This work reports the fractal designs of planar interdigital electrodes for buckypaper-based micro-supercapacitors (MSC) and studies their influences on MSC performance for different fractal levels. The fractal designs used in this study were derived from the H-tree structure. The electrodes were realized using a standard lithography process followed by the vacuum-filtration technique. The geometrical complexity of MSC electrodes increases with the level of the fractal structures and possibly results in higher electrical capacitance. The areal capacitance as measured by cyclic voltammetry indicates that the device with the fractal design of Level 3 gives the greatest areal capacitance (18.82 mF/cm2). The measured galvanostatic charge/discharge curves reveal that as the level of the MSC fractal electrode increases the measured areal capacitance increases as well. With a measured current density of 1 mA/cm2, the areal capacitance of the Level-3 fractal-electrode MSC design (17.25 mF/cm2) is 33% greater than that of the standard interdigital-electrode design. A Ragone plot shows that the power density as well as the energy density of MSCs increases with the level of fractal design. Electrochemical impedance spectroscopy measurements are also reported. These measured results confirm that the fractal designs of interdigital electrodes improve the energy-storage performance of MSCs.

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In this work, a flexible micro-supercapacitor with interdigital planar buckypaper electrodes is presented. A simple fabrication process involving vacuum filtration method and SU-8 molding techniques is proposed to fabricate in-plane interdigital buckypaper electrodes on a membrane filter substrate. The proposed process exhibits excellent flexibility for future integration of the micro-supercapacitors (micro-SC) with other electronic components. The device’s maximum specific capacitance measured using cyclic voltammetry was 107.27 mF/cm2 at a scan rate of 20 mV/s. The electrochemical stability was investigated by measuring the performance of charge-discharge at different discharge rates. Devices with different buckypaper electrode thicknesses were also fabricated and measured. The specific capacitance of the proposed device increased linearly with the buckypaper electrode thickness. The measured leakage current was approximately 9.95 µA after 3600 s. The device exhibited high cycle stability, with 96.59% specific capacitance retention after 1000 cycles. A Nyquist plot of the micro-SC was also obtained by measuring the impedances with frequencies from 1 Hz to 50 kHz; it indicated that the equivalent series resistance value was approximately 18 Ω.

Background The coexistence of sarcopenia and dementia in aging populations is not uncommon, and they may share common risk factors and pathophysiological pathways. This study aimed to evaluate the relationship between brain atrophy and low lean mass in the elderly with impaired cognitive function. Methods This cross-sectional study included 168 elderly patients who visited the multi-disciplinary dementia outpatient clinic at Kaohsiung Chang Gung Memorial Hospital for memory issues, between 2017 and 2019. The body composition was assessed by dual energy X-ray absorptiometry (DEXA) and CT based skeletal muscle index including L3 skeletal muscle index (L3SMI) and masseter muscle mass index (MSMI). The brain atrophy assessment was measured by CT based visual rating scale. Possible predictors of low lean mass in the elderly with cognitive impairement were identified by binary logistic regression. ROC curves were generated from binary logistic regression. Results Among the 81 participants, 43 (53%) remained at a normal appendicular skeletal muscle index (ASMI), whereas 38 (47%) showed low ASMI. Compared with the normal ASMI group, subjects with low ASMI exhibited significantly lower BMI, L3SMI, and MSMI (all p < 0.05), and showed significant brain atrophy as assessed by visual rating scale (p < 0.001). The accuracy of predictive models for low ASMI in the elderly with cognitive impairment were 0.875, (Area under curve (AUC) = 0.926, 95% confidence interval [CI] 0.844–0.972) in model 1 (combination of BMI, GCA and L3SMI) and 0.885, (Area under curve (AUC) = 0.931, [CI] 0.857–0.979) in model 2 (combination of BMI, GCA and MSMI). Conclusions Global cortical atrophy and body mass index combined with either L3 skeletal muscle index or masseter skeletal muscle index can predict low lean mass in the elderly with cognitive impairment.