Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying charge density and dielectric environment of graphene using
  Raman spectroscopy and deep learning   The impact of the environment on graphene's properties such as strain, charge
density, and dielectric environment can be evaluated by Raman spectroscopy.
These environmental interactions are not trivial to determine, since they
affect the spectra in overlapping ways. Data preprocessing such as background
subtraction and peak fitting is typically used. Moreover, collected
spectroscopic data vary due to different experimental setups and environments.
Such variations, artifacts, and environmental differences pose a challenge in
accurate spectral analysis. In this work, we developed a deep learning model to
overcome the effects of such variations and classify graphene Raman spectra
according to different charge densities and dielectric environments. We
consider two approaches: deep learning models and machine learning algorithms
to classify spectra with slightly different charge density or dielectric
environment. These two approaches show similar success rates for high
Signal-to-Noise data. However, deep learning models are less sensitive to
noise. To improve the accuracy and generalization of all models, we use data
augmentation through additive noise and peak shifting. We demonstrated the
spectra classification with 99% accuracy using a convolutional neural net (CNN)
model. The CNN model is able to classify Raman spectra of graphene with
different charge doping levels and even subtle variation in the spectra between
graphene on SiO$_2$ and graphene on silanized SiO$_2$. Our approach has the
potential for fast and reliable estimation of graphene doping levels and
dielectric environments. The proposed model paves the way for achieving
efficient analytical tools to evaluate the properties of graphene.

29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom