Advanced Materials Technologies

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ISSN / EISSN : 2365-709X / 2365-709X
Published by: Wiley-Blackwell (10.1002)
Total articles ≅ 2,020
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Tassawar Hussain, , Chulmin Youn, Hojin Lee, Turgun Boynazarov, Boncheol Ku, Yu‐Rim Jeon, Hoonhee Han, Jong Hyeon Lee, , et al.
Published: 15 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100744

The publisher has not yet granted permission to display this abstract.
Gaia Kravanja, Inna A. Belyaeva, Luka Hribar, , Mikhail Shamonin,
Published: 15 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202101045

Abstract:
A simple method for structuring of the surface of a magnetoactive elastomer (MAE) on the tens of micrometers scale, which capabilities extend beyond conventional mold-based polymer casting, is reported. The method relies on the ablation of the material by absorption of nanosecond infrared pulses from a commercial laser. It is shown that it is possible to fabricate parallel lamellar structures with a high aspect ratio (up to 6:1) as well as structures with complex scanning trajectories. The method is fast (fabrication time for the 7 × 7 mm2 is about 60 s), and the results are highly reproducible. To illustrate the capabilities of the fabrication method, both orthogonal to the MAE surface and tilted lamellar structures are fabricated. These magnetosensitive lamellae can be easily bent by ±45° using an external magnetic field of about 230 mT. It is demonstrated that this bending allows one to control the sliding angle of water droplets in a great range between a sticky (>90°) and a sliding state (<20°). Perspectives on employing this fabrication technology for magnetosensitive smart surfaces in microfluidic devices and soft robotics are discussed.
, Matthias Paur, Kenji Watanabe, Takashi Taniguchi, Thomas Mueller
Published: 15 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100915

Abstract:
The high-speed modulation of the nanoscale light sources is of fundamental interest in nanophotonics. Here, electrically driven light emission from a metal–insulator–semiconductor heterostructure consisting of graphene, hexagonal boron nitride (h-BN), and monolayer tungsten disulfide (WS2) is demonstrated. Electroluminescence in these devices originates from radiative recombination of majority carriers (electrons) accumulated by electrostatic doping and hot minority carriers (holes) injected into monolayer WS2 from graphene through an ultrathin h-BN tunnel barrier. The devices are electrically driven with a radio frequency signal and electrical modulation of the light emission at frequencies up to 1.5 GHz is demonstrated. The high-speed WS2 tunnel diodes provide a promising path for on-chip nanophotonics.
Weiguo Wu, , Yiming Li, Mengli Li, Yang Chen, Yi Yang, Xiaodie Chen, Yuanyuan Wu, ,
Published: 14 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100708

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Kun Yang, Haonan Cheng, Bo Wang, Yongsong Tan, Ting Ye, Yongqiang Yang,
Published: 13 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100675

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Lewis Howell, Vasileios Anagnostidis,
Published: 13 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202101053

Abstract:
The encapsulation of cells together with micro-objects in monodispersed water-in-oil microdroplets offers a powerful means to perform quantitative biological studies within large cell populations. In such applications, accurate object detection is crucial to ensure control over the content for every compartment. In particular, the ability to rapidly count and localize objects is key to future applications in single-cell -omics, cellular aggregation, and cell-to-cell interactions. In this paper, the authors combine the Deep Learning object detector YOLOv4-tiny with microfluidic Image-Activated Droplet Sorting (DL-IADS), to perform flexible, label-free classification, counting, and localization of multiple micro-objects simultaneously and at high-throughput. They trained YOLOv4-tiny to detect SH-SY5Y cells, polyacrylamide beads, and cellular aggregates in a single model, with a precision of 92% for cells, 98% for beads, and 81% for aggregates. They exploit this accuracy and counting ability to implement a closed-loop feedback that enables controlled loading of microbeads via the automated adjustment of flow rates. They subsequently demonstrate the combinatorial sorting of co-encapsulated single cells and single beads based on real-time classification at up to 111 Hz, with enrichment factors of up to 145. Finally, they demonstrate spatially-resolved sorts by evaluating cell-to-cell distances in real-time to isolate cell doublets with high purity.
Negar Zebardastan, Jonathan Bradford, Bharati Gupta, , Jennifer MacLeod, , , , Annalena Wolff, Kailong Hu, et al.
Published: 13 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100963

The publisher has not yet granted permission to display this abstract.
Meng Li, Hongyu Liu, Junqing Chang, Tiantian Dai, Kazuki Nagashima, , , ,
Published: 13 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100762

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Amandeep Singh, Minh Tam Hoang, Ngoc Duy Pham, Tony Wang, Joshua Mcdonald, Qin Li, Kostya (Ken) Ostrikov, Hongxia Wang,
Published: 13 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100583

The publisher has not yet granted permission to display this abstract.
Emmanuel Okogbue, Sohrab Alex Mofid, Changhyeon Yoo, Sang Sub Han,
Published: 12 October 2021
Advanced Materials Technologies; https://doi.org/10.1002/admt.202100639

The publisher has not yet granted permission to display this abstract.
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