How RHEOS.jl Decodes the Behavior of Squishy Stuff
From the comforting spread of peanut butter on toast to the satisfying bounce of a memory foam pillow, our world is filled with materials that defy simple classification. They are neither solid nor liquid, but something intriguingly in between. Rheology, the science of deformation and flow, helps us understand these complex materials that don't exhibit simple linear elastic or Newtonian behaviors2 6 . This field plays a crucial role in characterizing soft viscoelastic materials found everywhere from the food and cosmetics industries to biology and bioengineering2 6 .
Yet, for scientists trying to analyze rheological data, a significant challenge has persisted: the lack of standardized, accessible software tools. Most research groups have had to invest significant effort developing custom software, making systematic analysis and reproducibility difficult1 .
Enter RHEOS.jl (RHEology, Open-Source), a revolutionary software package written in the Julia programming language that's making rheological data analysis simpler, faster, and more reproducible1 2 6 .
Rheology originates from the Greek word "rhei" meaning "to flow"5 . It describes the deformation and flow behavior of all kinds of materials, helping us understand why different substances behave the way they do under stress.
To visualize the fundamental concepts, imagine a material sandwiched between two parallel plates5 . When force is applied to the top plate, several key parameters come into play:
Study of material deformation and flow under stress
| Material | Temperature | Viscosity Range |
|---|---|---|
| Air | Room temperature | 0.01-0.02 mPa·s |
| Water | 20°C | 1.0 mPa·s |
| Olive oil | Room temperature | ~100 mPa·s |
| Motor oil (SAE 10W-30) | +23°C | 175 mPa·s |
| Polymer melts | +150°C to +300°C | 10 mPa·s to 10,000 Pa·s |
| Bitumen | 0°C | 1 MPa·s |
RHEOS represents a paradigm shift in how scientists approach rheological data analysis. Designed specifically for the broad family of linear viscoelastic models, its particular strength lies in handling models containing fractional derivatives2 6 . These advanced models have demonstrated remarkable utility for modeling biological materials but have remained relatively obscure, possibly due to their mathematical and computational complexity2 6 .
Fractional derivatives provide a mathematical framework to describe materials with memory effects and power-law responses, which are common in biological tissues and complex fluids. RHEOS.jl makes these advanced mathematical tools accessible to researchers without deep expertise in fractional calculus2 6 .
What sets RHEOS apart is its comprehensive approach to rheological analysis:
Stress/strain/time data and complex modulus (G'/G''/frequency) data can easily be fitted to viscoelastic models1
A fitted model can predict material behavior under different loading conditions, enabling the powerful fit/predict paradigm of model selection1
Users can generate artificial loading conditions to better understand model responses1
RHEOS includes both traditional and fractional viscoelastic models, with the flexibility for users to add custom models1
| Feature | Function | User Benefit |
|---|---|---|
| Model Fitting | Converts experimental data into model parameters | Quantifies material behavior |
| Prediction | Uses fitted models to predict responses to different loads | Saves experimental time and resources |
| Signal Generation | Creates common loading patterns (step, ramp, stairs) | Facilitates model exploration and education |
| Data Preprocessing | Includes resampling and smoothing functions | Prepares raw data for analysis |
| Multi-paradigm Support | Handles creep, relaxation, and oscillatory testing | Provides comprehensive analysis framework |
One compelling application of RHEOS.jl demonstrates why this tool represents such an advancement in rheological analysis. Researchers used the package to validate a fractional viscoelastic model for epithelial monolayers - the tissues that line our organs and cavities6 .
The experiment involved studying epithelial cell monolayers, with some treated with Y27632 (an inhibitor of contractility) and others left untreated as controls6
The monolayers were loaded with a constant strain rate of 75%/s, simulating mechanical stress these tissues might experience in biological contexts6
Stress relaxation behavior was measured following deformation, with time set to zero at the beginning of the loading ramp6
Using RHEOS, researchers fitted a three-element fractional model to the relaxation data from both treated and untreated monolayers6
The analysis revealed something remarkable: the fractional model implemented in RHEOS provided an excellent fit to the experimental data for both untreated and treated epithelial monolayers6 . The black curves representing experimental data closely aligned with the red and blue fit curves generated by RHEOS6 .
More significantly, the model successfully captured meaningful biological differences: the treated monolayers showed altered mechanical properties due to reduced cellular contractility6 . This demonstrated that the fractional calculus framework, made accessible through RHEOS, could quantitatively describe living tissues in a way that traditional models could not.
This experiment served as an experimentum crucis (crucial experiment) in the sense that it helped establish the superiority of fractional models for certain biological materials, making alternative theories less probable given the evidence7 . While not definitively ruling out all possible alternative explanations, the compelling fit and biological plausibility provided strong evidence for the fractional approach.
| Tool Category | Specific Examples | Function in Research |
|---|---|---|
| Software | RHEOS.jl1 , NLopt optimization2 | Data fitting, model selection, prediction |
| Experimental Data Types | Stress/strain/time data1 , Complex modulus (G'/G''/frequency) data1 | Material characterization under different conditions |
| Rheological Models | Traditional viscoelastic models1 , Fractional derivative models2 6 | Mathematical representation of material behavior |
| Testing Paradigms | Creep testing, Relaxation testing, Oscillatory testing | Different methods to probe material responses |
| Hardware | Rotational rheometers5 , Oscillatory rheometers5 , Cone-plate geometries5 | Experimental data generation |
RHEOS.jl can be installed directly from the Julia package manager:
import Pkg; Pkg.add("RHEOS")
Complete documentation with examples is available, and users can try RHEOS interactively in their browser using Binder without any local installation required1 .
The implications of accessible rheological analysis extend far beyond academic research. With RHEOS.jl, scientists across industries can:
Better understanding of material behavior leads to enhanced formulations in cosmetics, food products, and pharmaceuticals
The ability to accurately model tissues and cellular structures opens new avenues in understanding disease mechanisms and regenerative medicine
As open-source software, RHEOS removes financial barriers to sophisticated rheological analysis, enabling researchers worldwide to conduct state-of-the-art analyses1
The intuitive interface and comprehensive documentation make RHEOS an excellent tool for teaching rheological concepts to students1
RHEOS.jl represents more than just another software package - it embodies a movement toward open, reproducible science in the study of material behavior. By making sophisticated analysis tools accessible to all researchers, regardless of their programming expertise or financial resources, RHEOS is helping to advance our understanding of the squishy, messy, and fascinating materials that make up our world.
As the package continues to evolve through community contributions, its potential to unlock new discoveries in material science, biology, and engineering continues to grow. The next time you spread mayonnaise on a sandwich or marvel at the bounce of a rubber ball, remember that there's an entire scientific discipline - and now, better tools than ever - working to understand exactly what makes these materials behave the way they do.
For those interested in exploring RHEOS.jl, the software is freely available and can be installed following the instructions on the official GitHub repository. Complete documentation with numerous examples is provided, and users can even try RHEOS interactively in their browser using Binder without any local installation required1 .