Spin-selective transport in semiconductor spintronics and single-defect quantum technology

2020 ◽  
Author(s):  
Stephen Ross McMillan
1994 ◽  
Vol 49 (4) ◽  
pp. 2959-2962 ◽  
Author(s):  
Kookjin Chun ◽  
Norman O. Birge

2021 ◽  
Vol 19 (1) ◽  
pp. 806-817
Author(s):  
Muhammad Cholid Djunaidi ◽  
Nabilah Anindita Febriola ◽  
Abdul Haris

Abstract High levels of urea and creatinine in the blood are a sign of decreased kidney function. To remove these substances from the blood, hemodialysis which utilizes membranes could be used. In this study, a molecularly imprinted membrane (MIM) was synthesized for the selective transport of urea. The synthesis is initiated with the polymerization of eugenol into polyeugenol and then into polyeugenoxy acetate (PA). The PA is then contacted with urea and then used as the functional polymer in the synthesis of MIM with polysulfone as the membrane base, and polyethylene glycol as the cross-linking agent. The result was later analyzed with FTIR and SEM-EDX. The membrane is then used in the transport of urea, creatinine, and vitamin B12 and then compared with the non-imprinted membrane (NIM) performance. By using UV-Vis spectrophotometry, the results showed that the membrane with 10 h heating variation is able to transport more urea and is more selective than NIM; this proves that the urea template on the MIM enables it to recognize urea molecules better than creatinine and vitamin B12. The order of transport from the best results is urea > creatinine > vitamin B12.


Photonics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 33
Author(s):  
Lucas Lamata

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.


2021 ◽  
Author(s):  
Chunying Li ◽  
Hui Chen ◽  
Xiaohai Yang ◽  
Kemin Wang ◽  
Jianbo Liu

A light-responsive ion transport switch has been developed based on conformation-dependent azobenzene-incorporated lipophilic G-quadruplex channels, which provides a new smart approach for the selective transport of K+ ions across the...


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhizhan Qiu ◽  
Matthew Holwill ◽  
Thomas Olsen ◽  
Pin Lyu ◽  
Jing Li ◽  
...  

AbstractThe discovery of two-dimensional (2D) magnetism combined with van der Waals (vdW) heterostructure engineering offers unprecedented opportunities for creating artificial magnetic structures with non-trivial magnetic textures. Further progress hinges on deep understanding of electronic and magnetic properties of 2D magnets at the atomic scale. Although local electronic properties can be probed by scanning tunneling microscopy/spectroscopy (STM/STS), its application to investigate 2D magnetic insulators remains elusive due to absence of a conducting path and their extreme air sensitivity. Here we demonstrate that few-layer CrI3 (FL-CrI3) covered by graphene can be characterized electronically and magnetically via STM by exploiting the transparency of graphene to tunneling electrons. STS reveals electronic structures of FL-CrI3 including flat bands responsible for its magnetic state. AFM-to-FM transition of FL-CrI3 can be visualized through the magnetic field dependent moiré contrast in the dI/dV maps due to a change of the electronic hybridization between graphene and spin-polarised CrI3 bands with different interlayer magnetic coupling. Our findings provide a general route to probe atomic-scale electronic and magnetic properties of 2D magnetic insulators for future spintronics and quantum technology applications.


2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Monia Makhoul ◽  
Philippe Beltrame

AbstractThis paper analyzes the possibility of obtaining the selective transport of microparticles suspended in air in a microgravity environment through modulated channels without net displacement of air. Using numerical simulation and bifurcation analysis tools, we show the existence of intermittent particle drift under the Stokes assumption of the fluid flow. The particle transport can be selective and the direction of transport is controlled only by the kind of pumping used. The selective transport is interpreted as a deterministic ratchet effect due to spatial variations in the flow and the particle drag. This ratchet phenomenon could be applied to the selective transport of metal particles during the short duration of microgravity experiments.


Crystals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 643
Author(s):  
Soo-Ho Jo ◽  
Byeng D. Youn

Several previous studies have been dedicated to incorporating double defect modes of a phononic crystal (PnC) into piezoelectric energy harvesting (PEH) systems to broaden the bandwidth. However, these prior studies are limited to examining an identical configuration of the double defects. Therefore, this paper aims to propose a new design concept for PnCs that examines differently configured double defects for broadband elastic wave energy localization and harvesting. For example, a square-pillar-type unit cell is considered and a defect is considered to be a structure where one piezoelectric patch is bonded to a host square lattice in the absence of a pillar. When the double defects introduced in a PnC are sufficiently distant from each other to implement decoupling behaviors, each defect oscillates like a single independent defect. Here, by differentiating the geometric dimensions of two piezoelectric patches, the defects’ dissimilar equivalent inertia and stiffness contribute to individually manipulating defect bands that correspond to each defect. Hence, with adequately designed piezoelectric patches that consider both the piezoelectric effects on shift patterns of defect bands and the characteristics for the output electric power obtained from a single-defect case, we can successfully localize and harvest the elastic wave energy transferred in broadband frequencies.


Metals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 223
Author(s):  
Dongcheng Wang ◽  
Yanghuan Xu ◽  
Bowei Duan ◽  
Yongmei Wang ◽  
Mingming Song ◽  
...  

The edge of a hot rolling strip corresponds to the area where surface defects often occur. The morphologies of several common edge defects are similar to one another, thereby leading to easy error detection. To improve the detection accuracy of edge defects, the authors of this paper first classified the common edge defects and then made a dataset of edge defect images on this basis. Subsequently, edge defect recognition models were established on the basis of LeNet-5, AlexNet, and VggNet-16 by using a convolutional neural network as the core. Through multiple groups of training and recognition experiments, the model’s accuracy and recognition time of a single defect image were analyzed and compared with recognition models with different learning rates and sample batches. The experimental results showed that the recognition model based on the AlexNet had a maximum accuracy of 93.5%, and the average recognition time of a single defect image was 0.0035 s, which could meet the industry requirement. The research results in this paper provide a new method and thought for the fine detection of edge defects in hot rolling strips and have practical significance for improving the surface quality of hot rolling strips.


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