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I-Corps: Scalable Nanopillar Arrays

I-Corps: Scalable Nanopillar Arrays
I-Corps:可扩展纳米柱阵列
批准号:
1358450
负责人:
Nicholas Kotov
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-10-01 至 2014-03-31

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项目成果

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中文摘要
翻译
研究组开发出了一种以纳米柱周期性排列为基础的防伪标签制作方法,该方法可以在包括纺织品在内的任何表面上实现。利用喷墨逐层组装的优势,利用亲水性和疏水性纳米柱域的图案可以创建显性和隐性图像。然后,隐藏的图像被可视化,用人类的呼吸将它们雾化,以进行真实性验证。通过使用可扩展的聚氨酯纳米柱阵列,该技术也可能比其他选择成本更低。该技术提供了一个可靠的平台,可以制作具有可调谐光学和表面特性的周期性、密集排列的孔和柱阵列,具有许多技术应用。纳米柱技术为解决影响美国经济的假冒商品等大规模经济问题提供了一种新工具。在包括织物、玻璃、塑料等在内的广泛常见材料上制备耐磨纳米柱特征,使其能够大规模利用,能够推进除假冒标签以外的其他技术,包括能源应用(例如太阳能电池的自清洁耐用涂层)和生物医学技术(例如抗生物污染涂层)。
英文摘要
The research team has developed a method to produce anti-counterfeit tags based on periodic arrays of nanopillars that can be realized on any surfaces including textiles. Both overt and covert images can be created using patterns from hydrophilic and hydrophobic nanopillars domains taking advantage of the inkjet layer-by-layer assembly. The hidden images are then visualized for authenticity verification by fogging them with human breath. By utilizing scalable nanopillar arrays from polyurethane, this technology may also be less costly than other options.This technology presents a reliable platform to make periodic, densely packed hole and pillary arrays with tunable optical and surface properties, which has many technological applications. Nanopillar technology offers a new tool to combat such large-scale economic problems such as counterfeit goods that affect the US economy. Preparation of wear-resistant nanopillar features on a wide range of common materials including fabric, glass, plastic, etc enables their wide-scale utilization capable of advancing other technologies besides counterfeit tags that include energy applications (e.g. self-cleaning durable coatings for solar cells) and biomedical technologies (e.g. anti-biofouling coatings).
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会议论文
CENTER FOR COMPLEX PARTICLE SYSTEMS (COMPASS)
LOCK-AND-KEY INTERACTIONS BETWEEN CHIRAL NANOPARTICLES AND PROTEINS
Planning IUCRC at University of Michigan: Center for Hierarchical Emergent Materials (CHEM)
PFI-TT: Biomimetic Aramid Separators for Long-Lifetime Lithium-Sulfur Batteries
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis