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Optimization and Design of an Absorbance Spectrometer Controlled Using a Raspberry Pi to Improve Analytical Skills

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Title: Optimization and Design of an Absorbance Spectrometer Controlled Using a Raspberry Pi to Improve Analytical Skills
Language: English
Authors: Bougot-Robin, Kristelle, Paget, Jack, Atkins, Stephen C., Edel, Joshua B.
Source: Journal of Chemical Education. Jul 2016 93(7):1232-1240.
Availability: Division of Chemical Education, Inc and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Peer Reviewed: Y
Page Count: 9
Publication Date: 2016
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Chemistry, Science Instruction, Laboratory Equipment, Critical Thinking, Interpretive Skills, Computer Uses in Education, Programming, Least Squares Statistics, Computation, Outcomes of Education, Problem Based Learning, Undergraduate Students, College Science, Foreign Countries
Geographic Terms: United Kingdom (London)
DOI: 10.1021/acs.jchemed.5b01006
ISSN: 0021-9584
Abstract: It is not uncommon for students to view laboratory instruments as black boxes. Unfortunately, this can often result in poor experimental results and interpretation. To tackle this issue, a laboratory course was designed to enable students not only to critically think about operating principles of the instrument but also to improve interpretation skills. Students were required to build their own visible spectrometer using interlocking building bricks with simple optical elements and a Raspberry Pi computer. Experiments were then conducted to explore the instrumental capabilities while, at the same time, using Python programming to plot data, perform linear least-squares fitting, and calculate errors. Instrument response and spectral measurements were followed by kinetic studies, enabling the students to tackle a "real" problem by extracting rate constants. The main learning outcomes were that the students would gain a better understanding of instrumental components and at the same time learn valuable analytical techniques such as calibration, determination of the limits of linearity, and dynamic range. These outcomes were achieved by applying a problem based learning approach.
Abstractor: As Provided
Number of References: 15
Entry Date: 2016
Accession Number: EJ1108460
Database: ERIC
FullText Text:
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  Data: Optimization and Design of an Absorbance Spectrometer Controlled Using a Raspberry Pi to Improve Analytical Skills
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  Data: Division of Chemical Education, Inc and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
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  Data: It is not uncommon for students to view laboratory instruments as black boxes. Unfortunately, this can often result in poor experimental results and interpretation. To tackle this issue, a laboratory course was designed to enable students not only to critically think about operating principles of the instrument but also to improve interpretation skills. Students were required to build their own visible spectrometer using interlocking building bricks with simple optical elements and a Raspberry Pi computer. Experiments were then conducted to explore the instrumental capabilities while, at the same time, using Python programming to plot data, perform linear least-squares fitting, and calculate errors. Instrument response and spectral measurements were followed by kinetic studies, enabling the students to tackle a "real" problem by extracting rate constants. The main learning outcomes were that the students would gain a better understanding of instrumental components and at the same time learn valuable analytical techniques such as calibration, determination of the limits of linearity, and dynamic range. These outcomes were achieved by applying a problem based learning approach.
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  Data: EJ1108460
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