Published May 15, 2023 | Version v1.0
Dataset Open

FTIR dataset from the article "Resistance to Degradation of Silk Fibroin Hydrogels Exposed to Neuroinflammatory Environments"

  • 1. Center for Biomedical Technology, Universidad Politécnica de Madrid. 28223 Pozuelo de Alarcón (Madrid) Spain
  • 2. Neurocomputing and Neurorobotics Research Group, Faculty of Biology and Faculty of Optics, Universidad Complutense de Madrid, 28040 Madrid, Spain
  • 3. Laboratorio micro-CT UCM. Departamento de Radiología, Rehabilitación y Fisioterapia. Facultad de Medici-na. Universidad Complutense de Madrid, Madrid, Spain

Description

The attached archive encounters the FTIR data used for the calculation of the β-sheet content of the silk fibroin hydrogels in the in-vitro experiments. Such data is used in main Figures 2c and 2d, 4a and 7a as well as Supplementary Figure 2a of the associated article.

 

The acquisition of the files has been performed as indicated below:

  • Equipment: Thermo Scientific™ Nicolet™ iS™ 5 Spectrometer (Thermofisher, United States)
  • Acquisition Software: OMNIC 9.2.86 (Thermofisher, United States)
  • Number of Scans: 64
  • Data Spacing: 0.482 cm -1

 

The files have been reported as raw complete spectrum including the absorbances in the wavenumbers from 600 to 1500 cm -1 in the format of “.SPA”. In the following lines, we provide examples of software to process and analyze the spectra files included in the database.

  • Python version 3.8 to 3.10 upon the availability of the “SpectroChemPy” (Travert and Christian, 2023) function.
  • Matlab R2016a and newer upon the availability of the “LoadSpectra” (Oldenburg, 2023) function.
  • OMNIC 9 (Thermofisher, United States).
  • Essential FTIR (Operant LLC, United States).

 

Provision of the data in other formats is available upon request. Inquiries may be directed to Daniel González-Nieto or Mahdi Yonesi.

References:

OLDENBURG, K. 2023. LoadSpectra. MATLAB Central File Exchange.

TRAVERT, A. & CHRISTIAN, F. 2023. SpectroChemPy, a framework for processing, analyzing and modeling spectroscopic data for chemistry with Python. Github.

 

Files

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