Sub-5 nm nanogap electrodes towards single-molecular biosensing

He, Qing; Tang, Longhua

doi:10.1016/j.bios.2022.114486

Cited by 9 publications

(7 citation statements)

References 76 publications

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“…Our demonstration of optical cavities with few-to sub-nanometre features therefore takes the first steps towards a new generation of integrated nanophotonic devices exhibiting extreme field intensities that may approach both the limits to light-matter interaction strength 39 and regimes where the continuum model of electromagnetism breaks down 40 . Such levels of confinement may impact a wide span of potential applications such as surface-enhanced Raman spectroscopy 17 , nonlinear optics 25 , biosensing 41 and quantum technologies 7,42 . For example, our self-assembled waveguide-coupled cavity features an unprecedented set of parameters: with a mode volume of 8.8 × 10 −4 cubic wavelengths and a loaded Q-factor of 1.4 × 10 4 , the light-matter interaction is enhanced by a Purcell factor of 1.3 × 10 6 with a wide bandwidth and a high on-resonance transmission.…”

Section: Discussionmentioning

confidence: 99%

Self-assembled photonic cavities with atomic-scale confinement

Babar,

Weis,

Tsoukalas

et al. 2023

Nature

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Despite tremendous progress in research on self-assembled nanotechnological building blocks, such as macromolecules1, nanowires2 and two-dimensional materials3, synthetic self-assembly methods that bridge the nanoscopic to macroscopic dimensions remain unscalable and inferior to biological self-assembly. By contrast, planar semiconductor technology has had an immense technological impact, owing to its inherent scalability, yet it seems unable to reach the atomic dimensions enabled by self-assembly. Here, we use surface forces, including Casimir–van der Waals interactions4, to deterministically self-assemble and self-align suspended silicon nanostructures with void features well below the length scales possible with conventional lithography and etching5, despite using only conventional lithography and etching. The method is remarkably robust and the threshold for self-assembly depends monotonically on all the governing parameters across thousands of measured devices. We illustrate the potential of these concepts by fabricating nanostructures that are impossible to make with any other known method: waveguide-coupled high-Q silicon photonic cavities6,7 that confine telecom photons to 2 nm air gaps with an aspect ratio of 100, corresponding to mode volumes more than 100 times below the diffraction limit. Scanning transmission electron microscopy measurements confirm the ability to build devices with sub-nanometre dimensions. Our work constitutes the first steps towards a new generation of fabrication technology that combines the atomic dimensions enabled by self-assembly with the scalability of planar semiconductors.

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Section: Discussionmentioning

confidence: 99%

Self-assembled photonic cavities with atomic-scale confinement

Babar,

Weis,

Tsoukalas

et al. 2023

Nature

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“…It is noteworthy that, although there are several reports in the literature for such vertical nanotrench fabrication methods, there is no demonstration of those nanostructures in rectifier circuits, as they are significant challenges in creating asymmetric electrode structures, which are needed to obtain rectification in diodes. For this reason, they have been traditionally applied in biosensing applications [494] or in photonics [495]. There is, thus, ample room for further investigations in the field of nanotechnology, specifically targeting radiofrequency energy harvesting.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

Roadmap on energy harvesting materials

et al. 2023

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Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g., combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g., smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and electromagnetic power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyzes the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

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“…They rely on the nanoscale confined sensing devices, such as nanopore [ 3 ] and nanogap electrodes, [ 4 ] to convert chemical molecular states to detectable electrical signals. [ 5‐6 ] With extraordinarily high sensitivity and spatial resolution, nanogap electrodes‐based sensors monitor changes in tunneling current to gather chemical information. The electronic state of the analytes and their interactions with the electrodes are both correlated to tunneling current.…”

Section: Background and Originality Contentmentioning

confidence: 99%

Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes^†

Yang,

Jiang,

Tian

et al. 2023

Chin. J. Chem.

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Comprehensive SummaryThe electrical tunneling sensors have excellent potential in the next generation of single‐molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution capabilities. Electrical tunneling signals that have been measured at a high sampling rate may provide detailed molecular information. Despite the extraordinarily large amount of data that has been gathered, it is still difficult to correlate signal transformations with molecular processes, which creates great obstacles for signal analysis. Machine learning is an effective tool for data analysis that is currently gaining more significance. It has demonstrated promising results when used to analyze data from single‐molecule electrical measurements. In order to extract meaningful information from raw measurement data, we have combined intelligent machine learning with tunneling electrical signals. For the purpose of analyzing tunneling electrical signals, we investigated the clustering approach, which is a classic algorithm in machine learning. A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model clustering. Additionally, customized statistical algorithms were designed. It has been proved to efficiently gather molecular information and enhance the effectiveness of data analysis.This article is protected by copyright. All rights reserved.

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Sub-5 nm nanogap electrodes towards single-molecular biosensing

Cited by 9 publications

References 76 publications

Self-assembled photonic cavities with atomic-scale confinement

Self-assembled photonic cavities with atomic-scale confinement

Roadmap on energy harvesting materials

Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes^†

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Sub-5 nm nanogap electrodes towards single-molecular biosensing

Cited by 9 publications

References 76 publications

Self-assembled photonic cavities with atomic-scale confinement

Self-assembled photonic cavities with atomic-scale confinement

Roadmap on energy harvesting materials

Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes†

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Product

Resources

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Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes^†