Andrew Keong Ng

Antibacterial action of dispersed single-walled carbon nanotubes on Escherichia coli and Bacillus subtilis investigated by atomic force microscopy

Single-walled carbon nanotubes (SWCNTs) exhibit strong antibacterial activities. Direct contact between bacterial cells and SWCNTs may likely induce cell damages. Therefore, the understanding of SWCNT-bacteria interactions is essential in order to develop novel SWCNT-based materials for their potential environmental, imaging, therapeutic, and military applications. In this preliminary study, we utilized atomic force microscopy (AFM) to monitor dynamic changes in cell morphology and mechanical properties of two typical bacterial models (gram-negative Escherichia coli and gram-positive Bacillus subtilis) upon incubation with SWCNTs. The results demonstrated that individually dispersed SWCNTs in solution develop nanotube networks on the cell surface, and then destroy the bacterial envelopes with leakage of the intracellular contents. The cell morphology changes observed on air dried samples are accompanied by an increase in cell surface roughness and a decrease in surface spring constant. To mimic the collision between SWCNTs and cells, a sharp AFM tip of 2 nm was chosen to introduce piercings on the cell surface. No clear physical damages were observed if the applied force was below 10 nN. Further analysis also indicates that a single collision between one nanotube and a bacterial cell is unlikely to introduce direct physical damage. Hence, the antibacterial activity of SWCNTs is the accumulation effect of large amount of nanotubes through interactions between SWCNT networks and bacterial cells.

All-carbon solid-state yarn supercapacitors from activated carbon and carbon fibers for smart textiles

Smart textiles are intelligent devices that can sense and respond to environmental stimuli. They require integrated energy storage to power their functions. An emerging approach is to build integratable fiber-/yarn-based energy storage devices. Here, we demonstrate all-carbon solid-state yarn supercapacitors using commercially available activated carbon and carbon fiber yarns for smart textiles. Conductive carbon fibers concurrently act as current collectors in yarn supercapacitors and as substrates for depositing large surface area activated carbon particles. Two hybrid carbon yarn electrodes were twisted together in polyvinyl alcohol/H3PO4 polymer gel, which is used as both an electrolyte and a separator. A 10 cm long yarn supercapacitor, with the optimum composition of 2.2 mg cm−1 activated carbon and 1 mg cm−1 carbon fiber, shows a specific length capacitance of 45.2 mF cm−1 at 2 mV s−1, an energy density of 6.5 μW h cm−1, and a power density of 27.5 μW cm−1. Since the yarn supercapacitor has low equivalent series resistance at 4.9 Ω cm−1, longer yarn supercapacitors up to 50 cm in length were demonstrated, yielding a high total capacitance of up to 1164 mF. The all-carbon solid-state yarn supercapacitors also exhibit excellent mechanical flexibility with minor capacitance decreases upon bending or being crumpled. Utilizing three long yarn supercapacitors, a wearable wristband was knitted; this wristband is capable of lighting up an LED indicator, demonstrating strong potential for smart textile applications.

Space-confined assembly of all-carbon hybrid fibers for capacitive energy storage: realizing a built-to-order concept for micro-supercapacitors

Miniaturized portable and wearable electronics have diverse power requirements, ranging from one microwatt to several milliwatts. Fiber-based micro-supercapacitors are promising energy storage devices that can address these manifold power requirements. Here, we demonstrate a hydrothermal assembly method using space confinement fillers to control the formation of nitrogen doped reduced graphene oxide and multi-walled carbon nanotube hybrid fibers. Consequently, the all-carbon hybrid fibers have tunable geometries, while maintaining good electrical conductivity, high ion-accessible surface area and mechanical strength; this allows us to address two important issues in micro-supercapacitor research. First, we found a clear correlation between the geometry of the hybrid fibers and their capacitive energy storage properties. Thinner fibers (30 μm in diameter) have higher specific volumetric capacitance (281 F cm−3), superior rate capability, and better length dependent performance. In contrast, larger-diameter hybrid fibers (236 μm in diameter) can achieve much higher specific length capacitance (42 mF cm−1). Second, we realized the first built-to-order concept for micro-supercapacitors by using all-carbon hybrid fibers with diversified geometry as electrodes. The device energy can cover two orders of magnitude, from <0.1 μW h to nearly 10 μW h, and the device power can be tuned in four orders of magnitude, from 0.2 μW to 2000 μW. Furthermore, multiple mechanically flexible fiber-based micro-supercapacitors can be integrated into complex energy storage units with wider operation voltage windows, demonstrating broad application potentials in flexible devices.

Impact of obstructive sleep apnea on sleep-wake stage ratio

Patients with obstructive sleep apnea (OSA) experience fragmented sleep and exhibit different sleep architectures. While polysomnographic metrics for quantifying sleep architecture are studied, there is little information about the impact of OSA on the ratio of different sleep-wake stages (wake, W; rapid eye movement, REM; non-REM stages 1 to 3, N1 to N3). This study, therefore, aims to investigate the relationship between apnea-hypopnea index (AHI, a measure of OSA severity) and all possible ratios of sleep-wake stages. Sleep architectures of 24 adult subjects with suspected OSA were constructed according to the American Academy of Sleep Medicine scoring manual, and subsequently analyzed through various correlation (Pearson, Spearman, and Kendall) and regression (linear, logarithmic, exponential, and power-law) approaches. Results show a statistically significant positive, linear and monotonic correlation between AHI and REM/N3, as well as between AHI and N1/W (p-values <; 0.05). These findings imply that patients with increased severity of OSA may spend more time in REM than deep sleep, and in light sleep than wake (or less time in deep sleep than REM, and in wake than light sleep). A power-law regression model may possibly explain the relationships of AHI-REM/N3 and AHI-N1/W, and predict the value of AHI using REM/N3 or N1/W.

