Data for: FLUO-SPICES: natural aldehydes extraction and one-pot reaction to prepare and characterize new interesting fluorophores

Published: 15 Jun 2018 | Version 1 | DOI: 10.17632/nsrvnv9w99.1
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Description of this data

Table of Contents:
CAS numbers................................................................................................................. S2
Student lab manual notes for experiment........................................................................ S3
Instructor lab manual notes for experiment..................................................................... S4
Grading rubric for report................................................................................................. S14
Work instructions for service room................................................................................... S14
Student feedback………………………………………………………………………………….……... S15

Experiment data files

This data is associated with the following publication:

FLUO-SPICES: natural aldehydes extraction and one-pot reaction to prepare and characterize new interesting fluorophores

Published in: Education for Chemical Engineers

Latest version

  • Version 1

    2018-06-15

    Published: 2018-06-15

    DOI: 10.17632/nsrvnv9w99.1

    Cite this dataset

    volpi, giorgio; Magistris, Claudio; Garino, Claudio (2018), “Data for: FLUO-SPICES: natural aldehydes extraction and one-pot reaction to prepare and characterize new interesting fluorophores”, Mendeley Data, v1 http://dx.doi.org/10.17632/nsrvnv9w99.1

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Categories

Fluorescence, Emissions, Mass Spectrum, At-Risk Students, NMR Artifacts

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This dataset is licensed under a Creative Commons Attribution 4.0 International licence. What does this mean? You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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