Chang-Hyeon Kim

Effect of P(lLa-co-εCL) on the compatibility and crystallization behavior of PCL/PLLA blends

This article describes the compatibility of two semicrystalline polymers, poly(e-caprolactone) (PCL) and poly(l-lactic acid) (PLLA). The compatibility of the PCL/PLLA blends was enhanced by the compatibilizing effect of the poly(l,l-lactide-co-e-caprolactone) [P(lLA-co-eCL)]. A discussion details the effect of the concentration of the compatibilizing agent, the copolymer of l,l-lactide and e-caprolactone of a 50/50 mol ratio [P(lLA-co-eCL)], on the compatibility and the crystallization behavior of the blends of PCL and PLLA. It was found that the addition of P(lLA-co-eCL) could suppress the crystallization of PLLA at its Tc and induced the concurrent crystallization of PLLA and PCL.

Structure–property relationship in PCL/starch blend compatibilized with starch‐g‐PCL copolymer

The polycaprolactone (PCL)/starch blends were prepared by using the starch-g-PCL (SGCL) graft copolymers as compatibilizers, and their mechanical properties were correlated with the compatibilizing effect of the SGCL copolymers having various molecular structures. The modulus and strength of the PCL/starch blend were decreased, whereas the percent elongation and the toughness were increased remarkably with the addition of SGCL having appropriate graft structure. These property changes were analyzed in terms of the PCL crystallinity and the interfacial adhesion between the PCL matrix and starch dispersion phases, which were dominated by the compatibilizing effects of the SGCL copolymers.

Effect of PEG molecular weight on the tensile toughness of starch/PCL/PEG blends

The blend of a gelatinized starch and poly(e-caprolactone) (PCL) was prepared and the effect of starch gelatinization on the mechanical properties of the blend was studied. The gelatinization of starch resulted in good dispersion of the starch in the PCL matrix and a higher modulus and strength of the blend. The mechanical properties of the starch/PCL/poly(ethylene glycol) (PEG) blends were also investigated. From the change of the toughness of the blends with the PEG molecular weight, it was found that the blend containing PEG of molecular weight 3400 shows the highest tensile toughness. It was also found from the SEM images that the blend containing PEG of molecular weight 3400 had the smallest domain size of the starch dispersion phases, which implies that PEG of the proper molecular weight could effectively stabilize the interface of the starch/PCL blend. The PEG of the proper molecular weight seems to locate mainly at the interface between the starch and PCL phases and to interact with both the starch phase and the PCL phase. The interactions between starch and PEG and between PCL and PEG in the blend were studied using DSC and FTIR techniques.

Protein release microparticles based on the blend of poly(D,L-lactic-co-glycolic acid) and oligo-ethylene glycol grafted poly(L-lactide).

Bovine serum albumin (BSA), a model protein drug, was encapsulated with a microparticle based on the blend of poly(D,L-lactic-co-glycolic acid) (PLGA) and poly(L-lactide)-g-oligo(ethylene glycol) (PLLA-g-oligoEG). Effects of PLLA-g-oligoEG in the blend on degradation, characteristic properties, and release behavior of the microparticle were studied. Drug loading efficiency increased with increase in the graft frequency of oligoEG in the graft copolymer in the blend. The release of BSA was found to be more efficient for microparticles based on the blend than on the PLGA, which is due to the faster protein diffusion through the swollen phase of the hydrogel-like structure. The microparticles based on the blend showed a slower degradation and a lower pH shift compared to that of PLGA.

Synthesis and characterization of poly(ethylene glycol) grafted poly(L-lactide)

Poly(ethylene glycol) grafted poly(L-lactide) was prepared by ring opening polymerization of L-lactide and epoxy-terminated poly(ethylene glycol) methyl ether (PEGME). Stannous octoate and Al(Et)3·0.5 H2O were tested as polymerization catalysts, and Al(Et)3·0.5 H2O was found to be more effective for the ring-opening of the epoxy group of the modified PEGME monomer. The synthesized polymers were characterized by NMR and the efficiency of the incorporation of epoxy-terminated PEGME in the copolymer was determined.

