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   Aprile, E.; Abe, K.; Agostini, F.; Ahmed Maouloud, S.; Alfonsi, M.; Althueser, L.; Angelino, E.; Angevaare, J. R.; Antochi, V. C.; Antón Martin, D.; et al (July 2022, The European Physical Journal C)

   Abstract The selection of low-radioactive construction materials is of the utmost importance for rare-event searches and thus critical to the XENONnT experiment. Results of an extensive radioassay program are reported, in which material samples have been screened with gamma-ray spectroscopy, mass spectrometry, and $$^{222}$$ 222 Rn emanation measurements. Furthermore, the cleanliness procedures applied to remove or mitigate surface contamination of detector materials are described. Screening results, used as inputs for a XENONnT Monte Carlo simulation, predict a reduction of materials background ( $$\sim $$ ∼ 17%) with respect to its predecessor XENON1T. Through radon emanation measurements, the expected $$^{222}$$ 222 Rn activity concentration in XENONnT is determined to be 4.2 ( $$^{+0.5}_{-0.7}$$ - 0.7 + 0.5 )  $$\upmu $$ μ Bq/kg, a factor three lower with respect to XENON1T. This radon concentration will be further suppressed by means of the novel radon distillation system. 

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2. Dynamic material deposition control for large-scale additive manufacturing

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3. Differentiability and Control of a Model for Granular Material Accumulation

   https://doi.org/10.1109/CDC51059.2022.9992447  

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4. Spatial Iterative Learning Control for Multi-material Three-Dimensional Structures

   https://doi.org/10.1115/1.4046576  

   Afkhami, Zahra; Pannier, Christopher; Aarnoudse, Leontine; Hoelzle, David; Barton, Kira (January 2021, ASME Letters in Dynamic Systems and Control)

   Abstract Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process. 

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5. Control of chiral orbital currents in a colossal magnetoresistance material

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