arXiv subjects

R. van Roij

Publications and source records attributed to R. van Roij.

At least 19 recordsLinked to original sources

arXiv2026

Nonlinear iontronic signal processing with neuromorphic Spike Rate-Dependent Plasticity

We present an integrated iontronic memristor circuit that reproduces biologically inspired Spike Rate-Dependent Plasticity (SRDP) and functions as a physical nonlinear frequency kernel, which we demonstrate can be used to classify natural auditory data. The fluidic circuit integrates two parallel memristive membranes containing short and long conical memristive channels with opposite orientations, giving rise to heterogeneous internal timescales and different potentiation responses. As a result, the circuit exhibits a nonlinear frequency response in which low-frequency inputs decrease the overall conductance, whereas higher-frequency inputs increase it, thereby emulating biological SRDP. Our experimental measurements are inspired by and consistent with predictions of a theoretical model. We demonstrate the functionality of the device by separating encoded sound signals from different insects that cannot be linearly separated. By unifying theoretical predictions with experimental realisation of coupled iontronic memristors, this work moves beyond isolated components and demonstrates how heterogeneous iontronic dynamics can unlock nonlinear time-series processing capabilities, essential for future iontronic neuromorphic computing.

cond-mat.soft↗

arXiv2026

Chemically Regulated Conical Channel Synapse for Neuromorphic and Sensing Applications

Fluidic iontronics offer a unique capability for emulating the chemical processes found in neurons. We extract multiple distinct chemically regulated synaptic features from an experimentally accessible conical microfluidic channel carrying functionalized surface groups, using finite-element calculations of continuum transport equations. By modeling a Langmuir-type surface reaction on the channel wall we couple fast voltage-induced volumetric salt accumulation with a long-term channel surface charge modulation by means of fast charging and slow discharging. These nonlinear charging dynamics emerge across several orders of magnitude of reaction rates and equilibria, and are understood through an analytic approximation rooted in first-principles. We show how short-and long-term potentiation and depression, frequency-dependent plasticity, and chemical-electrical signal spike-timing dependence and coincidence detection (acting like a chemical-electrical AND logic gate), akin to the NMDA mechanism for Hebbian learning in biological synapses, can all be emulated.

cond-mat.soft↗

arXiv2026

Energy-efficient time series processing in real-time with fluidic iontronic memristor circuits

Iontronic neuromorphic computing has emerged as a rapidly expanding paradigm. The arrival of angstrom-confined iontronic devices enables ultra-low power consumption with dynamics and memory timescales that intrinsically align well with signals of natural origin, a challenging combination for conventional (solid-state) neuromorphic materials. However, comparisons to earlier conventional substrates and evaluations of concrete application domains remain a challenge for iontronics. Here we propose a pathway toward iontronic circuits that can address established time series benchmark tasks, enabling performance comparisons and highlighting possible application domains for efficient real-time time series processing. We model a Kirchhoff-governed circuit with iontronic memristors as edges, while the dynamic internal voltages serve as output vector for a linear readout function, during which energy consumption is also logged. All these aspects are integrated into the open-source pyontronics package. Without requiring input encoding or virtual timing mechanisms, our simulations demonstrate prediction performance comparable to various earlier solid-state reservoirs, notably with an exceptionally low energy consumption of over 5 orders of magnitude lower. These results suggest a pathway of iontronic technologies for ultra-low-power real-time neuromorphic computation.

cond-mat.soft↗

arXiv2025

Echo State and Band-pass Networks with aqueous memristors: leaky reservoir computing with a leaky substrate

Recurrent Neural Networks (RNN) are extensively employed for processing sequential data such as time series. Reservoir computing (RC) has drawn attention as an RNN framework due to its fixed network that does not require training, making it an attractive platform for hardware based machine learning. We establish an explicit correspondence between the well-established mathematical RC implementations of Echo State Networks and Band-pass Networks with Leaky Integrator nodes on the one hand and a physical circuit containing iontronic simple volatile memristors on the other. These aqueous iontronic devices employ ion transport through water as signal carriers, and feature a voltage-dependent (memory) conductance. The activation function and the dynamics of the Leaky Integrator nodes naturally materialise as the (dynamic) conductance properties of iontronic memristors, while a simple fixed local current-to-voltage update rule at the memristor terminals facilitates the relevant matrix coupling between nodes. We process various time series, including pressure data from simulated airways during breathing that can be directly fed into the network due to the intrinsic responsiveness of iontronic devices to applied pressures. We accomplish this by employing established physical equations of motion of iontronic memristors for the internal dynamics of the circuit.

cs.ET↗

arXiv2024

A simple mathematical theory for Simple Volatile Memristors and their spiking circuits

In pursuit of neuromorphic (brain-inspired) devices, memristors (memory-resistors) have emerged as effective components for emulating neuronal circuitry. Here we formally define a class of Simple Volatile Memristors (SVMs) based on a simple conductance equation of motion from which we build a simple mathematical theory on the dynamics of isolated SVMs and SVM-based spiking circuits. Notably, SVMs include various fluidic iontronic devices that have recently garnered significant interest due to their unique quality of operating within the same medium as the brain. Specifically we show that symmetric SVMs produce non self-crossing current-voltage hysteresis loops, while asymmetric SVMs produce self-crossing loops. Additionally, we derive a general expression for the enclosed area in a loop, providing a relation between the voltage frequency and the SVM memory timescale. These general results are shown to materialise in physical finite-element calculations of microfluidic memristors. An SVM-based circuit has been proposed that exhibits all-or-none and tonic neuronal spiking. We generalise and analyse this spiking circuit, characterising it as a two-dimensional dynamical system. Moreover, we demonstrate that stochastic effects can induce novel neuronal firing modes absent in the deterministic case. Through our analysis, the circuit dynamics are well understood, while retaining its explicit link with the physically plausible underlying system.

cond-mat.soft↗

arXiv2024

Advanced iontronic spiking modes with multiscale diffusive dynamics in a fluidic circuit

Fluidic iontronics is emerging as a distinctive platform for implementing neuromorphic circuits, characterized by its reliance on the same aqueous medium and ionic signal carriers as the brain. Drawing upon recent theoretical advancements in both iontronic spiking circuits and in dynamic conductance of conical ion channels, which form fluidic memristors, we expand the repertoire of proposed neuronal spiking dynamics in iontronic circuits. Through a modelled circuit containing channels that carry a bipolar surface charge, we extract phasic bursting, mixed-mode spiking, tonic bursting, and threshold variability, all with spike voltages and frequencies within the typical range for mammalian neurons. These features are possible due to the strong dependence of the typical conductance memory retention time on the channel length, enabling timescales varying from individual spikes to bursts of multiple spikes within a single circuit. These advanced forms of neuronal-like spiking support the exploration of aqueous iontronics as an interesting platform for neuromorphic circuits.

cond-mat.soft↗

arXiv2024

Brain-inspired computing with fluidic iontronic nanochannels

The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy to fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarisation our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarisation, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in-silico classification with a simple readout function. Our results represent a significant step towards realising the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain.

cond-mat.soft↗

arXiv2024

Pressure-Gated Microfluidic Memristor for Pulsatile Information Processing

A hitherto unexploited characteristic feature of emerging iontronic devices for information processing is the intrinsic mobility of the medium (water) of dissolved ions in aqueous electrolytes, which therefore not only respond to voltage but also to pressure. Here we study a microfluidic memristor, in the form of a conical channel, exposed to simultaneously applied time-dependent voltage and pressure drops, through numerical solutions of the Poisson-Nernst-Planck-Stokes equations for ion and fluid transport. We show that the channel's memristive properties can be enhanced, reduced or instantaneously reset by a suitable pressure, and we leverage this finding with two examples of time series processing of simultaneously applied voltage and pressure pulses. We not only show that the distinction between different voltage time series can be improved by enhancing the conductance response with corresponding pressure pulses, but also that the bandwidth of information transfer through the channel can be doubled by letting the pressure pulses represent a second independent time series.

cond-mat.soft↗

arXiv2024

Asymmetric rectified electric fields for symmetric electrolytes

In this paper, building upon the discovery of asymmetric rectified electric fields (AREF) in recent experiments [S.H. Hashemi et al., Physical Review Letters 121, 185504 (2018)], we explore the generation of AREF by applying a sawtooth-like voltage to 1:1 electrolytes with equal diffusion coefficients confined between two planar blocking electrodes. This differs from an earlier approach based on a sinusoidal AC voltage applied to 1:1 electrolytes with unequal diffusion coefficients. By numerically solving the full Poisson-Nernst-Planck equations, we demonstrate that AREF can be generated by a slow rise and a fast drop of the potential (or vice versa), even for electrolytes with equal diffusion coefficients of the cations and anions. We employ an analytically constructed equivalent electric circuit to explain the underlying physical mechanism. Importantly, we find that the strength of AREF can be effectively tuned from zero to its maximal value by only manipulating the time-dependence of the driving voltage, eliminating the necessity to modify the electrolyte composition between experiments. This provides valuable insights to control the manipulation of AREF, which facilitates enhanced applications in diverse electrochemical systems.

