Roger T Bonnecaze - 2026 Bingham Medalist

Chemical Engineer

Awarded Bingham Medal 2026

Fellow, Inducted 2018

University of Texas at Austin

Biography

Roger T. Bonnecaze is Dean of the Cockrell School of Engineering and Cockrell Regents Family Chair in Engineering, and a Professor in the McKetta Department of Chemical Engineering at The University of Texas at Austin. He earned his B.S. (Honors) in Chemical Engineering from Cornell University (1985), and his M.S. (1987) and Ph.D. (1991) in Chemical Engineering from the California Institute of Technology with John F. Brady, followed by a British Petroleum Postdoctoral Fellowship at the University of Cambridge (1991–1992), where he worked with Herbert Huppert on particulate suspensions in gravity currents and sediments. In 2017, Roger helped launch SandBox Semiconductor, a start-up based on the PhD work of Meghali Chopra who is now the CEO.

Major honors include the NSF Young Investigator Award (1993), David and Lucile Packard Foundation Fellowship (1994), AIChE Thomas Baron Award (2011), two Journal of Rheology Publication Awards (2005, 2014), and a Rheologica Acta Publication Award (2017). He is a Fellow of the Society of Rheology, the American Physical Society, the American Institute of Chemical Engineers, and the American Association for the Advancement of Science.

His research contributions span suspensions, emulsions, and complex fluids; rheological analysis and theory; and computational fluid mechanics, with a sustained focus on connecting macroscopic rheological response to the microphysics of dispersions.

Major Accomplishments

Soft particle glasses (SPGs) are very concentrated suspensions of deformable particles such as emulsion droplets, vesicles, microgels, polymer-grafted nanoparticles, and micelles. They are widely used as rheology modifiers, matrices for bioprinting, or binders in ceramics extrusion. Over the past two decades Roger Bonnecaze has helped define the rheology of dense soft particulate matter in the regimes where constitutive ideas are most severely tested: the onset of flow from arrested states, strongly nonlinear viscometric response, and the influence of material history. Through theory, simulations and experiment, his pioneering work established that despite a considerable phenotypical diversity, SPGs can be described by universal rheology. In a second major contribution to the field, Bonnecaze expanded the understanding of wall slip in yield-stress dispersions as boundary rheology with mechanistic, parameter-dependent laws. His novel approach to separating bulk deformation from interfacial motion identified the underlying mechanisms that set slip–stick behavior. This overall arc emerged from major contributions in three key areas:

Soft particle glasses (SPGs)

SPGs provide a controlled setting in which yielding and nonlinear viscometric functions can be traced to contact elasticity, near-contact structure, and shear-induced microstructural anisotropy via a contact network continually rebuilt under shear. The central rheological difficulty is that macroscopic signatures are often similar across materials even when the underlying contact mechanics, softness, and dissipation pathways differ. Bonnecaze addressed this issue via a theoretical framework modeling jammed assembly as a contact-elastic network coupled to viscous dissipation in the suspending fluid, making the physics explicit at the constitutive level (J. Rheol., 2006; Nature Materials, 2011; Soft Matter, 2018; J. Rheol., 2020).

His early work established how contact elasticity and near-contact structure enter the rheology of jammed soft particles (J. Rheol., 2006), later expanded into a microstructure-based constitutive description in which the pair-distribution function under shear serves as the key structural input. Total and normal stress differences are not independent empirical signatures but instead emerge from the same shear-distorted microstructure. This opened the door to unified scaling theory that depends explicitly on contact elasticity and dissipation (Nature Materials, 2011). The resulting nondimensionalization, organized around the reduced shear rate and associated stress scales, collapses flow curves and viscometric functions across multiple soft glassy material classes, enabling meaningful comparison of data across systems that otherwise appear dissimilar in their chemistry and particle architecture (Nature Materials, 2011; Soft Matter, 2018, J. Rheol., 2020a, J. Rheol., 2020b, Phys. Rev. Fluids, 2017–2018). This work is central to how SPGs are modeled and designed: microstructure-based prediction of shear and normal stresses; dimensionless cross-material characterization; and a mechanistic link between macroscopic viscometric response and particle-scale relaxation and force statistics.

Transients, trapped stress, memory, and aging: toward state descriptions for soft glasses.

Soft glasses in time-dependent flows are the most challenging to model because material flow and relaxation depend on preparation history, ongoing deformation, and evaluation time. A challenge for the field has been to identify measurable microscopic quantities that encode this dependence in history-responsive constitutive descriptions in a controlled, rather than post-hoc, way. Bonnecaze tackled this challenge by leveraging residual internal stresses and microstructural anisotropy as experimentally and computationally accessible memory kernels, developing a framework in which the evolving distribution of local stresses becomes a state variable. This work resulted in a scaling description of trapped internal stress in jammed SPGs after flow cessation (PRL, 2013). He established the functional dependence of trapped stress on solvent viscosity, particle elasticity, and packing through combined experiment and 3D simulation, providing predictive scaling laws (PRL, 2013, 2015).
Bonnecaze carried these ideas further into transient yielding and aging, showing that long-debated stress overshoots and relaxation in startup shear and flow reversal are shaped by the arrested contact network, and by the persistence of residual stress and strain. The work identified “directional memory”: the material remembers prior shear direction – but reversal does not simply retrace the prior evolution (J. Rheol., 2021–2022). Memory and aging are placed on an operational footing: they can be separated, quantified, and compared across systems by prescribed protocols rather than being treated as irreducible complications. Overall, this work transformed history dependence from a qualitative descriptor into a measurable, modellable property of SPGs.

Wall slip: boundary rheology for yield-stress dispersions.

In this body of work, Bonnecaze constructed mechanistic slip laws based on elastohydrodynamic lubrication coupled to particle elasticity and particle–wall interactions. Using rheometry along with local velocimetry, he showed that smooth-wall flows near yielding separate into regimes in which bulk shear and slip contribute in distinct proportions, and that at sufficiently low stresses the apparent motion can be dominated by slip even when bulk deformation is negligible. This established that “flow curves” can be used to map the boundary condition (J. Rheol., 2004). The associated mechanism was developed as an elastohydrodynamic lubrication problem, where slip is a consequence of the same contact-elastic physics that govern bulk response, expressed in a boundary geometry. These results have been extended to different surface chemistries (J. Rheol., 2008, Soft Matter, 2012) and to permeable particles (Soft Matter, 2022).

Importance of contributions in advancing the science of rheology

Bonnecaze’s contributions have advanced rheology in two major ways: (i) by providing constitutive models that explicitly connect viscometric signatures (shear stress and normal stress differences) to identifiable microstructural properties in soft particle glasses, and (ii) by establishing boundary rheology relationships that render wall slip measurable, predictable, and comparable across materials and surface chemistries. In both of these areas, the unifying impact is interpretability and predictability: his models and scalings turn historically system-specific observations – yielding, nonlinear viscometric response, and history dependence – into quantities that can be compared across materials, geometries, and protocols with a clear physical basis.