Artificial intelligence in nanotechnology

Sacha, G. M.; Varona, Pablo

doi:10.1088/0957-4484/24/45/452002

Cited by 91 publications

(47 citation statements)

References 123 publications

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“…Furthermore, our AI-assisted quality inspection method is not limited to Raman analysis, and should be applicable to analysis output by other characterization tools, such as scanning electron microscopy, photoluminescence, scanning probe microcopy, etc. [17]. This study can be considered a first step to further improve the analytical performance of Raman spectra classification based on AI-algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, artificial intelligence (AI) tools such as machine-learning paradigms have been proposed to improve the yield of nanomaterials characterization methods [16,17]. For 2D material research, it has been reported that machine learning algorithms integrated with optical microscopy can be used to quantify thickness, impurities, and stacking order in mechanically exfoliated graphene and transition metal chalcogenides [18], and even automatically locate them [19].…”

Section: T E Dmentioning

confidence: 99%

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RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future.

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Section: Discussionmentioning

confidence: 99%

Section: T E Dmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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“…Similarly, the combination of STM and Raman spectroscopy has enabled UHV tip‐enhanced Raman spectroscopy as a tool to probe surface chemistry down to the single molecule limit . Finally, the application of artificial intelligence to materials research is beginning to reshape the discovery of new materials including 2D systems. For example, several developments linking machine learning and data mining with scanning probe image recognition, analysis, and interpretation have been demonstrated .…”

Section: Discussionmentioning

confidence: 99%

Interface Characterization and Control of 2D Materials and Heterostructures

2018

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2D materials and heterostructures have attracted significant attention for a variety of nanoelectronic and optoelectronic applications. At the atomically thin limit, the material characteristics and functionalities are dominated by surface chemistry and interface coupling. Therefore, methods for comprehensively characterizing and precisely controlling surfaces and interfaces are required to realize the full technological potential of 2D materials. Here, the surface and interface properties that govern the performance of 2D materials are introduced. Then the experimental approaches that resolve surface and interface phenomena down to the atomic scale, as well as strategies that allow tuning and optimization of interfacial interactions in van der Waals heterostructures, are systematically reviewed. Finally, a future outlook that delineates the remaining challenges and opportunities for 2D material interface characterization and control is presented.

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“…The last decade in nanotechnology research witnessed an increasing use of artificial intelligence tools (Sacha and Varona, 2013). Current and future perspectives in the nanocomputing hardware development can boost the field of artificial-intelligence-based applications.…”

Section: Nanomaterialsmentioning

confidence: 99%

Nanostructures: a platform for brain repair and augmentation

Vidu

Rahman

Mahmoudi

et al. 2014

Front. Syst. Neurosci.

108

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Nanoscale structures have been at the core of research efforts dealing with integration of nanotechnology into novel electronic devices for the last decade. Because the size of nanomaterials is of the same order of magnitude as biomolecules, these materials are valuable tools for nanoscale manipulation in a broad range of neurobiological systems. For instance, the unique electrical and optical properties of nanowires, nanotubes, and nanocables with vertical orientation, assembled in nanoscale arrays, have been used in many device applications such as sensors that hold the potential to augment brain functions. However, the challenge in creating nanowires/nanotubes or nanocables array-based sensors lies in making individual electrical connections fitting both the features of the brain and of the nanostructures. This review discusses two of the most important applications of nanostructures in neuroscience. First, the current approaches to create nanowires and nanocable structures are reviewed to critically evaluate their potential for developing unique nanostructure based sensors to improve recording and device performance to reduce noise and the detrimental effect of the interface on the tissue. Second, the implementation of nanomaterials in neurobiological and medical applications will be considered from the brain augmentation perspective. Novel applications for diagnosis and treatment of brain diseases such as multiple sclerosis, meningitis, stroke, epilepsy, Alzheimer's disease, schizophrenia, and autism will be considered. Because the blood brain barrier (BBB) has a defensive mechanism in preventing nanomaterials arrival to the brain, various strategies to help them to pass through the BBB will be discussed. Finally, the implementation of nanomaterials in neurobiological applications is addressed from the brain repair/augmentation perspective. These nanostructures at the interface between nanotechnology and neuroscience will play a pivotal role not only in addressing the multitude of brain disorders but also to repair or augment brain functions.

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Artificial intelligence in nanotechnology

Cited by 91 publications

References 123 publications

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

Interface Characterization and Control of 2D Materials and Heterostructures

Nanostructures: a platform for brain repair and augmentation

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