Flexible Binder-Free Metal Fibril Mat-Supported Silicon Anode for High-Performance Lithium-Ion Batteries

We report the fabrication of a flexible and binder-free metal fibril mat-supported Si anode (Si@SFM) by a simple process. The fabricated Si@SFM anode showed a high discharge capacity, ∼3000 mAh g(-1) at a current rate of 300 mA g(-1), and exhibited stable capacity retention, 90% at a 1 C rate (2000 mA g(-1)) after 200 cycles. The rate capability of the electrode was still high even when both the charge and the discharge current rates were markedly increased at the same time (1234 mAh g(-1) for charge-discharge time of ∼12 min). Moreover, owing to its mechanical flexibility, the Si@SFM can be adopted as a key component of flexible lithium-ion batteries (LIBs). After cell packaging, the rechargeable flexible battery under bending stress showed only a little capacity fading (86% of initial capacity) at 1000 mA g(-1) over 150 cycles. These results suggest that the Si@SFM electrode is readily suitable for use in rechargeable flexible LIBs.

A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management

A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 × 2.25 mm 2 chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.

14.2 A 502GOPS and 0.984mW dual-mode ADAS SoC with RNN-FIS engine for intention prediction in automotive black-box system

Advanced driver-assistance systems (ADAS) are being adopted in automobiles for forward-collision warning, advanced emergency braking, adaptive cruise control, and lane-keeping assistance. Recently, automotive black boxes are installed in cars for tracking accidents or theft. In this paper, a dual-mode ADAS SoC is proposed to support both high-performance ADAS functionality in driving-mode (d-mode) and an ultra-low-power black box in parking-mode (p-mode). By operating in p-mode, surveillance recording can be triggered intelligently with the help of our intention-prediction engine (IPE), instead of always-on recording to extend battery life and prevent discharge.

Study on the ionic conductivity of polyether network polymer electrolytes: effect of the preparation method

Abstract In preparing the network polymer electrolytes, two different methods were taken in the incorporation of salt into the polymer network. In one method, the network polyether was dipped into the lithium salt solution with or without plasticizer, and in the other method the network formation was proceeded in the presence of the lithium salt in the reaction medium with or without plasticizer. We designated the former as the network polymer electrolyte (NPE), the latter as the salt added network polycondensate electrolyte (SNPE). For the NPEs, the ionic conductivities increased with decreasing the cross-linking degree, which was mainly attributed to the increase of the free ion fraction with the decrease in the cross-linking degree that was evidenced by 7 Li NMR relaxation studies. For the NPEs plasticized with MPEG 7 , the conductivities increased with increase of the MPEG 7 content, which was largely due to the increase of the mobility of the free ions. The ionic conductivity of the SNPE was higher than that of the NPE, which resulted from the lower microviscosity of the SNPE due to the larger amount of the contained linear polyether and the lower cross-linking degree.

14.6 A 0.62mW ultra-low-power convolutional-neural-network face-recognition processor and a CIS integrated with always-on haar-like face detector

Recently, face recognition (FR) based on always-on CIS has been investigated for the next-generation UI/UX of wearable devices. A FR system, shown in Fig. 14.6.1, was developed as a life-cycle analyzer or a personal black box, constantly recording the people we meet, along with time and place information. In addition, FR with always-on capability can be used for user authentication for secure access to his or her smart phone and other personal systems. Since wearable devices have a limited battery capacity for a small form factor, extremely low power consumption is required, while maintaining high recognition accuracy. Previously, a 23mW FR accelerator [1] was proposed, but its accuracy was low due to its hand-crafted feature-based algorithm. Deep learning using a convolutional neural network (CNN) is essential to achieve high accuracy and to enhance device intelligence. However, previous CNN processors (CNNP) [2–3] consume too much power, resulting in <10 hours operation time with a 190mAh coin battery.