Publications
Tunable Elasto-Viscoplastic Properties of Polymer Blends for 3D Printing Applications
Struggling with inconsistent 3D prints?
Does your material behave unpredictably during extrusion?
Spending too much time and material fine-tuning process parameters?
You’re not alone, and the solution may lie in rethinking your material’s rheology.
In extrusion of soft matter, elasto-viscoplastic (EVP) behavior—not just printer settings—governs filament stability, die swell, and deposition accuracy. By tuning yield stress and viscoelasticity and using the Ohnesorge and modified Bingham numbers, we define EVP “printing windows” that boost fidelity and cut trial-and-error.
Halbach Array Induced Magnetic Field Alignment in Boron Nitride Nanocomposites
Using a Halbach array to generate a highly uniform rotating magnetic field, we align hBN nanosheets in PDMS to form efficient heat-conduction pathways. With only 10 wt% filler, vertically aligned hBN delivers 3.58 W·m⁻¹·K⁻¹, about a 1950% jump over neat PDMS. Orientation also tunes dielectric behavior: vertical alignment maintains strong insulation while boosting permittivity (ε ≈ 14) and keeping losses ultra-low (≈0.0049 at 100 Hz). The result is a thermally conductive, electrically robust PDMS composite for thermal management and energy-storage capacitors.
The complex shear time response of saliva in healthy individuals
Saliva is a key lubricant and protector in the mouth. In 11 healthy, nonsmoking adults, we measured secretion rate, total protein, glycoproteins, and calcium, and related these to shear- and time-dependent rheology by rotational rheometry. Unstimulated saliva, rich in mucins MUC5B/MUC7, showed higher viscosity and stronger viscoelasticity than stimulated saliva. Overall, saliva behaved as a viscoelastic, shear-thinning, thixotropic fluid: viscosity dropped with shear, elastic and viscous responses coexisted, and structure partially rebuilt at rest. Composition mattered, higher calcium and glycoproteins correlated with greater elasticity, particularly higher storage modulus (G′). These links between chemistry and mechanics help explain saliva’s effectiveness in lubrication, protection, and adapting to changing oral conditions.
One test to predict them all: Rheological characterization of complex fluids via artificial neural network
Characterizing thixotropic, viscoelastic, and viscoplastic fluids is hard because their stress is transient and history-dependent. We present an ANN trained on step-shear transient tests that learns the shear history, capturing time–shear couplings directly from data. Once trained, it rapidly emulates diverse rheological protocols (e.g., start-up, creep/recovery, multi-step histories) with high accuracy. Compared with structural-kinetic constitutive models, it better tracks nonlinear evolution and hysteresis as material complexity increases. The model is flexible and robust across materials and shear histories, enabling fast “in silico” testing that can complement, and in many cases replace, traditional characterization to accelerate material development.
Scaling laws for near-wall flows of thixo-elasto-viscoplastic fluids in a millifluidic channel
TEVP fluids, elastic, viscoplastic, and thixotropic, defy a-priori flow prediction. We studied 18 samples (yogurts; Laponite and Carbopol at three concentrations, unstirred/stirred), combining standard rheology with an ex situ protocol that reproduces flow history, and measured micrometer-resolved duct flow via D-OCT. High-yield-stress samples show plug flow: at low rates the plug nearly reaches the wall and shrinks with increasing rate and thixotropy. The ex situ method enables near-wall stress/strain estimates and reveals two scaling laws linking four dimensionless groups for wall shear stress and slip velocity—an ansatz for a-priori near-wall prediction from rheometer flow curves.
Implementation of viscosity and density models for improved numerical analysis of melt flow dynamics in the nozzle during extrusion-based additive manufacturing
We model FFF nozzle flow using Cross–WLF viscosity and PVT density to improve melt-flow prediction, simulating combinations of extrusion speed (Ve), temperature (Te), and material (ABS, PLA). The models map pressure/temperature/velocity/viscosity, show PVT’s impact vs constant density, and locate “stable zones” where velocity is fully developed, zones that track with print quality. Optimal parameters for extended stability are: ABS, Ve = 60 mm/s, Te = 220 °C; PLA, Ve = 30 mm/s, Te = 195 °C.
All Posts
