The electrical resistance of nanoporous gold prepared by dealloying is tuned by charging the surfaces of the porous structure in an electrolyte. Reversible variations in the resistance up to approximately 4% and 43% occur due to charging in the regimes of double layer charging and specific adsorption, respectively. Charging-induced variations in the electron density or of the volume cannot account for the resistance variation, indicating that this variation is primarily caused by charge-induced modifications of the charge carrier scattering at the solid-electrolyte interface. The relative resistance variation in nanoporous Au with surface charging is found to be much higher than reported for porous nanocrystalline Pt. This is due to the lesser resistance contribution from internal grain boundaries. The resistance variation in nanoporous Au is also higher than that found in thin films owing to the stronger surface scattering in the ligament structure compared to plan surfaces. We argue that the strong resistance variation in up to 43% in the regime of specific adsorption is due to the reversible formation of a chemisorbed surface layer acting as scattering centers for the charge carriers.

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Continuous pharmaceutical manufacturing processes are of increased industrial interest and require uni- and multivariate Process Analytical Technology (PAT) data from different unit operations to be aligned and explored within the Quality by Design (QbD) context. Real-time pharmaceutical process verification is accomplished by monitoring univariate (temperature, pressure, etc.) and multivariate (spectra, images, etc.) process parameters and quality attributes, to provide an accurate state estimation of the process, required for advanced control strategies. This paper describes the development and use of such tools for a continuous hot melt extrusion (HME) process, monitored with generic sensors and a near-infrared (NIR) spectrometer in real-time, using SIPAT (Siemens platform to collect, display, and extract process information) and additional components developed as needed. The IT architecture of such a monitoring procedure based on uni- and multivariate sensor systems and their integration in SIPAT is shown. SIPAT aligned spectra from the extrudate (in the die section) with univariate measurements (screw speed, barrel temperatures, material pressure, etc.). A multivariate supervisory quality control strategy was developed for the process to monitor the hot melt extrusion process on the basis of principal component analysis (PCA) of the NIR spectra. Monitoring the first principal component and the time-aligned reference feed rate enables the determination of the residence time in real-time.

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Abstract. Blending of powders is a crucial step in the production of pharmaceutical solid dosage forms. The active pharmaceutical ingredient (API) is often a powder that is blended with other powders (excipients) in order to produce tablets. The blending efficiency is influenced by several external factors, such as the desired degree of homogeneity and the required blending time, which mainly depend on the properties of the blended materials and on the geometry of the blender. This experimental study investigates the mixing behavior of acetyl salicylic acid as an API and α-lactose monohydrate as an excipient for different filling orders and filling levels in a blender. A multiple near-infrared probe setup on a laboratoryscale blender is used to observe the powder composition quasi-simultaneously and in-line in up to six different positions of the blender. Partial least squares regression modeling was used for a quantitative analysis of the powder compositions in the different measurement positions. The end point for the investigated mixtures and measurement positions was determined via moving block standard deviation. Observing blending in different positions helped to detect good and poor mixing positions inside the blender that are affected by convective and diffusive mixing.