skip to main content

Title: An Experimental Study on Human Milk Rheology: Behavior Changes from External Factors
The influence of external factors, including temperature, storage, aging, time, and shear rate, on the general rheological behavior of raw human milk is investigated. Rotational and oscillatory experiments were performed. Human milk showed non-Newtonian, shear-thinning, thixotropic behavior with both yield and flow stresses. Storage and aging increased milk density and decreased viscosity. In general, increases in temperature lowered density and viscosity with periods of inconsistent behavior noted between 6–16 ∘ C and over 40 ∘ C. Non-homogeneous breakdown between the yield and flow stresses was found which, when coupled with thixotropy, helps identify the source of nutrient losses during tube feeding.  more » « less
Award ID(s):
1454334 1707063
Author(s) / Creator(s):
Date Published:
Journal Name:
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    The rapid equilibrium fluctuations of water molecules are intimately connected to the rheological response; molecular motions resetting the local structure and stresses seen as flow and volume changes. In the case of water or hydrogen bonding liquids generally, the relationship is a non-trivial consideration due to strong directional interactions complicating theoretical models and necessitating clear observation of the timescale and nautre of the associated equilibrium motions. Recent work has illustrated a coincidence of timescales for short range sub-picosecond motions and the implied timescale for the shear viscosity response in liquid water. Here, neutron and light scattering methods are used to experimentally illustrate the timescale of bulk viscosity and provide a description of the associated molecular relaxation. Brillouin scattering has been used to establish the timescale of bulk viscosity; and borrowing the Maxwell approach, the ratio of the bulk viscosity, ζ , to the bulk modulus, K , yields a relaxation time, τ B , which emerges on the order of 1–2 ps in the 280 K to 303 K temperature range. Inelastic neutron scattering is subsequently used to describe the motions of water and heavy water at the molecular scale, providing both coherent and incoherent scattering data. A rotational (alternatively described as localized) motion of water protons on the 1–2 ps timescale is apparent in the incoherent scattering spectra of water, while the coherent spectra from D 2 O on the length scale of the first sharp diffraction peak, describing the microscopic density fluctuations of water, confirms the relaxation of water structure at a comparable timescale of 1–2 ps. The coincidence of these three timescales provides a mechanistic description of the bulk viscous response, with the local structure resetting due to rotational/localized motions on the order of 1–2 ps, approximately three times slower than the relaxations associated with shear viscosity. In this way we show that the shear viscous response is most closely associated with changes in water network connectivity, while the bulk viscous response is associated with local density fluctuations. 
    more » « less
  2. We explore the rheology during a startup flow of well-characterized polyelectrolyte microgel suspensions, which form soft glasses above the jamming concentration. We present and discuss results measured using different mechanical histories focusing on the variations of the static yield stress and yield strain. The behavior of the shear stress growth function is affected by long-lived residual stresses and strains that imprint a slowly decaying mechanical memory inside the materials. The startup flow response is not reversible upon flow reversal and the amplitude of the static yield stress increases with the time elapsed after rejuvenation. We propose an experimental protocol that minimizes the directional memory and we analyze the effect of aging. The static yield strain γ p and the reduced static yield stress σ p / σ y , where σ y is the dynamic yield stress measured from steady flow measurements, are in good agreement with our previous simulations [Khabaz et al., “Transient dynamics of soft particle glasses in startup shear flow. Part I: Microstructure and time scales,” J. Rheol. 65, 241 (2021)]. Our results demonstrate the need to consider memory and aging effects in transient measurements on soft particle glasses. 
