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Abstract The impurity density in high-purity germanium detectors is crucial to understand and simulate such detectors. However, the information about the impurities provided by the manufacturer, based on Hall effect measurements, is typically limited to a few locations and comes with a large uncertainty. As the voltage dependence of the capacitance matrix of a detector strongly depends on the impurity density distribution, capacitance measurements can provide a path to improve the knowledge on the impurities. The novel method presented here uses a machine-learned surrogate model, trained on precise GPU-accelerated capacitance calculations, to perform full Bayesian inference of impurity distribution parameters from capacitance measurements. All steps use open-source Julia software packages. Capacitances are calculated with SolidStateDetectors.jl , machine learning is done with Flux.jl and Bayesian inference performed using BAT.jl . The capacitance matrix of a detector and its dependence on the impurity density is explained and a capacitance bias-voltage scan of an n -type true-coaxial test detector is presented. The study indicates that the impurity density of the test detector also has a radial dependence. 

Membrane characterization provides essential information for the scale-up, design, and optimization of new separation systems. We recently proposed the diafiltration apparatus for high-throughput analysis (DATA), which enables a 5-times reduction in the time, energy, and the number of experiments necessary to characterize membrane transport properties. This paper applies formal model-based design of experiments (MBDoE) techniques to further analyse and optimize DATA. For example, the eigenvalues and eigenvectors of the Fisher Information Matrix (FIM) show dynamic diafiltration experiments improve parameter identifiability by 3 orders of magnitude compared to traditional filtration experiments. Moreover, continuous retentate conductivity measurements in DATA improve A-, D-, E-, and ME-optimal MBDoE criteria by between 6 % and 32 %. Using these criteria, we identify pressure and initial concentrations conditions that maximize parameter precision and remove correlations. 

Studying tropical hydroclimate and productivity change in the past is critical for understanding global climate dynamics. Northwest Australia is an ideal location for investigating Australian monsoon dynamics, the variability of the Indonesian Throughflow (ITF), and their impact on past productivity and Pacific warm pool evolution, which remain poorly understood during the 40 kyr world in the mid‐early Pleistocene. In this study, we present multi‐proxy records from International Ocean Discovery Program (IODP) Site U1483 in the Timor Sea spanning the last 2,000 ka, including orbitally‐resolved records from the 40 kyr world between 2,000 and 1,300 ka. Our results suggest that northwest Australia underwent a step of increased aridification and that productivity in the Timor Sea declined during the transition from ∼1,700 to ∼1,400 ka. We attribute this aridification to the reduced moisture supply to this region caused by the ITF restriction and warm pool contraction. We ascribe the declined productivity to a decrease in the nutrient supply of the Pacific source water associated with global nutrient redistribution. At orbital timescale, multiple mechanisms, including sea level changes, monsoon, and the Intertropical Convergence Zone (ITCZ) dynamics, and variations in the ITF and Walker circulation could have controlled variations of productivity and terrigenous input in the Timor Sea during the 40 kyr world. Our bulk nitrogen and benthic carbon isotope records suggest a strong coupling to biogeochemical changes in the Pacific during this period. This research contributes to a better understanding of tropical hydroclimate and productivity changes during the 40 kyr world.

 

Patterned charged membranes with engendered useful characteristics can offer selective transport of electrolytes. Chemical patterning across the membrane surface via a physical inkjet deposition process requires precise control of the reactive-ink formulation, which enables the introduction of charged functionality to the membrane. This study develops a new dynamic mathematical model for the primary step of the batch reactive-ink formulation considering an ink mixture of copper sulphate and ascorbic acid. Nonlinear least squares parameter estimation is performed to infer three kinetic model parameters by analysing data from nine dynamic experiments simultaneously. Global sensitivity and Fisher information matrix (FIM) analyses reveal only one kinetic parameter is identifiable from time-series pH measurements. The fitted model can capture the overall nonlinear dynamics of the batch reaction and works best for initial Cu2 + concentrations between 30 and 50 mM. Time-series Cu2 + or Cu+ concentration measurements are recommended in future experiments to elucidate the kinetics of reactive-ink formulation.