficient update for stereophonic acoustic echo cancellation

Stereophonic acoustic echo cancellation (SAEC) has brought up recently much attention and found a viable place in a number of hands-free applications. In this paper, we propose an LMS-type algorithm for SAEC based on decomposing the long adaptive filter of each channel of the SAEC system into smaller subfilters. We further reduce the complexity of the algorithm by employing the selective coefficient update (SCU) method in each subfilter. This leads to a significant improvement in the convergence rate of the algorithm with low computational overhead. However, the algorithm has a high final mean-square error (MSE) at steady-state that increases as number of subfilters increases. A combined-error algorithm is presented that achieves fast convergence without compromising the steady state error level. Simulations demonstrate the convergence speed advantages of the combined-error algorithm.     

Microsurface Reverse Engineering and Compensation for Laser Micromachining

Microsurface scale characteristics (roughness, waviness and form) and the workpiece mounting fixture effects must be accounted and compensated for during laser micromachining such that the focused laser spot position is known in the coordinates of the measured surfaces. Thus, allowing rapid and accurate micromachining on the true workpiece engineering surface. The thin-plate splines (TPSs), a mathematically simple theory, is modified and employed in the reconstruction of 2 1/2 D unfolded continuous and differentiable microtopographical surfaces from a limited set of sampled digital elevation data. The TPS theory aids in restoring bad samples and in enhancing the visualization of the reconstructed surface and the characterization of microelectromechanical systems (MEMS) structures. The reverse engineered surface could also be interfaced and used with a CAD/CAM system to compensate for the focal spot location of a laser beam based on the actual reversed engineered workpiece surface. The practical examples of the real microsurfaces presented in this work, combine comprehensive identification with the ultimate goal of utilizing the algorithms in the compensation of the laser focused spot for a femtosecond laser micromachining (FLM) system currently under development in our laboratory.     

Leaky LMS algorithm: MSE analysis for Gaussian data

Despite the widespread usage of the leaky LMS algorithm, there has been no detailed study of its performance. This paper presents an analytical treatment of the mean-square error (MSE) performance for the leaky LMS adaptive algorithm for Gaussian input data. The common independence assumption regarding W(n) and X(n) is also used. Exact expressions that completely characterize the second moment of the coefficient vector and algorithm steady-state excess MSE are developed. Rigorous conditions for MSE convergence are also established. Analytical results are compared with simulation and are shown to agree well     

Cooling strategy for effective automotive power trains: 3D thermal modeling and multi-faceted approach for integrating thermoelectric modules into proton exchange membrane fuel cell stack

The 3D Thermal modeling utilizes a Finite Differencing heat alteration method augmented with empirical boundary conditions is employed to develop 3D thermal model for the integration of thermoelectric modules with proton exchange membrane fuel cell stack. Hardware-in-Loop was designed under pre-defined drive cycle to obtain fuel cell performance parameters along with anode and cathode gas flow-rates and surface temperatures. The fuel cell model is used to conjugate the experimental boundary conditions with the Finite Differencing code, which implemented heat generation across the stack to depict the chemical composition process. The structural and temporal temperature contours obtained from this model are in compliance with the actual recordings obtained from the infrared detector and thermocouples. The model is harmonized with thermo-electric modules with a modeling strategy, which enables optimize better temporal profile across the stack. This study presents the improvement of a 3D thermal model for proton exchange membrane fuel cell stack along with the interfaced thermo-electric module. The model provided a virtual environment using a model-based design approach to assist the design engineers to manipulate the design correction earlier in the process and eliminate the need for costly and time consuming prototypes.     

Principal Component Analysis-Based Image Fusion Routine with Application to Automotive Stamping Split Detection

This study discusses the development and implementation of noncontact split detection method, for automotive stamping press lines. The system features a novel fusion routine that combines thermal and visible images in real-time, assisted with principle component analysis (PCA) subroutine. The thermal detector scans the temperature maps of the highly reflective steel sheets in the die cavity to locate abnormal temperature readings that might be indicative of high local wrinkling pressure, while the visible vision system offsets the blurring effect caused by heat diffusion across the surface and provide a spatial reference. The employed PCA uses a new singular value decomposition (SVD) that is more efficient than standard SVD computations, enabling the PCA to be applied in real-time acquisitions (～30 Hz). The PCA affects the images by reducing the nonvalue data content (reduce redundancy, noise) while highlighting important features. The fusion is done using a pixel-level algorithm using different variations, where each is assessed for performance. The proposed detection system has been tested on an automotive pressline to assess the formability of complex-shaped panels and have shown high detection success rate. Different splits with variant shape, size, and severity have been detected under actual operating conditions.     

Study of Reaction Between Slag and Carbonaceous Materials

The chemical interaction of a typical slag of EAF with three different carbon sources, coke, rubber-derived carbon (RDC), coke-RDC blend, was studied in atmospheric pressure at 1823 K (1550 °C). Using an IR-gas analyzer, off-gases evolved from the sample were monitored. While the coke-RDC blend exhibited the best reducing performance in reaction with molten slag, the RDC sample showed poor interaction with the molten slag. The gasification of the coke, RDC, and coke-RDC blend was also carried out under oxidizing conditions using a gas mixture of CO₂ (4 wt pct) and Ar (96 wt pct) and it was shown that the RDC sample had the highest rate of gasification step \( C_{0} \mathop{\longrightarrow}\limits{{k_{3} }}{\text{CO}} + nC_{\text{f}} \) (11.6 site/g s (×6.023 × 10²³/2.24 × 10⁴)). This may be attributed to its disordered structure confirmed by Raman spectra and its nano-particle morphology observed by FE-SEM. The high reactivity of RDC with CO₂ provided evidence that the Boudouard reaction was fast during the interaction with molten slag. However, low reduction rate of iron oxide from slag with RDC can be attributed to the initial weak contact between RDC and molten slag implying that the contact between carbonaceous matter and slag plays significant roles in the reduction of iron oxide from slag.     

Liquid-Metal-Templated Synthesis of 2D Graphitic Materials at Room Temperature

Room-temperature synthesis of 2D graphitic materials (2D-GMs) remains an elusive aim, especially with electrochemical means. Here, it is shown that liquid metals render this possible as they offer catalytic activity and an ultrasmooth templating interface that promotes Frank–van der Merwe regime growth, while allowing facile exfoliation due to the absence of interfacial forces as a nonpolar liquid. The 2D-GMs are formed at low onset potential and can be in situ doped depending on the choice of organic precursors and the electrochemical set-up. The materials are tuned to exhibit porous or pinhole-free morphologies and are engineered for their degree of oxidation and number of layers. The proposed liquid-metal-based room-temperature electrochemical route can be expanded to many other 2D materials.     

A novel superdisintegrating agent made from physically modified chitosan with silicon dioxide

Disintegrants and fillers represent important excipients for immediate-release solid dosage forms in many pharmaceutical applications. A new excipient based on the coprecipitation of chitosan and silica has been achieved. The “intimate” physical association between chitosan and silica creates an insoluble, hydrophilic, highly absorbent material, consequently, resulting in superiority in water uptake, water saturation for gelling formation, and compactability among other superdisintegrants. The new excipient has an outstanding functionality that does not primarily depend on water wicking and swelling properties. In fact, it translates it into superior disintegration characteristics with improved powder flow and compaction properties. Thus, the new excipient could act as a superdisintegrant and pharmaceutical filler at the same time. Studies have shown that chitosan-silica delivers superior performance in wet granulation formulations and is the only disintegrant that is effective at all concentrations in tablet formulation.     

Modeling Blanking Process Using Multiple Regression Analysis and Artificial Neural Networks

The design of blanking processes requires the availability of a procedure able to deal with both tooling and mechanical properties of the workpiece material (blank thickness, hardness, ductility, etc.). This research presents the development and comparison of two models to predict the quality of the blanked edge represented by burrs height, the first model is an artificial neural network (ANN) based, while the second model is a multiple regression analysis (MRA) based. Finite Element modeling of the blanking process was used to generate the data for both models. Both ANN and MRA are able to give good prediction results, however, ANN still more accurate because it deals efficiently with hidden nonlinear relations when compared to MRA. The comparison between experimental and model results shows that average absolute relative error in the case of ANN was <2.20% for carbon steel and 4.85% for corrosion-resistant steel (CRES) compared to 15.18% for carbon steel and 14.22% for CRES obtained from the second order MRA. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost.