IRIS Functions

A black and white line drawing of a polymeric shape with interconnected angular segments and dashed lines indicating edges or separations.

IRIS plays a central role in transforming rheological data into clear structure–property relationships that guide formulation, processing, and application decisions. The program is intuitive to use, although it may take a little time to become fully comfortable with all of its features. Many users have found IRIS to be a powerful tool—not only for analyzing and modeling rheological behavior, but also for presenting their findings through elegant visualizations (IRIS Quickplots). These graphics help communicate advanced insights clearly, even to colleagues and collaborators who may not have a background in rheology.

IRIS is best understood through examples of its capabilities. Below is an overview of how IRIS supports each stage of material development.

1. Formulation Screening

  • Compares rheological fingerprints of different formulations (e.g., polymer blends, asphalt modifiers, emulsions).

  • Detects structural differences (network strength, particle interactions, gelation) that are not evident in standard viscosity data.

  • Identifies yield stress or flow transition points important for processing and application behavior (e.g., coating, spraying, additive flow).

2. Process Optimization

  • Uses spectral and model-based simulations to predict material behavior under:

    • steady shear (extrusion, mixing, coating)

    • transient stress (startup, stress relaxation)

    • oscillatory deformation

  • Applies time–temperature superposition (TTS) to estimate process windows such as softening or hardening transitions during heating or cooling.

  • Predicts viscosity curves under untested conditions (e.g., higher shear rates, untested temperatures).

  • Defines processing windows for curing materials (e.g., synchronizing printing speed with cure rate in 3D printing).

  • Fits rheological models (Maxwell, Lodge, etc.) to describe free-surface flows such as film blowing, fiber spinning, or blow molding.

  • Converts experimental data into compliance, modulus, or viscosity functions directly linked to process performance.

3. Microstructure–Rheology Correlation

  • Extracts relaxation or retardation spectra — the “fingerprint” of viscoelastic structure.

  • Monitors spectral shifts reflecting molecular weight, branching, crosslinking, or filler–polymer interactions.

  • Provides insight into why one formulation outperforms another.

4. Stability and Aging Studies

  • Tracks rheological evolution under time, temperature, or shear history.

  • Quantifies aging, gelation, or phase-separation effects.

  • For asphalt and bitumen: evaluates storage stability, performance grading, and viscosity changes before and after aging.

5. Rapid Iteration and Comparison

  • Integrates multiple tests (frequency sweep, creep, TTS) into a single, consistent model.

  • Enables quantitative comparison of materials rather than relying solely on visual inspection.

  • Accelerates R&D cycles, especially when screening many closely related formulations.

6. Soft Matter Example: Bitumen and Asphalt Modification

Objective: Predict and optimize bitumen performance under varying conditions.

How IRIS contributes:

  • Builds master curves (TTS) from DSR frequency sweeps to predict performance across temperatures.

  • Extracts relaxation spectra to identify whether polymer modification (e.g., SBS, EVA) introduces long relaxation modes — improving elasticity and rutting resistance.

  • Generates Black (Booij–Palmen) and Cole–Cole diagrams to assess phase stability.

  • Quantifies aging effects through spectral changes before and after aging.

  • Determines the Kaelble inflection temperature and elastic contribution of the modifier.

  • Supports formulation and crosslinking adjustments to balance elasticity and viscosity.

7. Soft Matter Example: Polymer Blends and Composites

Objective: Tailor melt behavior for extrusion, molding, or coating.

How IRIS contributes:

  • Fits generalized Maxwell models to oscillation data, revealing the distribution of relaxation modes.

  • Detects spectral shifts that indicate branching, blending, or filler effects.

  • Predicts viscosity–shear rate curves from spectral data, even when direct steady-shear tests are unstable.

  • Quantifies how additives or fillers modify viscoelastic response, supporting processability design.

8. Soft Matter Example: Food and Biopolymer Gels

Objective: Design desired texture, stability, and mouthfeel.

How IRIS contributes:

  • Uses creep–recovery and oscillation tests to quantify structure strength (storage modulus G′) and recovery.

  • Identifies gelation point where phase angle becomes frequency-independent.

  • Tracks structural evolution and aging over time.

  • Visualizes spectrum broadening as the network forms — a sensitive indicator of gel structure development.

  • Links rheological signatures to sensory texture and shelf stability, enabling optimized formulation and process conditions.

9. Soft Matter Example: Coatings and Inks

Objective: Ensure smooth application and sag-free drying.

How IRIS contributes:

  • Evaluates yield stress and thixotropy through stress growth and recovery analysis.

  • Simulates flow curves at various shear rates (printing, rolling, spraying).

  • Converts creep data into low-shear viscosity predictions where direct measurement is difficult.

  • Balances formulation between flowability and storage stability.

10. Soft Matter Example: Polymer Adhesive

Objective: Optimize the viscoelastic balance controlling tack, wetting, cohesion, debonding, and energy dissipation.

A — Pressure-Sensitive Adhesives (PSA)

IRIS enables rapid formulation screening and performance prediction.

  • Formulation Screening: Compares G′ and G″ across frequencies/temperatures to identify systems that are too elastic (poor wetting) or too viscous (low cohesion).

  • Viscoelastic Targeting: Highlights the optimal frequency window where G′ ≈ G″ using master curves or Booij–Palmen plots.

  • Time–Temperature Superposition (TTS): Builds master curves and shift factors to predict tack and peel behavior over broad temperature ranges.

  • Relaxation Spectrum Analysis: Reveals distribution of fast (tack) and slow (cohesive) modes to adjust molecular weight, crosslink density, or tackifier ratio.

  • Process Simulation: Models shear and elongation at high strain rates to optimize coating, rolling, and debonding behavior.

B — Curing and Crosslinking Adhesives

IRIS characterizes chemical and physical network formation throughout curing.

  • Cure Monitoring: Tracks modulus growth and tan δ decay to identify gel point, gel strength, vitrification onset, and cure completion.

  • Spectral Evolution: Quantifies the transition from viscous to elastic dominance as crosslinks form and networks mature.

  • Aging and Stability: Compares spectra before/after thermal or UV exposure to assess hardening, embrittlement, or plasticizer loss.

  • Predictive Modeling: Simulates stress relaxation, creep, and long-term performance under variable temperatures.