Automated Dielectrophoretic Tweezers-Based Force Spectroscopy System in a Microfluidic Device.

We reported an automated dielectrophoretic (DEP) tweezers-based force spectroscopy system to examine intermolecular weak binding interactions, which consists of three components: (1) interdigitated electrodes and micro-sized polystyrene particles used as DEP tweezers and probes inside a microfluidic device, along with an arbitrary function generator connected to a high voltage amplifier; (2) microscopy hooked up to a high-speed charge coupled device (CCD) camera with an image acquisition device; and (3) a computer aid control system based on the LabVIEW program. Using this automated system, we verified the measurement reliability by measuring intermolecular weak binding interactions, such as hydrogen bonds and Van der Waals interactions. In addition, we also observed the linearity of the force loading rates, which is applied to the probes by the DEP tweezers, by varying the number of voltage increment steps and thus affecting the linearity of the force loading rates. This system provides a simple and low-cost platform to investigate intermolecular weak binding interactions.

Classification of ventricular arrhythmia using a support vector machine based on morphological features.

This paper proposes a method for the classification of ventricular arrhythmia using support vector machines (SVM). The features used in the SVMs were extracted automatically based on morphological information. Three different features were extracted: RR interval, QRS slope, and QRS shape similarity. Then, the SVM was used to classify five different electrocardiogram (ECG) heartbeat episodes. The Gaussian Radial Basis Function was utilized for the kernel function because the ECG beat episodes were treated as a non-linear pattern. The sensitivity of the classification used for the five beat episodes was 93.16%.

Detection technique of muscle activation intervals for sEMG signals based on the empirical mode decomposition.

The best way to detect the onset and offset time of muscle activation is through visual decision making by clinical experts like physical therapists. Humans can recognize muscle activation trends recorded from surface EMG signals. Current computer-based algorithms are being researched toward yielding similar results by clinical experts. A new algorithm in this paper has the ability, like humans, to recognize a trend from noisy input signals. We propose using the Empirical Mode Decomposition (EMD), because it is effectual to recognize trends which are decomposed by Hilbert transform and synthesized of Intrinsic Mode Functions (IMFs). These synthesized functions represent hidden low-frequency trends according to more iterative processes. Iterations will be stopped at the minimum SD of a resting period of EMG signals. The proposed method is very useful and easy implemented, but there are some limitations. The EMD method is only available on an off-line data and requires relatively high computational performances to find the IMFs. To use the proposed method, it is possible to detect muscle activation intervals of sEMG signals.

Control of the ZnO nanowires nucleation site using microfluidic channels.

We report on the growth of uniquely shaped ZnO nanowires with high surface area and patterned over large areas by using a poly(dimethylsiloxane) (PDMS) microfluidic channel technique. The synthesis uses first a patterned seed template fabricated by zinc acetate solution flowing though a microfluidic channel and then growth of ZnO nanowire at the seed using thermal chemical vapor deposition on a silicon substrate. Variations the ZnO nanowire by seed pattern formed within the microfluidic channel were also observed for different substrates and concentrations of the zinc acetate solution. The photocurrent properties of the patterned ZnO nanowires with high surface area, due to their unique shape, were also investigated. These specialized shapes and patterning technique increase the possibility of realizing one-dimensional nanostructure devices such as sensors and optoelectric devices.

Selective growth of vertically-aligned ZnO nano-needles.

Vertically-aligned zinc oxide (ZnO) nano-needles have been selectively grown on the Si (100) substrates using chemical vapor transport and condensation method without metal catalyst. The selective nucleation of nano-needles was achieved by the controlled treatment of substrate surface using zinc acetate aqueous solution. The nano-needles were selectively grown on the zinc acetate treated area, while the nano-tetrapod structures were formed on the non-treated area. The nano-needles have uniform tip-diameter and length, about 10 nm and 2-3 microm, respectively. The angle of the ZnO nano-needles from the substrate was 90 +/- 0.2 degrees. The structural and optical properties of nano-needles and nanotetrapod structures were investigated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and photoluminescence (PL). The results showed that ZnO nano-needles grow along the c-axis of the crystal plane due to the c-oriented ZnO nanoseeds formed by zinc acetate treatment. The nano-needles have strong ultraviolet emission peak of 3.29 eV with green emission of 2.3 eV at room temperature. This selective growth technique of vertical nano-needles using aqueous solution method has potential applications in the field emission devices or optoelectronic devices hybridized with silicon based electronic devices.