arXiv subjects

Alexander Kovalev

Publications and source records attributed to Alexander Kovalev.

5 recordsLinked to original sources

arXiv2026

Surface Roughness and Filler Restructuring in Magneto-Active Elastomers: Magnetically Hard versus Magnetically Soft Particles

Magneto-active elastomers (MAEs) -- composites of magnetic nano-/micro-particles embedded in a soft polymer matrix -- are promising for soft robotics, as their shape and mechanical properties can be controlled by an applied magnetic field. Most MAEs are filled with magnetically soft (MS) micro-particles, such as carbonyl iron powder (CIP). We employ molecular dynamics to study the differences between thin MAE layers with MS and magnetically hard (MH) filler particles having the same saturation magnetization. We find that both MH and MS elastomers converge to the same high-field state -- a labyrinth of bundled, field-aligned chains -- but do so through distinct pathways: MH MAEs break their zero-field chains, which lie parallel to the MAE layer plane (in-plane), and rotate them into alignment with an external magnetic field, whereas MS MAEs gradually build up field-aligned chains from neighboring particles. We show that the MS model reproduces the magnetization curves and surface roughness of CIP-based MAEs for magnetic fields close to saturation, while maintaining the observed qualitative features at lower field strengths. The mismatch between simulation and experimental results at low fields suggests the need for a MS model that accounts for the multi-domain nature of carbonyl iron microparticles.

cond-mat.soft↗

arXiv2023

FingerFlex: Inferring Finger Trajectories from ECoG signals

Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-order dependencies in data. This article presents the FingerFlex model - a convolutional encoder-decoder architecture adapted for finger movement regression on electrocorticographic (ECoG) brain data. State-of-the-art performance was achieved on a publicly available BCI competition IV dataset 4 with a correlation coefficient between true and predicted trajectories up to 0.74. The presented method provides the opportunity for developing fully-functional high-precision cortical motor brain-computer interfaces.

q-bio.NC↗

arXiv2022

fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships

The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the realm of affective neuroscience including complex decision making processes and the eternal free-will dilemma and facilitates diagnostics of a range of neurological deceases. So far this was possible only using bulky, expensive and immobile fMRI equipment. Here we present an interpretable domain grounded solution to recover the activity of several subcortical regions from the multichannel EEG data and demonstrate up to 60% correlation between the actual subcortical blood oxygenation level dependent sBOLD signal and its EEG-derived twin. Then, using the novel and theoretically justified weight interpretation methodology we recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei. The described results not only pave the road towards wearable subcortical activity scanners but also showcase an automatic knowledge discovery process facilitated by deep learning technology in combination with an interpretable domain constrained architecture and the appropriate downstream task.

physics.med-ph↗

arXiv2017

The Influence of Surface Topography and Surface Chemistry on the Anti-Adhesive Performance of Nanoporous Monoliths

We designed spongy monoliths allowing liquid delivery to their surfaces through continuous nanopore systems (mean pore diameter ca. 40 nm). These nanoporous monoliths were flat or patterned with microspherical structures a few 10 microns in diameter, and their surfaces consisted of aprotic polymer or of TiO2 coatings. Liquid may reduce adhesion forces FAd; possible reasons include screening of solid-solid interactions and poroelastic effects. Softening-induced deformation of flat polymeric monoliths upon contact formation in the presence of liquids enhanced the work of separation WSe. On flat TiO2-coated monoliths, WSe was under wet conditions smaller than under dry conditions, possibly because of liquid-induced screening of solid-solid interactions. Under dry conditions, WSe is larger on flat TiO2-coated monoliths than on flat monoliths with polymeric surface. However, under wet conditions liquid-induced softening results in larger WSe on flat monoliths with polymeric surface than on flat monoliths with oxidic surface. Monolithic microsphere arrays show anti-adhesive properties; FAd and WSe are reduced by at least one order of magnitude as compared to flat nanoporous counterparts. On nanoporous monolithic microsphere arrays, capillarity (WSe is larger under wet than under dry conditions) and solid-solid interactions (WSe is larger on oxide than on polymer) dominate contact mechanics. Thus, the microsphere topography reduces the impact of softening-induced surface deformation and screening of solid-solid interactions associated with liquid supply. Overall, simple modifications of surface topography and chemistry combined with delivery of liquid to the contact interface allow adjusting WSe and FAd over at least one order of magnitude. (...)

cond-mat.soft↗

arXiv2016

Nanoporous monolithic microsphere arrays have anti-adhesive properties independent of humidity

Bioinspired artificial surfaces with tailored adhesive properties have attracted significant interest. While fibrillar adhesive pads mimicking gecko feet are optimized for strong reversible adhesion, monolithic microsphere arrays mimicking the slippery zone of the pitchers of carnivorous plants of the genus Nepenthes show anti-adhesive properties even against tacky counterpart surfaces. In contrast to the influence of topography, the influence of relative humidity (RH) on adhesion has been widely neglected. Some previous works deal with the influence of RH on the adhesive performance of fibrillar adhesive pads. Commonly, humidity-induced softening of the fibrils enhances adhesion. However, little is known on the influence of RH on solid anti-adhesive surfaces. We prepared polymeric nanoporous monolithic microsphere arrays (NMMAs) with microsphere diameters of a few 10 μm to test their anti-adhesive properties at RHs of 2 % and 90 %. Despite the presence of continuous nanopore systems through which the inner nanopore walls were accessible to humid air, the topography-induced anti-adhesive properties of NMMAs on tacky counterpart surfaces were retained even at RH = 90 %. This RH-independent robustness of the anti-adhesive properties of NMMAs significantly contrasts the adhesion enhancement by humidity-induced softening on nanoporous fibrillar adhesive pads made of the same material.

cond-mat.soft↗