Monodisperse thermosensitive dumbbell-shaped core-shell microgels are fabricated, which consist of a polystyrene core with a cross-linked poly (N-isopropylacrylamide) shell. The morphology of the microgels was investigated through cryogenic transmission electron microscopy and depolarized dynamic light scattering. The effective volume fraction and aspect ratio of the system could be adjusted through the swelling of the thermosensitive shell. We observe a phase transition of the microgels to an ordered, crystal-like state, which is apparent through Bragg-reflections in the visible range. These observations are further supported by rheological measurements where the shear-melting of the crystal phase is clearly detected.

We study the structure and viscoelastic behavior of 3D plastic crystals of colloidal dumbbells in an oscillatory shear field based on a combination of small-angle neutron scattering experiments under shear (rheo-SANS) and Brownian dynamics computer simulations. Sterically stabilized dumbbell-shaped microgels are used as hard dumbbell model systems which consist of dumbbell-shaped polystyrene (PS) cores and thermosensitive poly(N-isopropylacrylamide) (PNIPAM) shells. Under increasing shear strain, a discontinuous transition is found from a twinned-fcc-like crystal to a partially oriented sliding-layer phase with a shear-molten state in between. In the novel partially oriented sliding-layer phase, the hard dumbbells exhibit a small but finite orientational order in the shear direction. We find that this weak correlation is sufficient to perturb the nature of the nonequilibrium phase transition as known for hard sphere systems. The discontinuous transition for hard dumbbells is observed to be accompanied by a novel yielding process with two yielding events in its viscoelastic shear response, while only a single yielding event is observed for sheared hard spheres. Our findings will be useful in interpreting the shear response of anisotropic colloidal systems and in generating novel colloidal crystals from anisotropic systems with applications in colloidal photonics.

Interactions among annealed spherical polyelectrolyte brushes (SPB) in concentrated aqueous dispersion under the effect of concentration, pH, and salt concentration are investigated by means of rheology, and small angle X-ray scattering (SAXS). SPB consist of a solid polystyrene (PS) core and linear poly(acrylic acid) (PAA) chains densely grafted onto the core by one end. Rheological investigation demonstrates that the viscosity, the storage modulus G 0 and the loss modulus G 00 of SPB dispersion increase significantly upon increasing the SPB concentration and pH value which reflects the enhanced interactions among SPB. At high pH, a further increase in pH from 8 to 13 has almost no impact on the rheological properties and SAXS curves, while a "Uniform Shell Model" can fit the SAXS data very well probably due to the uniform filling of polyelectrolyte chains among SPB. When increasing the salt concentration from 10 25 to 10 23 M, the so-called "polyelectrolyte peak" appears at middle to high q range in SAXS curves which means the overlapped polyelectrolyte chains are associated under the bridging effect of counterions, which disappears at higher salt concentration due to the screening effect of further added salts.

In order to reduce the influence of differences in human characteristics on the blood pressure prediction model and further improve the accuracy of blood pressure prediction, this paper establishes support vector machine regression model and random forest regression model for accurate blood pressure measurement. First, the photoelectric method is used to obtain the photoelectric plethysmography signal (PPG) and ECG signals from people of different ages, and the blood pressure value is roughly estimated based on the high-quality physiological signals and the vascular elastic cavity model; then the human body characteristics are used as the input parameters of the blood pressure prediction model, and the model parameters are used to find the best parameter combination to improve the prediction performance of the model; finally, through a lot of training and learning, the best blood pressure prediction model is selected to achieve accurate measurement of blood pressure values. It has been verified by experiments that the average absolute error of diastolic and systolic blood pressure based on the random forest optimization model meets the standard of less than 5mmHg formulated by AAMI (American Medical Instrument Promotion Association), which is better consistent with the method of mercury sphygmomanometer, and has more excellent performance than support vector machine regression model under the same conditions.INDEX TERMS Blood pressure detection, random forest, support vector regression, human body characteristics.