Structure-dependent growth control in nanowire synthesis via on-film formation of nanowires

On-film formation of nanowires, termed OFF-ON, is a novel synthetic approach that produces high-quality, single-crystalline nanowires of interest. This versatile method utilizes stress-induced atomic mass flow along grain boundaries in the polycrystalline film to form nanowires. Consequently, controlling the magnitude of the stress induced in the films and the microstructure of the films is important in OFF-ON. In this study, we investigated various experimental growth parameters such as deposition rate, deposition area, and substrate structure which modulate the microstructure and the magnitude of stress in the films, and thus significantly affect the nanowire density. We found that Bi nanowire growth is favored in thermodynamically unstable films that facilitate atomic mass flow during annealing. A large film area and a large thermal expansion coefficient mismatch between the film and the substrate were found to be critical for inducing large compressive stress in a film, which promotes Bi nanowire growth. The OFF-ON method can be routinely used to grow nanowires from a variety of materials by tuning the material-dependent growth parameters

London’s segregated neighbourhoods by Wooyoung Lee

“We are being removed and dispersed from our own area, and the cheap restaurants we are going will be replaced by gastro pubs and very expensive eating places,” says Martin. Over the last 24 years, the East London resident has seen his neighborhood undergo dramatic changes. He has watched the residential area which has long been home for British working-class people, immigrants from Bangladeshi, Somalia, Ireland and Eastern Europe at different time periods shrinking slowly as luxury property developers and investors gentrify the historic working class neighbourhood

Simple two-step fabrication method of Bi2Te3 nanowires

Bismuth telluride (Bi2Te3) is an attractive material for both thermoelectric and topological insulator applications. Its performance is expected to be greatly improved when the material takes nanowire structures. However, it is very difficult to grow high-quality Bi2Te3 nanowires. In this study, a simple and reliable method for the growth of Bi2Te3 nanowires is reported, which uses post-sputtering and annealing in combination with the conventional method involving on-film formation of nanowires. Transmission electron microscopy study shows that Bi2Te3 nanowires grown by our technique are highly single-crystalline and oriented along [110] direction

Noise-aware Learning from Web-crawled Image-Text Data for Image Captioning

Image captioning is one of the straightforward tasks that can take advantage of large-scale web-crawled data which provides rich knowledge about the visual world for a captioning model. However, since web-crawled data contains image-text pairs that are aligned at different levels, the inherent noises (e.g., misaligned pairs) make it difficult to learn a precise captioning model. While the filtering strategy can effectively remove noisy data, however, it leads to a decrease in learnable knowledge and sometimes brings about a new problem of data deficiency. To take the best of both worlds, we propose a noise-aware learning framework, which learns rich knowledge from the whole web-crawled data while being less affected by the noises. This is achieved by the proposed quality controllable model, which is learned using alignment levels of the image-text pairs as an additional control signal during training. The alignment-conditioned training allows the model to generate high-quality captions of well-aligned by simply setting the control signal to desired alignment level at inference time. Through in-depth analysis, we show that our controllable captioning model is effective in handling noise. In addition, with two tasks of zero-shot captioning and text-to-image retrieval using generated captions (i.e., self-retrieval), we also demonstrate our model can produce high-quality captions in terms of descriptiveness and distinctiveness. Code is available at \url{https://github.com/kakaobrain/noc}

Noxious gas detection using carbon nanotubes with Pd nanoparticles

Noxious gas sensors were fabricated using carbon nanotubes [CNTs] with palladium nanoparticles [Pd NPs]. An increase in the resistance was observed under ammonia for both CNTs and CNT-Pd sensors. Under carbon monoxide [CO], the two sensors exhibited different behaviors: for CNT sensors, their resistance decreased slightly with CO exposure, whereas CNT-Pd sensors showed an increase in resistance. The sensing properties and effect of Pd NPs were demonstrated, and CNT-Pd sensors with good repeatability and fast responses over a range of concentrations may be used as a simple and effective noxious gas sensor at room temperature

Effectiveness analysis of a hard-kill underwater defense system for surface warships against wake-homing torpedo attack

We conducted simulations to analyze the effects of a hard-kill-type underwater defense system that defends friendly warships against an enemy wake-homing torpedo. Assuming that the enemy torpedo is a wake-homing torpedo, our surface warship detours to the prespecified evasive course by firing a hard-kill-type system, which is modeled as a passive acoustic homing-torpedo, to attack the enemy torpedo. We analyzed the effectiveness of a warship’s survival probability via Monte Carlo simulation, given the probabilistic angles of the launched torpedoes, to compare two cases where one used only evasive maneuvering and the other used the hard-kill-type underwater defense system with evasion at the same time. By changing the maximum torpedo detection range of a warship and the torpedo’s initial location, we observed that the resulting survival probability of a warship was above 61% with a hard-kill-type defense system, whereas it remained at 34% without a hard-kill defense system, the necessity of a hard-kill underwater defense system, especially against wake-homing torpedoes

Effectiveness analysis of a hard-kill underwater defense system for surface warships against wake-homing torpedo attack

We conducted simulations to analyze the effects of a hard-kill-type underwater defense system that defends friendly warships against an enemy wake-homing torpedo. Assuming that the enemy torpedo is a wake-homing torpedo, our surface warship detours to the prespecified evasive course by firing a hard-kill-type system, which is modeled as a passive acoustic homing-torpedo, to attack the enemy torpedo. We analyzed the effectiveness of a warship’s survival probability via Monte Carlo simulation, given the probabilistic angles of the launched torpedoes, to compare two cases where one used only evasive maneuvering and the other used the hard-kill-type underwater defense system with evasion at the same time. By changing the maximum torpedo detection range of a warship and the torpedo’s initial location, we observed that the resulting survival probability of a warship was above 61% with a hard-kill-type defense system, whereas it remained at 34% without a hard-kill defense system, the necessity of a hard-kill underwater defense system, especially against wake-homing torpedoes

Serum zinc deficiency could be associated with dementia conversion in Parkinson’s disease

BackgroundAssociation between heavy metals and Parkinson’s disease (PD) is well noted, but studies regarding heavy metal levels and non-motor symptoms of PD, such as PD’s dementia (PD-D), are lacking.MethodsIn this retrospective cohort study, we compared five serum heavy metal levels (Zn, Cu, Pb, Hg, and Mn) of newly diagnosed PD patients (n = 124). Among 124 patients, 40 patients were later converted to Parkinson’s disease dementia (PD-D), and 84 patients remained without dementia during the follow-up time. We collected clinical parameters of PD and conducted correlation analysis with heavy metal levels. PD-D conversion time was defined as the initiation time of cholinesterase inhibitors. Cox proportional hazard models were used to identify factors associated with dementia conversion in PD subjects.ResultsZn deficiency was significant in the PD-D group than in the PD without dementia group (87.53 ± 13.20 vs. 74.91 ± 14.43, p < 0.01). Lower serum Zn level was significantly correlated with K-MMSE and LEDD at 3 months (r = −0.28, p < 0.01; r = 0.38, p < 0.01). Zn deficiency also contributed to a shorter time to dementia conversion (HR 0.953, 95% CI 0.919 to 0.988, p < 0.01).ConclusionThis clinical study suggests that a low serum Zn level can be a risk factor for developing PD-D and could be used as a biological marker for PD-D conversion