AAMG 2015 Measurement, Information and Innovation October 2015 Automation in Analytical Laboratories: A teaching perspective Krishna Persaud CEAS, The University of Manchester, UK

AAMG 2015 Introduction “The dependence of science and technology grows relentlessly. From the basic application of computational power to undertake scientific calculations at unprecedented speeds, up to the current situation of extensive and sophisticated automation, black box measurement devices and multiuser information management systems, technology is causing glassware and paper notebooks to become increasingly rare in the laboratory landscape.” John Trigg 2012 (The smart laboratory)

AAMG 2015 Fighting ignorance Students might not be aware of the various components of an analyser and how the instrumental settings are controlled by a PC. They lack the appropriate knowledge to face and track failures in analytical instrumentation, or how to improve the analytical procedure outside the range of predefined experimental conditions. 3

AAMG 2015 Pilot plant (SCEAS) 4

AAMG

The problem To teach basic principles To teach understanding of what the automation software is doing To teach critical thinking – what you see on the screen does not equate to actuality The student task – devise a control algorithm to control the level of fluid in the third tank 6

AAMG 2015 Strategy Deliberately introduce hidden errors –Level sensor has an offset error –The sensor output is proportional to the height of the liquid but there is a step from one level to another along the curve –Fluid flow causes turbulence – noise on the output 7

AAMG Four broad learning categories: transmission, acquisition, emergence, accretion. Learning development cycle

AAMG 2015 Measurement, Information and Innovation October 2015 TOOLS 9

AAMG 2015 Curriculae Automation and data handling with respect to modern chemical laboratory instrumentation. Basic electronics Data acquisition Evaluation of laboratory needs Data processing methodologies 10

AAMG 2015 Instrumental analysis - Spectroscopy Spectroscopic methods of analysis will be studied with respect to fundamentals, operational aspects and instrument design. Topics will range from UV-visible to x-ray spectrometry. Methodologies will be evaluated with respect to their application in spectrometric systems. Laboratory automation and data analysis will be studied and applied in the laboratory. 11

AAMG 2015 Programming Tools Labview Matlab Visual Basic Visual C++ Data processing – statistics, chemometrics,...

AAMG 2015 Microcontrollers 13 Arduino Mbed BBC Internet of Things

AAMG 2015 Experiments electrochemical experiments –ion selective electrodes, voltammetry,amperometry, and coulometry; monitoring signals from chromatographic detectors; temperature and mobile phase control for chromatographs; capillary electrophoresis; microfluidic devices; simple applications of absorption spectroscopy and fluorimetry; thermometry; control of mechanical equipment such as solenoids, DC motors and stepper motors, and relays; control of heater blocks and cooling units; data logging for remote sensors; wireless and Internet communication 14

AAMG 2015 Photometric experiment 15 Published in: Gary A. Mabbott; J. Chem. Educ. 2014, 91, DOI: /ed Copyright © 2014 The American Chemical Society and Division of Chemical Education, Inc.

AAMG 2015 Thermal cycler for polymerase chain reaction (PCR) 16 Published in: Gary A. Mabbott; J. Chem. Educ. 2014, 91, DOI: /ed Copyright © 2014 The American Chemical Society and Division of Chemical Education, Inc.

AAMG 2015 More complex 17

AAMG 2015 Conclusion A problem based approach to automation in the chemical engineering and analytical science laboratory Outcomes – critical understanding of the principles, and understanding of what automation can and cannot do. Dynamic learning approach “We should value what we don’t know — or “high-quality ignorance” — just as much as what we know” (Stuart Firestein 2014) 18