Dataset Information

Kirigami-inspired multiscale patterning of metallic structures via predefined nanotrench templates.

ABSTRACT: Reliable fabrication of multiscale metallic patterns with precise geometry and size at both the nanoscale and macroscale is of importance for various applications in electronic and optical devices. The existing fabrication processes, which usually involve film deposition in combination with electron-beam patterning, are either time-consuming or offer limited precision. Inspired by the kirigami, an ancient handicraft art of paper cutting, this work demonstrates an electron-beam patterning process for multiscale metallic structures with significantly enhanced efficiency and precision. Similar to the kirigami, in which the final pattern is defined by cutting its contour in a paper and then removing the unwanted parts, we define the target multiscale structures by first creating nanotrench contours in a metallic film via an electron-beam-based process and then selectively peeling the separated film outside the contours. Compared with the conventional approach, which requires the exposure of the whole pattern, much less exposure area is needed for nanotrench contours, thus enabling reduced exposure time and enhanced geometric precision due to the mitigated proximity effect. A theoretical model based on interface mechanics allows a clear understanding of the nanotrench-assisted selective debonding behaviour in the peeling process. By using this fabrication process, multiscale metallic structures with sub-10-nm up to submillimetre features can be reliably achieved, having potential applications for anti-counterfeiting and gap-plasmon-enhanced spectroscopy.

SUBMITTER: Zheng M

PROVIDER: S-EPMC6885514 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Multiscale signal processing techniques such as wavelet filtering have proved to be particularly successful in predicting exon sequences. Traditional wavelet predictor is domain filtering, and enforces exon features by weighting nucleotide values with coefficients. Such a measure performs linear filtering and is not suitable for preserving the short coding exons and the exon-intron boundaries. This paper describes a prediction framework that is capable of non-linearly processing DNA sequences while achieving high prediction rates. There are two key contributions. The first is the introduction of a genomic-inspired multiscale bilateral filtering (MSBF) which exploits both weighting coefficients in the spatial domain and nucleotide similarity in the range. Similarly to wavelet transform, the MSBF is also defined as a weighted sum of nucleotides. The difference is that the MSBF takes into account the variation of nucleotides at a specific codon position. The second contribution is the exploitation of inter-scale correlation in MSBF domain to find the inter-scale dependency on the differences between the exon signal and the background noise. This favourite property is used to sharp the important structures while weakening noise. Three benchmark data sets have been used in the evaluation of considered methods. By comparison with four existing techniques, the prediction results demonstrate that: the proposed method reveals at least improvement of 4.1%, 50.5%, 25.6%, 2.5%, 10.8%, 15.5%, 11.1%, 12.3%, 9.2% and 2.4% on the exons length of 1-24, 25-49, 50-74, 75-99, 100-124, 125-149, 150-174, 175-199, 200-299 and 300-300+, respectively. The MSBF of its nonlinear nature is good at energy compaction, which makes it capable of locating the sharp variations around short exons. The direct scale multiplication of coefficients at several adjacent scales obviously enhanced exon features while the noise contents were suppressed. We show that the non-linear nature and correlation-based property achieved in proposed predictor is greater than that for traditional filtering, which leads to better exon prediction performance. There are some possible applications of this predictor. Its good localization and protection of sharp variations will make the predictor be suitable to perform fault diagnosis of aero-engine.

Dataset Information

Kirigami-inspired multiscale patterning of metallic structures via predefined nanotrench templates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure