The spreading of bacterial colonies is strongly influenced by physico-chemical effects. The interplay of surfactant production and wettability results in four qualitatively different morphologies.

We propose a generic model for thin films and shallow drops of a polar active liquid that have a free surface and are in contact with a solid substrate. The model couples evolution equations for the film height and the local polarization in the form of a gradient dynamics supplemented with active stresses and fluxes. A wetting energy for a partially wetting liquid is incorporated allowing for motion of the liquid-solid-gas contact line. This gives a consistent basis for the description of drops of dense bacterial suspensions or compact aggregates of living cells on solid substrates. As example, we analyze the dynamics of two-dimensional active drops (i.e., ridges) and demonstrate how active forces compete with passive surface forces to shape droplets and drive their motion. In our simple two-dimensional scenario we find that defect structures within the polarization profile drastically influence the shape and motility of active droplets. Thus, we can observe a transition from resting to motile droplets via the elimination of defects in the polarization profile. Furthermore, droplet motility is modulated by strong active stresses. Contractile stresses even lead to topological changes, i.e., drop splitting, which is naturally encoded in the evolution equations.

Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.

Light fields carrying orbital angular momentum (OAM) offer a broad variety of applications in which especially an accurate determination of the respective OAM spectrum, i.e., unraveling the content of OAM by its topological charge ℓ, has become a main subject. Even though various techniques have been proposed to measure the OAM spectrum of such modes, many of them fail if optical vortices have to be considered in perturbed or dynamically changing experimental systems. Here, we put forward a novel technique capable of determining the OAM spectrum of light by a single measurement shot, which specifically applies to those fields that have been distorted. Experimentally, our technique only requires to interfere the perturbed light field with a reference field. From the resulting intensity pattern, the accurate OAM spectrum is determined in an all-digital way. We demonstrate our novel approach by numerical simulations and a proof-of-concept experiment employing a model ball lens as an exemplary disturbing object.