Nanofiber Composites as Highly Active and Robust Anodes for Direct-Hydrocarbon Solid Oxide Fuel Cells.

Direct utilization of methane fuels in solid oxide fuel cells (SOFCs) is a key technology to realize the immediate inclusion of such high-efficiency fuel cells into the current electricity generation infrastructure. However, the broad commercialization of direct-methane fueled SOFCs is critically hindered by the inadequate electrode activity and their poor longevity, which primarily stems from the carbon build-up issues. To make the technology more competitive, a novel electrode structure that can dramatically improve the tolerance against coking is essential. Herein, we present highly active and robust core-shell nanofiber anodes, La0.75Sr0.25Cr0.5Mn0.5O3@Sm0.2Ce0.8O1.9 (LSCM@SDC), directly obtained with a single-nozzle electrospinning process through the use of two immiscible polymers. The intimate coverage of SDC on LSCM not only increases the active reaction sites but also promotes resistance toward carbon deposition and thermal aggregation. As such, the electrode polarization resistance obtained with LSCM@SDC NFs is among the lowest value ever reported with LSCM derivatives (∼0.11 Ω cm2 in wet H2 at 800 °C). The facile fabrication process of such complex heterostructures developed in this work is attractive for the design of not only SOFC electrodes but also other solid-state devices such as electrolysis cells, membrane reformers, and protonic cells.

High-Resolution, Fast, and Shape-Conformable Hydrogen Sensor Platform: Polymer Nanofiber Yarn Coupled with Nanograined Pd@Pt.

We report a flexible hydrogen sensing platform based on a single-strand yarn consisting of high-density electrospun nanofibers, on which nanograined Pd or Pd@Pt is coated via yarn spinning followed by sputter deposition. In general, Pd undergoes a phase transition to PdH x (α-PdH x at [H2] < 1% and ß-PdH x at [H2] > 2%), in which H atoms act as electron scattering centers, thus increasing the resistance. In our system, the sensors exhibit switchable H2 sensing behaviors, that is, (i) Δ R/ R0 > 0 at [H2] > 1% by the active electron scattering and (ii) Δ R/ R0 < 0 at [H2] < 1% derived from nanograined Pd effects. Due to high mechanical stability stemming from nanogranular morphologies of Pd, which is essential for enduring a huge volume expansion upon exposure to high-concentration H2, we could obtain a wide concentration range (4-0.0001%) H2 detection resolution. Moreover, an ultrathin Pt overlayer coated on Pd offers an accelerated H2 detection capability based on effective gas dissociation and activation properties. Furthermore, by virtue of the core (thread)-shell (nanofiber yarn) scaffold, long cycling reliability and flexibility were achieved. This facile and low-cost yarn fabrication method offers the development of single-strand thread-type wearable chemiresistors that possess a high surface area and open porosity, facilitating gas diffusion and reaction.

Bio-inspired heterogeneous sensitization of bimetal oxides on SnO2 scaffolds for unparalleled formaldehyde detection.

Multi-heterogeneous oxide sensing layers, i.e., NiO/Fe2O3 catalyst loaded SnO2 fiber-in-tube nanostructures (NiO/Fe2O3-FITs), were rationally synthesized from an electrospinning solution containing a bio-inspired chitosan-bimetal complex template. The NiO/Fe2O3-FITs showed remarkably enhanced sensing properties and superior cross-sensitivity toward sub-ppm levels of formaldehyde.

Heterogeneous Metal Oxide-Graphene Thorn-Bush Single Fiber as a Freestanding Chemiresistor.

The development of freestanding fiber-type chemiresistors, having high integration ability with various portable electronics including smart clothing systems, is highly demanding for the next-generation wearable sensing platforms. However, critical challenges stemming from the irreversible chemical sensing kinetics and weak reliability of the freestanding fiber-type chemiresistor hinder their practical use. In this work, for the first time, we report on the potential suitability of the freestanding and ultraporous reduced graphene oxide fiber functionalized with WO3 nanorods (porous WO3 NRs-RGO composite fiber) as a sensitive nitrogen dioxide (NO2) detector. By employing a tunicate cellulose nanofiber (TCNF), which is a unique animal-type cellulose, the numerous mesopores are formed on a wet-spun TCNF-GO composite fiber, unlike a bare GO fiber with dense surface structure. More interestingly, due to the superior wettability of TCNF, the aqueous tungsten precursor is uniformly adsorbed on an ultraporous TCNF-GO fiber, and subsequent heat treatment results in the thermal reduction of a TCNF-GO fiber and hierarchical growth of WO3 NRs perpendicular to the porous RGO fiber (porous WO3 NRs-RGO fiber). The freestanding porous WO3 NRs-RGO fiber shows a notable response to 1 ppm NO2. Furthermore, we successfully demonstrate reversible NO2 sensing characteristics of the porous WO3 NRs-RGO fiber, which is integrated on a wrist-type wearable sensing device.

Automated Tracking and Quantification of Autistic Behavioral Symptoms Using Microsoft Kinect.

The prevalence of autism spectrum disorder (ASD) has risen significantly in the last ten years, and today, roughly 1 in 68 children has been diagnosed. One hallmark set of symptoms in this disorder are stereotypical motor movements. These repetitive movements may include spinning, body-rocking, or hand-flapping, amongst others. Despite the growing number of individuals affected by autism, an effective, accurate method of automatically quantifying such movements remains unavailable. This has negative implications for assessing the outcome of ASD intervention and drug studies. Here we present a novel approach to detecting autistic symptoms using the Microsoft Kinect v.2 to objectively and automatically quantify autistic body movements. The Kinect camera was used to film 12 actors performing three separate stereotypical motor movements each. Visual Gesture Builder (VGB) was implemented to analyze the skeletal structures in these recordings using a machine learning approach. In addition, movement detection was hard-coded in Matlab. Manual grading was used to confirm the validity and reliability of VGB and Matlab analysis. We found that both methods were able to detect autistic body movements with high probability. The machine learning approach yielded highest detection rates, supporting its use in automatically quantifying complex autistic behaviors with multi-dimensional input.

Status of Plasmodium vivax malaria in the Republic of Korea, 2008-2009: decrease followed by resurgence.

The number of Plasmodium vivax malaria cases in the Republic of Korea (ROK) in 2008 was 1009, a 54.2% decrease on the previous year. It then resurged to 1317 cases in 2009 (30.5% increase on 2008). One possible cause for the sharp decrease in 2008 might be the large-scale presumptive anti-relapse therapy with primaquine that was undertaken in the Democratic People's Republic of Korea in 2007. Of the 2326 cases of P. vivax malaria diagnosed in the ROK during 2008-2009, 599 cases (25.8%) were military personnel, 535 cases (23.0%) were veterans, and 1192 cases (51.2%) were civilians. Local transmission within the ROK appeared to increase gradually, and the length of the transmission period of P. vivax malaria extended during this period. Parasite clearance time after chloroquine treatment has increased in the late 2000s, which requires the introduction of countermeasures against the decreasing chloroquine susceptibility, including reduction of mass chemoprophylaxis with chloroquine in the ROK Army.