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A Low-Cost Spectrometry Remote Laboratory

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Artificial Intelligence and Online Engineering (REV 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 524))

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Abstract

The Covid-19 pandemic has precipitated the digital transformation in education worldwide and has exposed weaknesses and limitations in laboratory and experimental activities, mainly in the field of engineering. This forced us to provide rapid answers through a change in practice in our renewable energy program, from a conventional hands-on classroom experiment to a remote spectrometry laboratory. In this context, using costly spectrometry equipment that was not adapted to be operated remotely, was not an option.

In this paper we describe how we adapted our low-cost spectrometry technology (which is based on a 3D-printed mini-spectrometer and a smartphone) to deploy a remote laboratory as a rapid solution, due to the impossibility of using conventional and costly spectrometers, which work only for on-campus learning. This adaptation was helpful, not only to have several spectrometers available for a higher number of students, but also to allow teachers to prepare asynchronous activities that can be realized without their presence. We applied Internet of Things (IoT) technology for remotely controlling the experiments and used Machine Learning to automatically calibrate our low-cost smartphone spectrometer. We believe that such a low-cost spectrometry remote laboratory can benefit developing countries and enable the development of MOOC and MOOL type courses.

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Notes

  1. 1.

    https://labsland.com/en/about.

  2. 2.

    https://www.labwork.com.hk/about-us.

  3. 3.

    See several compact spectrometers at https://www.oceaninsight.com/products/spectrometers/microspectrometer/.

  4. 4.

    https://upb-edu.gitlab.io/spectraupb/.

  5. 5.

    http://www.imsglobal.org/activity/learning-tools-interoperability.

  6. 6.

    MQTT (Message Queue Telemetry Transport) is a lightweight, publish-subscribe network protocol that transports messages between devices.

  7. 7.

    https://www.espressif.com/en/products/socs/esp32.

  8. 8.

    https://micropython.org/.

  9. 9.

    http://umop.net/spectra/spectrum.php?elem=Hg.

  10. 10.

    http://umop.net/spectra/spectrum.php?elem=Ne.

  11. 11.

    https://angular.io/.

  12. 12.

    https://www.django-rest-framework.org/.

  13. 13.

    https://www.mysql.com/.

  14. 14.

    https://flutter.dev/.

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Acknowledgement

This work was partially funded by the ERASMUS+ Project “EUBBC-Digital” (No. 618925-EPP-1-2020-1-BR-EPPKA2-CBHE-JP).

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Correspondence to Alex Villazon .

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Villazon, A., Ormachea, O., Zenteno, A., Orellana, A. (2023). A Low-Cost Spectrometry Remote Laboratory. In: Auer, M.E., El-Seoud, S.A., Karam, O.H. (eds) Artificial Intelligence and Online Engineering. REV 2022. Lecture Notes in Networks and Systems, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-031-17091-1_21

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