Simulated SEC/GPC Chromatography Data

Simulated Size Exclusion Chromatography (SEC) / Gel Permeation Chromatography (GPC) data for demonstration of molecular weight analysis. The dataset includes both narrow polystyrene calibration standards and polymer samples with broad molecular weight distributions.

Format

A tibble with 7,510 observations and 6 variables:

sample_id

Sample identifier (standard or polymer name)

sample_type

Either "standard" or "sample"

elution_time

Elution/retention time in minutes

ri_signal

Refractive index detector signal (arbitrary units)

known_mw

Known weight-average molecular weight (g/mol)

known_dispersity

Known dispersity (Mw/Mn); ~1.05 for standards

Source

Simulated data generated for the measure package. See data-raw/generate_datasets.R for the generation script.

Details

SEC/GPC separates molecules by hydrodynamic size, with larger molecules eluting before smaller ones. This allows determination of molecular weight distributions and averages (Mn, Mw, Mz, dispersity).

The dataset is useful for demonstrating:

• Baseline correction for chromatography

• Calibration curve construction using standards

• Molecular weight calculations (step_measure_mw_averages)

• Molecular weight distribution analysis

The dataset contains:

Calibration Standards (narrow dispersity polystyrene):

• PS_1k: 1,000 g/mol

• PS_5k: 5,000 g/mol

• PS_20k: 20,000 g/mol

• PS_100k: 100,000 g/mol

• PS_500k: 500,000 g/mol

Polymer Samples (broad distribution):

• Polymer_A through Polymer_E with varying Mw and dispersity

The calibration relationship follows: log10(MW) = 9.5 - 0.35 * time

See also

sec_calibration for the calibration standards summary hplc_chromatograms for HPLC chromatography data step_measure_mw_averages for molecular weight calculations

Examples

data(sec_chromatograms)

# View structure
str(sec_chromatograms)
#> tibble [7,510 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ sample_id       : chr [1:7510] "PS_1k" "PS_1k" "PS_1k" "PS_1k" ...
#>  $ sample_type     : chr [1:7510] "standard" "standard" "standard" "standard" ...
#>  $ elution_time    : num [1:7510] 5 5.02 5.04 5.06 5.08 5.1 5.12 5.14 5.16 5.18 ...
#>  $ ri_signal       : num [1:7510] 1.63 2.29 2.95 1.89 1.97 ...
#>  $ known_mw        : num [1:7510] 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ...
#>  $ known_dispersity: num [1:7510] 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 ...

# Separate standards and samples
library(dplyr)
standards <- sec_chromatograms |> filter(sample_type == "standard")
samples <- sec_chromatograms |> filter(sample_type == "sample")

# Plot standards (if ggplot2 available)
if (requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggplot2)
  ggplot(standards, aes(x = elution_time, y = ri_signal, color = sample_id)) +
    geom_line() +
    labs(x = "Elution Time (min)", y = "RI Signal",
         title = "SEC Calibration Standards",
         color = "Standard")
}