Elastoviscoplasticity, hyperaging, and time-age-time-temperature superposition in aqueous dispersions of bentonite clay.

Clay slurries are both ubiquitous and essential in the oil exploration industry, and are most commonly employed as drilling fluids. Due to its natural abundance, bentonite clay is often the de facto choice for these materials. Understanding and predicting the mechanical response of these fluids is critical for safe and efficient drilling operations. However, rheological modeling of bentonite clay suspensions is complicated by the fact that thermally-driven microscopic arrangements of particle aggregates lead to a continual evolution of the viscoelastic properties and the yield stress of the suspension with time. Ergodic relations fundamental to linear viscoelastic theory, such as the Boltzmann superposition principle, do not hold in this scenario of 'rheological aging'. We present an approach for modeling the linear viscoelastic response of aging bentonite suspensions across a range of temperatures that is based on the transformation from laboratory time to an effective 'material time' domain in which time-translation invariance holds, and the typical relations of non-aging linear viscoelastic theory apply. In particular, we model the constitutive relationship between stress and strain-rate in the bentonite suspensions as fractional Maxwell gels with constant relaxation dynamics in the material time domain, in parallel with a non-aging Newtonian viscous contribution to the total stress. This approach is supported by experimental measurements of the stress relaxation and rapid time-resolved measurements of the linear viscoelastic properties performed using optimized exponential chirps. This data is then reduced to master curves in the material domain using time-age-time superposition to obtain best fits of the model parameters over a range of operating temperatures.

Scientific machine learning for modeling and simulating complex fluids.

The formulation of rheological constitutive equations-models that relate internal stresses and deformations in complex fluids-is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these "digital fluid twins" to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation-a task that is not achievable using any previously developed data-driven rheological equation of state.

Optical tweezer measurements of asymptotic nonlinearities in complex fluids.

This article presents micro-medium-amplitude oscillatory shear (µMAOS), a method to measure the frequency-dependent micromechanical properties of soft materials in the asymptotically nonlinear regime using optical tweezers. We have developed a theoretical framework to extract these nonlinear mechanical properties of the material from experimental measurements and also proposed a physical interpretation of the third-order nonlinearities measured in single-tone oscillatory tests. We validate the method using a well-characterized surfactant solution of wormlike micelles, and subsequently employ this technique to demonstrate that the cytoplasm of a living cell undergoes strain softening and shear thinning when locally subjected to weakly nonlinear oscillatory deformations.

Engineering Efficient Photon Upconversion in Semiconductor Heterostructures.

Photon upconversion is a photophysical process in which two low-energy photons are converted into one high-energy photon. Photon upconversion has broad appeal for a range of applications from biomedical imaging and targeted drug release to solar energy harvesting. Current upconversion nanosystems, including lanthanide-doped nanocrystals and triplet-triplet annihilation molecules, have achieved upconversion quantum yields on the order of 10-30%. However, the performance of these materials is hampered by inherently narrow absorption cross sections and fixed energy levels originating in atomic, ionic, or molecular states. Semiconductors, on the other hand, have inherently wide absorption cross sections. Moreover, recent advances enable the synthesis of colloidal semiconductor nanoparticles with complex heterostructures that can control band alignments and tune optical properties. We synthesize and characterize a three-component heterostructure that successfully upconverts photons under continuous-wave illumination and solar-relevant photon fluxes. The heterostructure is composed of two cadmium selenide quantum dots (QDs), an absorber and emitter, spatially separated by a cadmium sulfide nanorod (NR). We demonstrate that the principles of semiconductor heterostructure engineering can be applied to engineer improved upconversion efficiency. We first eliminate electron trap states near the surface of the absorbing QD and then tailor the band gap of the NR such that charge carriers are funneled to the emitting QD. When combined, these two changes result in a 100-fold improvement in photon upconversion performance.