Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering

Lussier, Félix; Thibault, Vincent; Charron, Benjamin; Wallace, Gregory Q.; Masson, Jean-François

doi:10.1016/j.trac.2019.115796

Cited by 376 publications

(304 citation statements)

References 93 publications

Supporting

Mentioning

302

Contrasting

Order By: Relevance

“…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%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

View full text Add to dashboard Cite

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.

show abstract

Section: T E Dmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

View full text Add to dashboard Cite

show abstract

“…The ANN algorithm is one of the most popular multivariate techniques because it can be used for regression, classication and clustering purposes. The ANN mimics the action of a biological network of neurons, and integration with backpropagation (BP) 28 improves the training of feedforward neural networks for supervised learning. Therefore, this ANN-BP was utilized during the regression process.…”

Section: Resultsmentioning

confidence: 99%

Accurate chiral pattern recognition for amines from just a single chemosensor

et al. 2020

View full text Add to dashboard Cite

show abstract

“…CNN algorithms used as a decision-making aid to radiologists to accelerate the diagnosis of COVID -19 [33]. AI techniques for SERS [34]. A study has been developed using the biosensor based on a Field-Effect Transistor (FET) method of detecting SARS-CoV 2 virus [35].…”

Section: Background Virus Detectionmentioning

confidence: 99%

An Analysis Review, Detection Coronavirus Disease 2019 (COVID-19) based on Biosensor Application

Taha¹,

Arsad²,

Mashhadany³

2020

Preprint

View full text Add to dashboard Cite

The global spread of coronavirus disease (COVID -19) worldwide has had a significant effect on social and economic growth. The contamination keeps on advancing quickly and eccentrically, representing a significant test to its recognition and conclusion. Coronaviruses are commonly recognized by seclusion from tests, regardless of whether natural or clinical, utilizing some atomic science procedures, which can take a few days. In this work an analytical review of virus transmission, methods of diagnosing COVID -19 using artificial intelligence techniques to classify images and types of biosensors. At long last, the deformities and points of interest of each kind of sensor are recognized and examined. This exploration gives an explanatory audit of the utilization of crown infection COVID-19 in 2019. Related examinations were led utilizing five dependable databases, for example, Science Direct, IEEE Xplore, Scopus, Web of Science, and PubMed. An acceptable investigation is remembered for this audit, which can be depended upon as a logical database to put resources into another technique for recognizing COIVD-19.

show abstract

Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering

Cited by 376 publications

References 93 publications

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

Accurate chiral pattern recognition for amines from just a single chemosensor

An Analysis Review, Detection Coronavirus Disease 2019 (COVID-19) based on Biosensor Application

Contact Info

Product

Resources

About