arXiv subjects

John Linkhorst

Publications and source records attributed to John Linkhorst.

4 recordsLinked to original sources

arXiv2026

Morphology Engineering of Mixed Ionic Electronic Conductors through Aqueous Phase Separation

The performance of organic electrochemical transistors (OECTs) is fundamentally governed by the interplay between ionic accessibility and electronic transport within organic mixed ionic-electronic conductors. Although increasing channel thickness enhances transconductance, it also prolongs ion transport, resulting in the well-known gain-speed trade-off. Here, we demonstrate that engineering the internal morphology of PEDOT:PSS:PEI films through pH-induced aqueous phase separation provides an effective route to mitigate this limitation. The resulting interconnected pore network promotes electrolyte penetration and increases the electrochemically addressable volume, while DMSO treatment and annealing enhance the continuity and ordering of the PEDOT-rich electronic phase. Consequently, porous OECT channels achieve a transconductance of 30 mS and a response time of 13 ms at an ultralow gate voltage of 0.05 V despite channel thicknesses exceeding 100 um. Comparison of films with comparable electronic conductivity but different pore architectures identifies morphology as the dominant factor governing device performance, supporting a transition from predominantly surface-limited modulation toward spatially distributed mixed ionic-electronic transport. Beyond demonstrating a scalable water-based fabrication strategy, this work establishes internal morphology as a design parameter that complements molecular structure and device geometry in organic mixed conductors, providing a general framework for the development of high-performance OECTs, soft bioelectronics, and future neuromorphic materials.

cond-mat.mtrl-sci↗

arXiv2022

Reminding Forgetful Organic Neuromorphic Device Networks

Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high switching speeds and low energy demand. However, as electrochemical systems, they are prone to self-discharge through parasitic electrochemical reactions. Therefore, the network's synapses forget their trained conductance states over time. This work integrates single-device high-resolution charge transport models to simulate neuromorphic device networks and analyze the impact of self-discharge on network performance. Simulation of a single-layer nine-pixel image classification network reveals no significant impact of self-discharge on training efficiency. And, even though the network's weights drift significantly during self-discharge, its predictions remain 100\% accurate for over ten hours. On the other hand, a multi-layer network for the approximation of the circle function is shown to degrade significantly over twenty minutes with a final mean-squared-error loss of 0.4. We propose to counter the effect by periodically reminding the network based on a map between a synapse's current state, the time since the last reminder, and the weight drift. We show that this method with a map obtained through validated simulations can reduce the effective loss to below 0.1 even with worst-case assumptions. Finally, while the training of this network is affected by self-discharge, a good classification is still obtained. Electrochemical organic neuromorphic devices have not been integrated into larger device networks. This work predicts their behavior under nonideal conditions, mitigates the worst-case effects of parasitic self-discharge, and opens the path toward implementing fast and efficient neural networks on organic neuromorphic hardware.

cs.ET↗

arXiv2022

Direct 3D observation and unraveling of electroconvection phenomena during concentration polarization at ion-exchange membranes

A decade ago, two-dimensional microscopic flow visualization proved the theoretically predicted existence of electroconvection roles as well as their decisive role in destabilizing the concentration polarization layer at ion-selective fluid/membrane interfaces. Electroconvection induces chaotic flow vortices injecting volume having bulk concentration into the ion-depleted diffusion layer at the interface. Experimental quantification of these important flow patterns have so far only been carried out in 2D. Numerical direct simulations suggest 3D features, yet experimental proof is lacking. 3D simulations are also limited in covering extended spacial and temporal scales. This study presents a new comprehensive experimental method for the time-resolved recording of the 3D electroconvective velocity field near a cation-exchange membrane. For the first time, the spatio-temporal velocity field can be visualized in 3D at multiples of the overlimiting current density. In contrast to today's simulations, these experiments cover length and time scales typical for actual electrodialytic membrane processes. We visualize coherent vortex structures and reveal the changes in the velocity field and its statistics during the transition from vortex rolls to vortex rings with increasing current density. The transition is characterized by changes in the rotational direction, mean square velocity, and temporal energy spectrum with only little influence on the spatial spectrum. These findings indicate a more significant impact of EC's structural change on the mean square velocities and temporal spectra than on the spatial spectra. This knowledge is a prerequisite for engineering ion-selective surfaces that will enable the operation of electrically driven processes beyond the diffusion-limited Nernst regime.

physics.flu-dyn↗

arXiv2018

3D Nanofabrication inside rapid prototyped microfluidic channels showcased by wet-spinning of single micrometre fibres

Microfluidics is an established multidisciplinary research domain with widespread applications in the fields of medicine, biotechnology and engineering. Conventional production methods of microfluidic chips have been limited to planar structures, preventing the exploitation of truly three-dimensional architectures for applications such as multi-phase droplet preparation or wet-phase fibre spinning. Here the challenge of nanofabrication inside a microfluidic chip is tackled for the showcase of a spider-inspired spinneret. Multiphoton lithography, an additive manufacturing method, was used to produce free-form microfluidic masters, subsequently replicated by soft lithography. Into the resulting microfluidic device, a threedimensional spider-inspired spinneret was directly fabricated in-chip via multiphoton lithography. Applying this unprecedented fabrication strategy, the to date smallest printed spinneret nozzle is produced. This spinneret resides tightly sealed, connecting it to the macroscopic world. Its functionality is demonstrated by wet-spinning of single-digit micron fibres through a polyacrylonitrile coagulation process induced by a water sheath layer. The methodology developed here demonstrates fabrication strategies to interface complex architectures into classical microfluidic platforms. Using multiphoton lithography for in-chip fabrication adopts a high spatial resolution technology for improving geometry and thus flow control inside microfluidic chips. The showcased fabrication methodology is generic and will be applicable to multiple challenges in fluid control and beyond.

physics.app-ph↗