arXiv subjects

M. Schilling

Publications and source records attributed to M. Schilling.

4 recordsLinked to original sources

arXiv2026

Clustering based magnetic assays for SARS-CoV-2 detection with scFv-functionalized magnetic nanoparticles

Magnetic nanoparticles (MNP) can be used in magnetic immunoassays (MIA) by functionalizing them with antibodies. In homogeneous assays that follow a "mix-and-measure" approach, it is then sufficient to add the sample in question and evaluate the response of the MNPs using a magnetic measurement method. Magnetic Particle Spectroscopy (MPS) is a fast and sensitive method for this purpose, capable of determining the binding state by measuring changes in Brownian relaxation. For further analysis, alternating current susceptometry (ACS) is considered, although it is significantly more time-consuming and therefore no option for applications interested in point of care detection. In this study, we aim to further improve the approach of MIA evaluated by MPS. To this end, we use self-synthesized scFv fragments instead of whole IgG antibodies during functionalization in order to keep the size of the BNF-Dextran MNP with 80 nm nominal diameter as small as possible, which allows for greater relative size changes upon binding to an analyte. Virus-like particles (VLP) and the N protein of SARS-CoV-2, which were also produced in-house, are used as analytes. Due to multiple binding sites, these form cluster structures, which, in the case of VLP, were investigated in greater depth ACS to assess their field dependence. In addition, the sensitivity of the assays was analyzed as a function of MNP concentration, and the detection limit for both analytes was estimated. We found that, using scFv-functionalized MNPs, we were able to detect low concentrations of just 490 fM of SARS-CoV-2 VLP and 1.7 nM of the N protein. However, the slightly above-proportional increase in sensitivity as the MNP concentration decreases does not automatically imply a better detection limit.

physics.med-ph↗

arXiv2026

Full Single-Quantum Control of Particles in Penning Traps for Symmetry Tests at the Quantum Limit

The BASE collaboration aims to measure antimatter systems with the highest precision in order to perform a rigorous test of CPT symmetry and search for physics beyond the Standard Model. As part of the BASE collaboration, we pursue the development of quantum logic inspired cooling and detection techniques for g-factor measurements of (anti-)protons. Implementing these methods requires full quantum-level control of individual antimatter particles confined in cryogenic Penning traps. By mapping the (anti-)proton's internal state onto a co-trapped 9Be+ "logic" ion via free Coulomb coupling in a double-well potential, we can accelerate measurement cycles and push g-factor precision measurements on (anti-)protons toward the quantum limit. Here, we present an overview of the proposed method and the current status of the project, with special emphasis on the new cryogenic multi-Penning-trap stack and the proton detection system.

physics.atom-ph↗

arXiv2022

Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent

Legged locomotion is widespread in nature and has inspired the design of current robots. The controller of these legged robots is often realized as one centralized instance. However, in nature, control of movement happens in a hierarchical and decentralized fashion. Introducing these biological design principles into robotic control systems has motivated this work. We tackle the question whether decentralized and hierarchical control is beneficial for legged robots and present a novel decentral, hierarchical architecture to control a simulated legged agent. Three different tasks varying in complexity are designed to benchmark five architectures (centralized, decentralized, hierarchical and two different combinations of hierarchical decentralized architectures). The results demonstrate that decentralizing the different levels of the hierarchical architectures facilitates learning of the agent, ensures more energy efficient movements as well as robustness towards new unseen environments. Furthermore, this comparison sheds light on the importance of modularity in hierarchical architectures to solve complex goal-directed tasks. We provide an open-source code implementation of our architecture (https://github.com/wzaielamri/hddrl).

cs.AI↗

arXiv2018

MnSi-nanostructures obtained from thin films: magnetotransport and Hall effect

We present a comparative study of the (magneto)transport properties, including Hall effect, of bulk, thin film and nanostructured MnSi. In order to set our results in relation to published data we extensively characterize our materials, this way establishing a comparatively good sample quality. Our analysis reveals that in particular for thin film and nanostructured material, there are extrinsic and intrinsic contributions to the electronic transport properties, which by modeling the data we separate out. Finally, we discuss our Hall effect data of nanostructured MnSi under consideration of the extrinsic contributions and with respect to the question of the detection of a topological Hall effect in a skyrmionic phase.

cond-mat.mes-hall↗