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Analytical Chemistry 3.0 is a project to explore a resource for studying analytical chemistry that focuses on visual elements—mostly figures, but some tables; mostly interactive, but some static—as the primary vehicle for introducing students to analytical chemistry. The inspiration for this project is the unusual resource Pitfalls and Errors of HPLC in Pictures by Veronika R. Meyer (Wiley-VCH, 2006), which uses 89 lessons to introduce 89 ideas, each limited to a single page of text and a single page that contains one or two figures (the third edition extends this to 100 lessons).
Each of the modules gathered here is an application developed in the R programming environment using the Shiny package, which makes it possible to develop and run interactive browser-based applications. All Shiny apps are released under a GNU General Public License that provides users with four essential freedoms: (0) to run the program, (1) to study and change the program in source code form, (2) to redistribute exact copies, and (3) to distribute modified versions.
The slides from a departmental seminar on this project are available here.
Running a Shiny App Through an External Server
The easiest way to explore a Shiny app is to use the link provided below (of the form link to app on shinyapps.io) which runs the app remotely using the shinyapps.io server. Using the shinyapps.io server does not require a local installation of R or the Shiny package. Access to shinyapps.io server, however, is through an account that limits the number of hosted apps and that limits the monthly usage of the apps; if you cannot access one of these apps at shinyapps.io, try running the app locally on a computer running R.
Running a Shiny App Locally
To run a Shiny app locally you must have R, the Shiny package, and the shinythemes package installed on your computer (and, perhaps, one or more additional packages). To install R, first go the website www.r-project.org and click on the link to CRAN on the left side of the page under the heading "Downloads." Scroll through the list of CRAN mirror sites and click on a link to a site located in your region. Select the link in the "Download and Install R" box at the top of the page that is appropriate for your operating system and follow the directions. To install the Shiny package, launch R and type install.packages("shiny") at the command prompt; this will download and install the Shiny package and other packages required by Shiny. Installing a package does not make the package immediately available to you; to make the package available any R session, type library("shiny") at the command prompt. Finally, to run the app, use the "run locally using code from github" link and follow the instructions in the app's readme file; in general, the command is shiny::runGitHub("name of app","dtharvey"). Some apps require that you install packages of functions; where necessary, a list of packages is provided, which you may download using the install.packages command given above, replacing "shiny" with the package's name.
Note: these are works-in-progress—not finished products—and, as such, they explore a variety of formats. As the project develops, the modules will settle into a common format governed by a style guide.
Data Analysis
Distributions of Data. Students are introduced to four common distributions of experimental data: the normal distribution, the Poisson distribution, the uniform distribution, and the binomial distribution. Each distribution is modeled using a data set that is either real or simulated. The mathematical model for each distribution is presented and students can adjust a model's parameters to fit the mathematical model to the data. The data sets can be downloaded as .csv spreadsheet files. Most recent version posted on 08/01/2023.
Central Limit Theorem. Introduces students to the central limit theorem. The user can select one of four traditional distributions—normal, uniform, binomial, and Poisson—and a bimodal distribution and observe how an increase in the size of individual samples yields a distribution that closely approximates a normal distribution. Most recent version posted on 08/01/2023.
Central Composite Experimental Design. Introduces students to response surfaces and the use of the central composite experimental design to model the response surface. The module also allows students to adjust the coefficients of a full second-order polynomial model and observe the effect on the response surface's shape and position. Most recent version posted on 09/28/2023.
Sequential and Steepest Ascent Optimization. This module introduces students to a sequential one-factor optimization and a steepest ascent one-factor optimization of a response surface defined by two factors. Most recent version posted on 10/27/2023.
Signal Processing
Fourier Transform. Introduces students to the Fourier transform with particular attention to the relationship between the time domain and the frequency domain for a simple system that consists of a single Gaussian, Lorentzian, or Voigt peak, or two Gaussian peaks. Most recent version posted on 08/04/2023.
Signals and Noise. Introduces students to the difference between signals and noise, and the signal-to-noise ratio as a measure of the quality of a spectrum. The use of signal averaging, moving average filters, and Savitzky-Golay filters as computational methods for improving the signal-to-noise ratio are introduced. The application uses a database that contains 64 visible spectra of a blue dye; students can download the data as a .csv file. Most recent version posted on 08/08/2023.
Visualizing the Superposition of Waves. Introduces students to the superposition of waves. The first activity explores the superposition of two sine waves, and explores how the properties of the individual sine waves—amplitude, periodicity, and phase—affects their sum. The second activity examines how summing together multiple sine waves leads to a square wave, a triangular wave, and a sawtooth wave. Most recent version posted on 08/05/2023.
Electrochemistry
Chronoamperometry. This module introduces students to chronoamperometry using four primary types of visual features: ladder diagrams, time-dependent applied potential profiles, diffusion profiles, and current profiles. The Cottrell equation is introduced and verified using simulated data. A brief introduction is included to double-step chronoamperometry and chronocoulometry. Most recent version posted on 08/15/2023.
Spectroscopy
Power, Transmittance, and Absorbance. In this module, students explore the relationship between power, transmittance, and absorbance, ultimately deriving for themselves Beer's law (A = abC). Most recent version posted on 08/09/2023.
Scanning Monochromator. In this module students explore how the choice of slit width, response time, and scan rate affect the UV absorbance spectrum of benzene dissolved in cyclohexane. The application uses a database that contains 97 different spectra. Most recent version posted on 08/11/2023.
Separations
Liquid-Liquid Extractions. Introduces students to a simple liquid-liquid extraction of a solute that has no additional reactions in either the aqueous phase or the organic phase, and the liquid-liquid extractions of weak acids and weak bases that have pH-dependent distribution functions. Most recent version posted on 08/07/2023.
Countercurrent Separation. Introduces students to counter-current extractions as a method for separating two solutes from each other. The distribution ratios for each solute are set by the user and the progress of the extraction viewed manually by examining the separation after a user-selected number of steps. The simulation is limited to 100 tubes and 100 steps. Most recent version posted on 08/07/2023.
Sampling
Sampling Strategies. This module introduces students to the importance of sampling. Topics include where to collect samples, how much sample to collect, how many samples to collect, and how many times to analyze each sample. Most recent version posted on 09/07/2023.
Titrations
Designing an Acid-Base Titration. This module introduces students to acid-base titrations with a focus on the titration of strong acids and bases, the titration of monoprotic weak acids and bases, and the titration of diprotic weak acids and bases. Students can explore the affect of the titrand's acid/base strength, concentration, and volume, and the titrants concentration on the shape of a titration curve. THe use of a visual indicator for locating endpoints also is considered. Most recent version posted on 08/21/2023.
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