    more » « less
  3. Debris flows are dense and fast-moving complex suspensions of soil and water that threaten lives and infrastructure. Assessing the hazard potential of debris flows requires predicting yield and flow behavior. Reported measurements of rheology for debris flow slurries are highly variable and sometimes contradictory due to heterogeneity in particle composition and volume fraction ( ϕ ) and also inconsistent measurement methods. Here we examine the composition and flow behavior of source materials that formed the postwildfire debris flows in Montecito, CA, in 2018, for a wide range of ϕ that encapsulates debris flow formation by overland flow. We find that shear viscosity and yield stress are controlled by the distance from jamming, Δ ϕ = ϕ m − ϕ , where the jamming fraction ϕ m is a material parameter that depends on grain size polydispersity and friction. By rescaling shear and viscous stresses to account for these effects, the data collapse onto a simple nondimensional flow curve indicative of a Bingham plastic (viscoplastic) fluid. Given the highly nonlinear dependence of rheology on Δ ϕ , our findings suggest that determining the jamming fraction for natural materials will significantly improve flow models for geophysical suspensions such as hyperconcentrated flows and debris flows. 
    more » « less
  4. Neuromorphic computing systems execute machine learning tasks designed with spiking neural networks. These systems are embracing non-volatile memory to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate non-volatile memories cause aging of CMOS-based transistors in each neuron and synapse circuit in the hardware, drifting the transistor’s parameters from their nominal values. If these circuits are used continuously for too long, the parameter drifts cannot be reversed, resulting in permanent degradation of circuit performance over time, eventually leading to hardware faults. Aggressive device scaling increases power density and temperature, which further accelerates the aging, challenging the reliable operation of neuromorphic systems. Existing reliability-oriented techniques periodically de-stress all neuron and synapse circuits in the hardware at fixed intervals, assuming worst-case operating conditions, without actually tracking their aging at run-time. To de-stress these circuits, normal operation must be interrupted, which introduces latency in spike generation and propagation, impacting the inter-spike interval and hence, performance (e.g., accuracy). We observe that in contrast to long-term aging, which permanently damages the hardware, short-term aging in scaled CMOS transistors is mostly due to bias temperature instability. The latter is heavily workload-dependent and, more importantly, partially reversible. We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems by designing an intelligent run-time manager (NCRTM), which dynamically de-stresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target. NCRTM de-stresses these circuits only when it is absolutely necessary to do so, otherwise reducing the performance impact by scheduling de-stress operations off the critical path. We evaluate NCRTM with state-of-the-art machine learning workloads on a neuromorphic hardware. Our results demonstrate that NCRTM significantly improves the reliability of neuromorphic hardware, with marginal impact on performance. 
    more » « less
  5. Dense suspensions of particles in viscous liquid often demonstrate the striking phenomenon of abrupt shear thickening, where their viscosity increases strongly with increase of the imposed stress or shear rate. In this work, discrete-particle simulations accounting for short-range hydrodynamic, repulsive, and contact forces are performed to simulate flow of shear thickening bidisperse suspensions, with the packing parameters of large-to-small particle radius ratio δ = 3 and large particle fraction ζ = 0.15, 0.50, and 0.85. The simulations are carried out for volume fractions 0.54 ≤ ϕ ≤ 0.60 and a wide range of shear stresses. The repulsive forces, of magnitude F R , model the effects of surface charge and electric double-layer overlap, and result in shear thinning at small stress, with shear thickening beginning at stresses σ ∼ F R a −2 . A crossover scaling analysis used to describe systems with more than one thermodynamic critical point has recently been shown to successfully describe the experimentally-observed shear thickening behavior in suspensions. The scaling theory is tested here on simulated shear thickening data of the bidisperse mixtures, and also on nearly monodisperse suspensions with δ = 1.4 and ζ = 0.50. Presenting the viscosity in terms of a universal crossover scaling function between the frictionless and frictional maximum packing fractions collapses the viscosity for most of the suspensions studied. Two scaling regimes having different exponents are observed. The scaling analysis shows that the second normal stress difference N 2 and the particle pressure Π also collapse on their respective curves, with the latter featuring a different exponent from the viscosity and normal stress difference. The influence of the fraction of frictional contacts, one of the parameters of the scaling analysis, and its dependence on the packing parameters are also presented. 
    more » « less