Automatic Sleep Onset Detection Using Single EEG Sensor

Sleep has been shown to be imperative for the health and well-being of an individual. To design intelligent sleep management tools, such as the music-induce sleep-aid device, automatic detection of sleep onset is critical. In this work, we propose a simple yet accurate method for sleep onset prediction, which merely relies on Electroencephalogram (EEG) signal acquired from a single frontal electrode in a wireless headband. The proposed method first extracts energy power ratio of theta (4-8Hz) and alpha (8-12Hz) bands along a 3-second shifting window, then calculates the slow wave of each frequency band along the time domain. The resulting slow waves are then fed to a rule-based engine for sleep onset detection. To evaluate the effectiveness of the approach, polysomnographic (PSG) and headband EEG signals were obtained from 20 healthy adults, each of which underwent 2 sessions of sleep events. In total, data from 40 sleep events were collected. Each recording was then analyzed offline by a PSG technologist via visual observation of PSG waveforms, who annotated sleep stages N1 and N2 by using the American Academy of Sleep Medicine (AASM) scoring rules. Using this as the gold standard, our approach achieved a 87.5% accuracy for sleep onset detection. The result is better or at least comparable to the other state of the art methods which use either multi-or single- channel based data. The approach has laid down the foundations for our future work on developing intelligent sleep aid devices.

Optimizing low-frequency common spatial pattern features for multi-class classification of hand movement directions

Recent studies have demonstrated that hand movement directions can be decoded from low-frequency electroencephalographic (EEG) signals. This paper proposes a novel framework that can optimally select dyadic filter bank common spatial pattern (CSP) features in low-frequency band (0-8 Hz) for multi-class classification of four orthogonal hand movement directions. The proposed framework encompasses EEG signal enhancement, dyadic filter bank CSP feature extraction, fuzzy mutual information (FMI)-based feature selection, and one-versus-rest Fishers linear discriminant analysis. Experimental results on data collected from seven human subjects show that (1) signal enhancement can boost accuracy by at least 4%; (2) low-frequency band (0-8 Hz) can adequately and effectively discriminate hand movement directions; and (3) dyadic filter bank CSP feature extraction and FMI-based feature selection are indispensable for analyzing hand movement directions, increasing accuracy by 6.06%, from 60.02% to 66.08%.

Automatic selection of neuronal spike detection threshold via smoothed Teager energy histogram

Spike detection is a prerequisite to analyzing neuronal activity. While simple spike detectors are favorable for hardware implementation, manual setting of spike detection threshold can be tedious and time-consuming, especially for extracellular recordings of multiple neuronal activity. This paper, therefore, investigates and proposes an automatic threshold selection using smoothed Teager energy histogram (STEH), with consideration of signal prewhitening, histogram bin width, and histogram equalization. Results from spikes with signal-to-noise ratio = 0.9-2.6 dB reveals that (1) prewhitening of neural signals can enhance true detection rate (TDR) by 10-20% at constant false alarm (FA) ranging 3-12 spikes/s; (2) Freedman-Diaconis choice of STEH bin width delivers higher TDR (1.52 ± 1.41%) and FA (0.48 ± 0.25 spikes/s) than square-root choice; and (3) histogram equalization can raise average TDR by 2.84% and FA by 1.01 spikes/s. Thresholds determined by STEH with signal prewhitening, Freedman-Diaconis choice or square-root choice of bin width, and histogram equalization fall around knee point of receiver operating characteristic curve, yielding average TDR = 87.88% and FA = 1.82 spikes/s for Freedman-Diaconis choice, and TDR = 86.49% and FA = 1.41 spikes/s for square-root choice of STEH bin width.

Managing rail corrugation through modelling, simulation, and instrumentation technologies

Rail corrugation is a common rail roughness phenomenon that can deteriorate the reliability, availability, maintainability, and safety of rail transportation. Detection of rail corrugation via the human eyes are labour-intensive and time-consuming. Furthermore, treatment of rail corrugation through routine rail grinding does not stop corrugation from recurring. To better control and monitor rail corrugation growth, this paper (1) reviews and discusses various causes and contributing factors of rail corrugation, as well as (2) compares and contrasts different direct and indirect technologies for measuring rail corrugation. The contributing factors include track system and geometry; rail metallurgy, padding and fastening; as well as sleeper metallurgy and spacing. The instrumentation technologies comprise corrugation analysis trolley, acoustic measurement system, vibration measurement system, hollow shaft sensing system, and onboard monitoring system. Dynamic simulation models and system designs, along with signal processing algorithms, are presented and described with statistical findings from simulations and field experiments. Results are encouraging, opening more research and development opportunities to better manage rail corrugation.