# Non-negative matrix factorization analysis of spatially-resolved photoemission spectra for epitaxially grown graphene on SiC

https://mdr.nims.go.jp/datasets/1add5ee5-c498-4812-9b9b-36a476334e05

## File

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## Id

1add5ee5-c498-4812-9b9b-36a476334e05

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-06-24T02:22:09.764510Z

## Updated at

2026-06-24T06:38:41.697672Z

## Published at

2026-06-24T09:27:28.276239Z

## Doi

https://doi.org/10.48505/nims.6365

## First published url

https://doi.org/10.1080/27660400.2026.2688747

## Date published

2026-06-19

## Recorded date published



## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Non-negative matrix factorization analysis of spatially-resolved photoemission
    spectra for epitaxially grown graphene on SiC
  title_type: original
  lang: en

## Description

- description: Spatially-resolved ARPES is a powerful tool for probing local electronic
    structures in low-dimensional materials, but its analysis becomes challenging
    for spatially inhomogeneous samples due to spectral variations, feature overlap,
    and minor shifts. Here, we present a practical framework based on non-negative
    matrix factorization (NMF), which decomposes ARPES spectra into physically interpretable
    components without relying on prior assumptions. Visualizing the activation matrix
    as spatial heatmaps reveals latent spectral structures and provides an intuitive
    map of how individual components are distributed, enabling identification of the
    domains and local electronic variations. We validate this framework using epitaxial
    graphene on SiC, demonstrating its ability to quantitatively disentangle spectral
    features associated with layer thickness, step structures, and growth conditions.
    This study establishes the NMF-based framework as a scalable and robust tool for
    managing large-scale datasets and assessing electronic inhomogeneity in low-dimensional
    materials.
  description_type: abstract
  lang: en

## Creator

- name: Masaki Imamura
  role: author
  organization: Saga University
  department: Synchrotron Light Application Center
- name: Kazutoshi Takahashi
  role: author

## Contact agent



## Publisher

organization: Taylor & Francis
ror: https://ror.org/

## Managing organization



## Keyword

- subject: Photoemission spectroscopy
  schema: not_defined
- subject: non-negative matrix factorization
  schema: not_defined
- subject: graphene
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: data-driven analysis
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Science and Technology of Advanced Materials
  issn: '27660400'
  volume: '6'
  article_number: '2688747'

## Conference



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## Instrument



## Instrument operator



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## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



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## Custom property



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