Two-Phase Metallic Thermal Interface Materials Processed Through Liquid Phase Sintering Followed by Accumulative Roll Bonding

Thermal interface materials (TIMs) form a mechanical and thermal link between a heat source and a heat sink. Thus, they should have high thermal conductivity and high compliance to efficiently transfer heat and accommodate any differential strain between the heat source and the sink, respectively. This paper reports on the processing and the characterization of potential metallic TIM composite solders comprising of Cu, a high conductivity phase, uniformly embedded in In matrix, a highly compliant phase. We propose the fabrication of such a material by a two-step fabrication technique comprising of liquid phase sintering (LPS) followed by accumulative roll bonding (ARB). To demonstrate the efficacy of the employed two-step processing technique, an In-40 vol. % Cu composite solder was produced first using LPS with short sintering periods (30 or 60 s at 160 degrees C) followed by ARB up to five passes, each pass imposing a strain of 50%. Mechanical response and electrical and thermal conductivities of the fabricated samples were evaluated. It was observed that processing through ARB homogenizes the distribution of Cu in an In matrix, disintegrates the agglomerates of Cu powders, and also significantly increases thermal and electrical conductivities, almost attaining theoretically predicted values, without significantly increasing the flow stress. Furthermore, the processing technique also allows the insertion of desired foreign species, such as reduced graphene oxide, in In-Cu for further enhancing a target property, such as electrical conductivity.

No More HF: Teflon-Assisted Ultrafast Removal of Silica to Generate High-Surface-Area Mesostructured Carbon for Enhanced CO2 Capture and Supercapacitor Performance

An innovative technique to obtain high-surface-area mesostructured carbon (2545m(2)g(-1)) with significant microporosity uses Teflon as the silica template removal agent. This method not only shortens synthesis time by combining silica removal and carbonization in a single step, but also assists in ultrafast removal of the template (in 10min) with complete elimination of toxic HF usage. The obtained carbon material (JNC-1) displays excellent CO2 capture ability (ca. 26.2wt% at 0 degrees C under 0.88bar CO2 pressure), which is twice that of CMK-3 obtained by the HF etching method (13.0wt%). JNC-1 demonstrated higher H-2 adsorption capacity (2.8wt%) compared to CMK-3 (1.2wt%) at -196 degrees C under 1.0bar H-2 pressure. The bimodal pore architecture of JNC-1 led to superior supercapacitor performance, with a specific capacitance of 292Fg(-1) and 182Fg(-1) at a drain rate of 1Ag(-1) and 50Ag(-1), respectively, in 1m H2SO4 compared to CMK-3 and activated carbon.

No More HF: Teflon-Assisted Ultrafast Removal of Silica to Generate High-Surface-Area Mesostructured Carbon for Enhanced CO2 Capture and Supercapacitor Performance

An innovative technique to obtain high-surface-area mesostructured carbon (2545m(2)g(-1)) with significant microporosity uses Teflon as the silica template removal agent. This method not only shortens synthesis time by combining silica removal and carbonization in a single step, but also assists in ultrafast removal of the template (in 10min) with complete elimination of toxic HF usage. The obtained carbon material (JNC-1) displays excellent CO2 capture ability (ca. 26.2wt% at 0 degrees C under 0.88bar CO2 pressure), which is twice that of CMK-3 obtained by the HF etching method (13.0wt%). JNC-1 demonstrated higher H-2 adsorption capacity (2.8wt%) compared to CMK-3 (1.2wt%) at -196 degrees C under 1.0bar H-2 pressure. The bimodal pore architecture of JNC-1 led to superior supercapacitor performance, with a specific capacitance of 292Fg(-1) and 182Fg(-1) at a drain rate of 1Ag(-1) and 50Ag(-1), respectively, in 1m H2SO4 compared to CMK-3 and activated carbon.

Effect of morphology on the magnetic properties of Gd2O3 nanotubes

Gadolinium oxide (Gd2O3) nanotubes of micron length and average diameter 100 nm have been synthesized by a controlled template-assisted electrochemical deposition technique. Structure and morphology of the synthesized nanotubes have been well characterized by using microscopy and spectroscopy analyses. HRTEM and XRD analysis revealed the crystalline planes of Gd2O3 nanotubes. Magnetic measurements of the aligned Gd2O3 nanotubes have been performed for both parallel and perpendicular orientations of the magnetic field with respect to the axis of the Gd2O3 nanotube array. Large bifurcation in ZFC-FC over the regime of 2-320 K without any signature of long range magnetic ordering confirms the presence of SPM clusters in Gd2O3 nanotubes. Also, large magnetocaloric effect is observed in the cryogenic temperature regime. No anisotropy is seen at the low temperature region but is found to evolve with temperature and becomes significant 300 K. These nanotubes can be considered as promising candidates for magnetic refrigeration at cryogenic temperature. (C) 2016 Elsevier B.V. All rights reserved.

A mode-shape classification technique for robust speech rate estimation and syllable nuclei detection

Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem - mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F-scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively. (C) 2016 Elsevier B.V. All rights reserved.