The particle size dependence of the reversible shear thickening transition in dense colloidal suspensions is explored. Five suspensions of monodisperse silica are synthesized via the Stöber synthesis. The physicochemical properties of the dispersions are quantified using transmission electron microscopy, dynamic light scattering, small angle light scattering, electrophoresis, and viscometry. Rheology measurements indicate a critical stress marking the onset of reversible shear thickening that depends on the dispersion's particle size, concentration, polydispersity, and interparticle interactions. A simplified two particle force balance between the interparticle repulsive forces and the hydrodynamic compressive forces is used to derive a scaling relationship between this critical shear stress and the suspension properties. The scaling is tested against the fully characterized silica dispersions, which span nearly a decade in particle size. Furthermore, bimodal mixtures of the dispersions are employed to evaluate the accuracy of the scaling to predict the critical shear stress for dispersions with varying degrees of polydispersity. The success of the scaling supports the hydrocluster mechanism for shear thickening and suggests methods for controlling shear thickening by tailoring particle properties.

The shear induced microstructure for electrostatic and Brownian suspensions are compared using in situ small angle neutron scattering ͑SANS͒. The dispersions consist of 75 nm Stöber silica coated with 3-͑trimethoxysilyl͒ propyl methacrylate ͑TPM͒ and have a zeta potential of Ϫ42.6 Ϯ4.7 mV. Neutralizing the surface charge with 0.066 M nitric acid yields stable hard-sphere dispersions. SANS is conducted over a range of shear rates on the charge-stabilized and Brownian suspensions to test the order-disorder transition and hydrocluster mechanisms for shear thickening, and demonstrate the influence of stabilizing forces on the shear induced microstructure evolution. Through treatment of the colloidal micromechanics, shear induced changes in the microstructure are correlated to the hydrodynamic component of the shear stress and the thermodynamic component of the normal stress, i.e., the method of ''Rheo-SANS'' is developed. The results demonstrate that hydrocluster formation accompanies the shear thickening transition.

A comparison between the effects of two colloidal stabilizing methods ͑electrostatic versus Brownian͒ on the reversible shear thickening transition in concentrated colloidal suspensions is explored. Five suspensions of monodisperse silica are synthesized via the Stöber synthesis and dispersed in an index matched organic solvent to minimize van der Waals interactions. The residual surface charge is neutralized with nitric acid (c HNO 3 Ϸ 0.1 M͒ resulting in a near hard-sphere interaction that is confirmed by small angle neutron scattering measurements across a range of volume fractions. Rheological measurements demonstrate the effects of neutralization on the low shear and high shear rheology, which show that the onset of shear thickening moves to lower applied shear stresses and scales inversely with particle size cubed, in agreement with theory. Quantitative comparisons of both the low shear viscosity and the critical stress for shear thickening to predictions for hard spheres and literature data demonstrate the extreme sensitivity of high shear rheology to the surface properties in concentrated suspensions.

Detector response is not always equivalent between detectors or instrument types. Factors that impact detector response include molecular structure and detection wavelength. In liquid chromatography (LC), ultraviolet (UV) is often the primary detector; however, without determination of UV response factors for each analyte, chromatographic results are reported on an area percent rather than a weight percent. In extreme cases, response factors can differ by several orders of magnitude for structurally dissimilar compounds, making the uncalibrated data useless for quantitative applications. While impurity reference standards are normally used to calculate UV relative response factors (RRFs), reference standards of reaction mixture components are typically not available during route scouting or in the early stages of process development. Here, we describe an approach to establish RRFs from a single experiment using both online nuclear magnetic resonance (NMR) and LC. NMR is used as a mass detector from which a UV response factor can be determined to correct the high performance liquid chromatography (HPLC) data. Online reaction monitoring using simultaneous NMR and HPLC provides a platform to expedite the development and understanding of pharmaceutical reaction processes. Ultimately, the knowledge provided by a structurally information rich technique such as NMR can be correlated with more prevalent and mobile instrumentation [e.g., LC, mid-infrared spectrometers (MIR)] for additional routine process understanding and optimization.

Process analytical technology (PAT) plays an important role in the pharmaceutical industry. PAT is used extensively in process development, process understanding, and process control. Often, quantitative measurements are desired/required and a calibrated model will have to be developed and implemented. The development, implementation, and maintenance of these quantitative models are both resource and time intensive. This paper describes a calibration-free/minimum approach, iterative optimization technology (IOT), which is used to predict (without calibration standards) the composition of a mixture while maintaining a similar predictability to calibration standard models. It typically involves using only pure standard spectra (collected prior to the analysis) and sample spectra collected during the analysis. This technology is applicable for predicting compositions during development of pharmaceutical products (where the synthetic route, formulation, or process is not set) and is not intended for use in good manufacturing practice (GMP) manufacture where quantitative measurements are made using validated models. For ideal mixture cases, the mixture composition is iteratively computed at every sample time point to minimize an excess absorption subject to constraints (e.g., mixture constraints, upper/lower limits). Linear IOT is used to describe these ideal mixture cases. For nonideal mixture cases, the excess absorption, including the nonlinear characteristic, is first represented by a Box-Cox transformation. A limited number of training/calibration samples is required for these nonlinear examples. The mixture composition is then iteratively obtained in a similar optimization framework as linear IOT. Nonlinear IOT is used to describe these nonideal mixture cases. Linear and nonlinear IOT have provided comparable prediction accuracy on binary and ternary mixtures as compared to a calibrated partial least squares (PLS) model. IOT enhanced the understanding of dosage form blending processes by determining the composition/ratio of all (spectrally discriminated) components in the blend in real time. As composition is predicted each revolution, determination of the blending end point (does each component trend meet the known target mixture ratio) can be easily determined. Linear and nonlinear IOT can also be used to aid process understanding via detecting/representing molecular interaction effects utilizing the excess absorption calculation. The effectiveness of the linear and nonlinear IOT is demonstrated through four online and offline pharmaceutical process examples (bin-blending process, rotary tablet press feed frame process, and two different solvent mixtures).