arXiv subjects

Thomas Strunskus

Publications and source records attributed to Thomas Strunskus.

3 recordsLinked to original sources

arXiv2026

Tunable Conformal Graphene Growth on Oxide Nanotube scaffolds: Towards Superwettable Hierarchical 2D-3D Architectures

Hierarchical hybrid nanoarchitectures that integrate vertically oriented graphene nanowalls, GNWs, with metal oxide, MeOx, nanotube scaffolds offer versatile platform for smart surfaces, nanoelectronics, and electrochemical technologies. Herein we present rapid, dry, plasma-assisted fabrication route that enables direct and conformal growth of GNWs on mechanically robust MeOx nanoforests. The method combines supported single-crystalline organic nanowires as a 1D soft template with sequential plasma-enabled oxide deposition and GNW growth, all performed under mild temperature, power, and vacuum conditions. This approach yields an unprecedented 2D-3D hierarchical architecture consisting of tunable-thickness MeOx nanotubes uniformly decorated with radially oriented graphene nanosheets, forming re-entrant, multiscale surface. Resulting hierarchical roughness imparts fluorine-free, long-term omniphobicity, with contact angles exceeding 170 degree for water, bovine serum, and other complex fluids. GNWs dominate the wetting response across TiO2, Al2O3, and SiO2 nanotube scaffolds, effectively decoupling surface behavior from intrinsic oxide chemistry and maintaining robust repellency under UV irradiation and water condensation. Comprehensive SEM, TEM, XPS, angle-resolved NEXAFS, and Raman analyses elucidate growth mechanism and confirm preservation of the sp2 graphitic framework, together with controlled degree of edge functionalization. Overall, this work establishes universal, substrate-compatible, low-temperature, and scalable route for the fabrication of tunable graphene-metal oxide nano-microstructured multifunctional surfaces.

cond-mat.mtrl-sci↗

arXiv2025

Report on Neural-like Criticality in Ag-based Nanoparticle Networks

Emulating the neural-like information processing dynamics of the brain provides a time and energy efficient approach for solving complex problems. While the majority of neuromorphic hardware currently developed rely on large arrays of highly organized building units, such as in rigid crossbar architectures, in biological neuron assemblies make use of dynamic transitions within highly parallel, reconfigurable connection schemes. Neuroscience suggests that efficiency of information processing in the brain rely on dynamic interactions and signal propagations which are self-tuned and non-rigid. Brain-like dynamic and avalanche criticality have already been found in a variety of self-organized networks of nanoobjects, such as nanoparticles (NP) or nanowires. Here we report on the dynamics of the electrical spiking signals from Ag-based self-organized nanoparticle networks (NPNs) at the example of monometallic Ag NPNs, bimetallic AgAu alloy NPNs and composite Ag/ZrN NPNs, which combine two distinct NP species. We present time series recordings of the resistive switching responses in each network and showcase the determination of switching events as well as the evaluation of avalanche criticality. In each case, for Ag NPN, AgAu NPN and Ag/ZrN NPN, the agreement of three independently derived estimates of the characteristic exponent provides evidence for avalanche criticality. The study shows that Ag-based NPNs offer a broad range of versatility for integration purposes into physical computing systems without destroying their critical dynamics, as the composition of these NPNs can be modified to suit specific requirements for integration.

cond-mat.dis-nn↗

arXiv2022

Strain-invariant, highly water stable all-organic soft conductors based on ultralight multi-layered foam-like framework structures

Soft and flexible conductors are essential in the development of soft robots, wearable electronics, as well as electronic tissue and implants. However, conventional soft conductors are inherently characterized by a large change in conductance upon mechanical deformation or under alternating environmental conditions, e.g., humidity, drastically limiting their application potential and performance. Here, we demonstrate a novel concept for the development of strain-invariant, fatigue resistant and highly water stable soft conductor. By combining different thin film technologies in a three-dimensional fashion, we develop nano- and micro-engineered, multi-layered (< 50 nm), ultra-lightweight (< 15 mg/cm$^3$) foam-like composite framework structures based on PEDOT:PSS and PTFE. The all-organic composite framework structures are characterized by conductivities of up to 184 S/m, remaining strain-invariant between 80 % compressive and 25 % tensile strain. We further show, that the multi-layered composites are characterized by properties that surpass that of framework structures based on the individual materials. Both, the initial electrical and mechanical properties of the composite framework structures are retained during long-term cycling, even after 2000 cycles at 50 % compression. Furthermore, the PTFE functionalization renders the framework structure highly hydrophobic, resulting in stable electrical properties, even when immersed in water for up to 30 days. The here presented concept overcomes the previous limitations of strain-invariant soft conductors and demonstrates for the first time a versatile approach for the development of innovative multi-scaled and multi-layered functional materials, for applications in soft electronics, energy storage and conversion, sensing, catalysis, water and air purification, as well as biomedicine.

physics.app-ph↗