Spontaneous assembly and real-time growth of micrometre-scale tubular structures from polyoxometalate-based inorganic solids

被引:0
|
作者
Ritchie C. [1 ]
Cooper G.J.T. [1 ]
Song Y.-F. [1 ]
Streb C. [1 ]
Yin H. [2 ]
Parenty A.D.C. [1 ]
MacLaren D.A. [3 ]
Cronin L. [1 ]
机构
[1] WestCHEM, Department of Chemistry
[2] Department of Electronics and Electrical Engineering, University of Glasgow
[3] Department of Physics and Astronomy, University of Glasgow
基金
英国工程与自然科学研究理事会;
关键词
D O I
10.1038/nchem.113
中图分类号
学科分类号
摘要
We report the spontaneous and rapid growth of micrometre-scale tubes from crystals of a metal oxide-based inorganic solid when they are immersed in an aqueous solution containing a low concentration of an organic cation. A membrane immediately forms around the crystal, and this membrane then forms micrometre-scale tubes that grow with vast aspect ratios at controllable rates along the surface on which the crystal is placed. The tubes are composed of an amorphous mixture of polyoxometalate-based anions and organic cations. It is possible for liquid to flow through the tubes, and for the direction of growth and the overall tube diameter to be controlled. We demonstrate that tube growth is driven by osmotic pressure within the membrane sack around the crystal, which ruptures to release the pressure. These robust, self-growing, micrometre-scale tubes offer opportunities in many areas, including the growth of microfluidic devices and the self-assembly of metal oxide-based semipermeable membranes for diverse applications. © 2009 Macmillan Publishers Limited. All rights reserved.
引用
收藏
页码:47 / 52
页数:5
相关论文
共 30 条
  • [21] MOVPE growth of (Al,Ga)InP-based laser structures monitored by real-time reflectance anisotropy spectroscopy
    Haberland K.
    Bhattacharya A.
    Zorn M.
    Weyers M.
    Zettler J.-T.
    Richter W.
    Journal of Electronic Materials, 2000, 29 (04) : 468 - 472
  • [22] MOVPE growth of (Al,Ga)InP-based laser structures monitored by real-time reflectance anisotropy spectroscopy
    Haberland, K
    Bhattacharya, A
    Zorn, M
    Weyers, M
    Zettler, JT
    Richter, W
    JOURNAL OF ELECTRONIC MATERIALS, 2000, 29 (01) : 94 - 98
  • [23] Real-time observation of crystals growth under optical microscopy: A self-assembly process of organic-inorganic intercalation composites driven by electrostatic interaction
    Zheng, Zhaokang
    Liu, Zhen
    Ma, Yujie
    Wang, Aiwu
    Zhou, Cangtao
    MATERIALS TODAY CHEMISTRY, 2024, 41
  • [24] Real-Time Marine Radar Observations of Nearshore Waves and Flow Structures from Shore-based Towers
    Haller, Merrick C.
    Honegger, David A.
    Pittman, Randall
    O'Dea, Annika
    Simpson, Alexandra
    2019 IEEE/OES TWELFTH CURRENT, WAVES AND TURBULENCE MEASUREMENT (CWTM), 2019,
  • [25] Enhancing Direct Georeferencing Using Real-Time Kinematic UAVs and Structure from Motion-Based Photogrammetry for Large-Scale Infrastructure
    Han, Soohee
    Han, Dongyeob
    Drones, 2024, 8 (12)
  • [26] Near real-time corn and soybean mapping at field-scale by blending crop phenometrics with growth magnitude from multiple temporal and spatial satellite observations
    Shen, Yu
    Zhang, Xiaoyang
    Tran, Khuong H.
    Ye, Yongchang
    Gao, Shuai
    Liu, Yuxia
    An, Shuai
    REMOTE SENSING OF ENVIRONMENT, 2025, 318
  • [27] Real-time polymerase chain reaction-based identification of bacteria in milk samples from bovine clinical mastitis with no growth in conventional culturing
    Taponen, S.
    Salmikivi, L.
    Simojoki, H.
    Koskinen, M. T.
    Pyorala, S.
    JOURNAL OF DAIRY SCIENCE, 2009, 92 (06) : 2610 - 2617
  • [28] Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms
    Chen Hongqian
    Hongwei Xin
    Teng Guanghui
    Meng Chaoying
    Du Xiaodong
    Mao Taotao
    Wang Cheng
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING, 2016, 9 (04) : 106 - 115
  • [29] AMSASeg: An Attention-Based Multi-Scale Atrous Convolutional Neural Network for Real-Time Object Segmentation From 3D Point Cloud
    Kim, Moogab
    Ilyas, Naveed
    Kim, Kiseon
    IEEE ACCESS, 2021, 9 : 70789 - 70796
  • [30] Training Machine Learning Surrogate Models From a High-Fidelity Physics-Based Model: Application for Real-Time Street-Scale Flood Prediction in an Urban Coastal Community
    Zahura, Faria T.
    Goodall, Jonathan L.
    Sadler, Jeffrey M.
    Shen, Yawen
    Morsy, Mohamed M.
    Behl, Madhur
    WATER RESOURCES RESEARCH, 2020, 56 (10)