Unraveling the Dual Defect Sites in Graphite Carbon Nitride for Ultra-high Photocatalytic H2O2 Evolution

Defect engineering modified graphite carbon nitride (g-C3N4) has been widely used in various photocatalytic systems due to the enhanced catalytic activity by multiple defect sites (such as vacancies or functional...

Integrated Multiple-Defect Detection and Evaluation of Rail Wheel Tread Images using Convolutional Neural Networks

The wheel-rail interface is regarded as the most important factor for the dynamic behaviour of a railway vehicle, affecting the safety of the service, the passenger comfort, and the life of the wheelset asset. The degradation of the wheels in contact with the rail is visibly manifest on their treads in the form of defects such as indentations, flats, cavities, etc. To guarantee a reliable rail service and maximise the availability of the rolling-stock assets, these defects need to be constantly and periodically monitored as their severity evolves. This inspection task is usually conducted manually at the fleet level and therefore it takes a lot of human resources. In order to add value to this maintenance activity, this article presents an automatic Deep Learning method to jointly detect and classify wheel tread defects based on smartphone pictures taken by the maintenance team. The architecture of this approach is based on a framework of Convolutional Neural Networks, which is applied to the different tasks of the diagnosis process including the location of the defect area within the image, the prediction of the defect size, and the identification of defect type. With this information determined, the maintenancecriteria rules can ultimately be applied to obtain the actionable results. The presented neural approach has been evaluated with a set of wheel defect pictures collected over the course of nearly two years, concluding that it can reliably automate the condition diagnosis of half the current workload and thus reduce the lead time to take maintenance action, significantly reducing engineering hours for verification and validation. Overall, this creates a platform or significant progress in automated predictive maintenance of rolling stock wheelsets.

Realizing quinary charge states of solitary defects in two-dimensional intermetallic semiconductor

Creating and manipulating multiple charge states of solitary defects in semiconductors is of essential importance for solitary defect electronics, but is fundamentally limited by Coulomb's law. Achieving this objective is challenging, due to the conflicting requirements of the localization necessary for the sizable band gap and delocalization necessary for a low charging energy. Here, using scanning tunneling microscopy/spectroscopy experiments and first-principles calculations, we realized exotic quinary charge states of solitary defects in two-dimensional intermetallic semiconductor Sn2Bi. We also observed an ultralow defect charging energy that increases sublinearly with charge number rather than displaying the usual quadratic behavior. Our work suggests a promising route for constructing multiple defect-charge states by designing intermetallic semiconductors, and opens new opportunities for developing quantum devices with charge-based quantum states.

Experimental verification of improved probability of detection model considering the effect of sensor’s location on low frequency electromagnetic monitoring signals

Structural health monitoring (SHM) is a promising method for maintaining the integrity of structures. A reasonable approach is necessary to quantify its detection uncertainty by taking into account the effect of random sensor locations on inspection signals. Recent studies of the authors proposed a model that adopts Monte Carlo simulation to numerically obtain the distribution of inspection signals influenced by random sensor locations. This model can evaluate the effect not only of multiple defect dimensions but also of the placement of sensors on the detection uncertainty. However, its effectiveness has only been confirmed using pseudo-experimental signals generated by artificial pollution. This study aims to examine the effectiveness of the proposed model in quantifying the detection uncertainty of SHM methods using the experimental signals of low frequency electromagnetic monitoring for inspecting wall thinning in pipes. The results confirm the capability of the proposed model to correctly characterize the distribution of inspection signals affected by random sensor locations and to determine the reasonable probability of detection.

Self Assembly of Arsenic Nanoparticles into Magnetic Nanotubules and Their SERS Activity

<p>Nano-Arsenic are synthesized using Bergenia cilliata roots extracts in water from Arsenic trioxide. The synthesized As nanoparticles, with an average diameter of 13(1) nm, self-assemble into nanotubles with average Feret diameter 530(20) nm. These As nanotubules/nanoparticles have direct band-gap of 2.74 eV and incorporates multiple defect related states. The presence of weak ferromagnetism in these nanotubules/nanoparticles are attributed to the dipolar inertactions amongst the charges on the defect sites. Owing to van derWaals interactions between nanotubules and smaller nanoparticles, nanotubules presents surface roughness which is utilized as surface enhanced raman spectroscopy substrate for probing methylene blue dye with an enhancement factor > 10³.</p>

Self Assembly of Arsenic Nanoparticles into Magnetic Nanotubules and Their SERS Activity

<p>Nano-Arsenic are synthesized using Bergenia cilliata roots extracts in water from Arsenic trioxide. The synthesized As nanoparticles, with an average diameter of 13(1) nm, self-assemble into nanotubles with average Feret diameter 530(20) nm. These As nanotubules/nanoparticles have direct band-gap of 2.74 eV and incorporates multiple defect related states. The presence of weak ferromagnetism in these nanotubules/nanoparticles are attributed to the dipolar inertactions amongst the charges on the defect sites. Owing to van derWaals interactions between nanotubules and smaller nanoparticles, nanotubules presents surface roughness which is utilized as surface enhanced raman spectroscopy substrate for probing methylene blue dye with an enhancement factor > 10³.</p>

Spectral Peculiarities of Multilayer Cholesteric Wedge-Cell System with Dye-Doped Polymer Layer-=SUP=-*-=/SUP=-

Spectral properties of the three-layered wedge-cell system of two identical cholesteric layers with an isotropic defect (dye-doped polymer layer) between them were investigated experimentally and theoretically. It was shown that multiple defect modes can be observed in this kind of system.s photonic bandgap which widen the application range of mentioned above system such as low threshold lasing, multi-position trigger, multiwavelength filters, light shutters, etc. Supporting simulation was also provided showing an agreement between experimental results and theoretical calculations. The problem was solved by Ambartsumian.s layer addition modified method. Keywords: cholesteric liquid crystals, chirality, photonic bandgap, defect modes, tuning, laser dye, polymer layer.