A Model for Dry Electron Beam Etching of Resist.

This paper presents a detailed physical model for a novel method of two- and three-dimensional microstructure formation: dry electron beam etching of the resist (DEBER). This method is based on the electron-beam induced thermal depolymerization of positive resist, and its advantages include high throughput and relative simplicity compared to other microstructuring techniques. However, the exact mechanism of profile formation in DEBER has been unclear until now, hindering the optimization of this technique for certain applications.

Fast Electrochemical Actuator with Ti Electrodes in the Current Stabilization Regime.

The actuators needed for autonomous microfluidic devices have to be compact, low-power-consuming, and compatible with microtechnology. The electrochemical actuators could be good candidates, but they suffer from a long response time due to slow gas termination. An actuator in which the gas is terminated orders of magnitude faster has been demonstrated recently.

Highly energetic impact of H and O nanobubbles on Pt surface.

Hypothesis Water electrolysis performed by short (≲5μs) voltage pulses of alternating polarity generates a dense cloud of H and O nanobubbles. Platinum electrodes turn black in this process, while they behave differently when the polarity is not altered. We prove that the modification of Pt is associated with highly energetic impact of nanobubbles rather than with any electrochemical process.

Machine Learning Non-Markovian Quantum Dynamics.

Machine learning methods have proved to be useful for the recognition of patterns in statistical data. The measurement outcomes are intrinsically random in quantum physics, however, they do have a pattern when the measurements are performed successively on an open quantum system. This pattern is due to the system-environment interaction and contains information about the relaxation rates as well as non-Markovian memory effects.

Simulation Complexity of Open Quantum Dynamics: Connection with Tensor Networks.

The difficulty to simulate the dynamics of open quantum systems resides in their coupling to many-body reservoirs with exponentially large Hilbert space. Applying a tensor network approach in the time domain, we demonstrate that effective small reservoirs can be defined and used for modeling open quantum dynamics. The key element of our technique is the timeline reservoir network (TRN), which contains all the information on the reservoir's characteristics, in particular, the memory effects timescale.

Accuracy of Estimating the Area of Cortical Muscle Representations from TMS Mapping Data Using Voronoi Diagrams.

Motor evoked potentials (MEPs) caused by transcranial magnetic stimulation (TMS) provide a possibility of noninvasively mapping cortical muscle representations for clinical and research purposes. The interpretation of such results is complicated by the high variability in MEPs and the lack of a standard optimal mapping protocol. Comparing protocols requires the determination of the accuracy of estimated representation parameters (such as the area), which is problematic without ground truth data.

Optimization of the Navigated TMS Mapping Algorithm for Accurate Estimation of Cortical Muscle Representation Characteristics.

Navigated transcranial magnetic stimulation (nTMS) mapping of cortical muscle representations allows noninvasive assessment of the state of a healthy or diseased motor system, and monitoring changes over time. These applications are hampered by the heterogeneity of existing mapping algorithms and the lack of detailed information about their accuracy. We aimed to find an optimal motor evoked potential (MEP) sampling scheme in the grid-based mapping algorithm in terms of the accuracy of muscle representation parameters.