cond-mat.soft↗

arXiv2023

Iontronic Neuromorphic Signaling with Conical Microfluidic Memristors

Experiments have shown that the conductance of conical channels, filled with an aqueous electrolyte, can strongly depend on the history of the applied voltage. These channels hence have a memory and are promising elements in brain-inspired (iontronic) circuits. We show here that the memory of such channels stems from transient concentration polarization over the ionic diffusion time. We derive an analytic approximation for these dynamics which shows good agreement with full finite-element calculations. Using our analytic approximation, we propose an experimentally realisable Hodgkin-Huxley iontronic circuit where micrometer cones take on the role of sodium and potassium channels. Our proposed circuit exhibits key features of neuronal communication such as all-or-none action potentials upon a pulse stimulus and a spike train upon a sustained stimulus.

cond-mat.soft↗

arXiv2023

Unveiling the capabilities of bipolar conical channels in neuromorphic iontronics

Conical channels filled with an aqueous electrolyte have been proposed as promising candidates for iontronic neuromorphic circuits. This is facilitated by a novel analytical model for the internal channel dynamics [Kamsma et al., arXiv:2301.06158, 2023], the relative ease of fabrication of conical channels, and the wide range of achievable memory retention times by varying the channel lengths. In this work, we demonstrate that the analytical model for conical channels can be generalized to channels with an inhomogeneous surface charge distribution, which we predict to exhibit significantly stronger current rectification and more pronounced memristive properties in the case of bipolar channels, i.e. channels where the tip and base carry a surface charge of opposite sign. Additionally, we show that the use of bipolar conical channels in a previously proposed iontronic circuit features hallmarks of neuronal communication, such as all-or-none action potentials and spike train generation. Bipolar channels allow, however, for circuit parameters in the range of their biological analogues, and exhibit membrane potentials that match well with biological mammalian action potentials, further supporting its potential for bio-compatibility.

physics.bio-ph↗

arXiv2021

Primitive Model Electrolytes in the Near and Far Field: Decay Lengths from DFT and Simulations

Inspired by recent experimental observations of anomalously large decay lengths in concentrated electrolytes, we revisit the Restricted Primitive Model (RPM) for an aqueous electrolyte. We investigate the asymptotic decay lengths of the one-body ionic density profiles for the RPM in contact with a planar electrode using classical Density Functional Theory (DFT), and compare these with the decay lengths of the corresponding two-body correlation functions in bulk systems, obtained in previous Integral Equation Theory (IET) studies. Extensive Molecular Dynamics (MD) simulations are employed to complement the DFT and IET predictions. Our DFT calculations incorporate electrostatic interactions between the ions using three different (existing) approaches: one based on the simplest mean field treatment of Coulomb interactions (MFC), whilst the other two employ the Mean Spherical approximation (MSA). The MSAc invokes only the MSA bulk direct correlation function whereas the MSAu also incorporates the MSA bulk internal energy. Although MSAu yields profiles that agree best with MD simulations in the near field, in the far field we observe that the decay lengths are consistent between IET, MSAc, and MD simulations, whereas those from MFC and MSAu deviate significantly. Using DFT we calculated the solvation force, which relates directly to surface force experiments. We find that its decay length is neither qualitatively nor quantitatively close to the large decay lengths measured in experiments and conclude that the latter cannot be accounted for by the primitive model. The anomalously large decay lengths found in surface force measurements require an explanation that lies beyond primitive models.

cond-mat.soft↗

arXiv2019

Coupled water, charge and salt transport in heterogeneous nano-fluidic systems

We theoretically study the electrokinetic transport properties of nano-fluidic devices under the influence of a pressure, voltage or salinity gradient. On a microscopic level the behaviour of the device is quantified by the Onsager matrix ${\bf L}$, a generalised conductivity matrix relating the local driving forces and the induced volume, charge and salt flux. Extending ${\bf L}$ from a local to a global linear-response relation is trivial for homogeneous electrokinetic systems, but in this manuscript we derive a generalised conductivity matrix ${\bf G}$ from ${\bf L}$ that applies also to heterogeneous electrokinetic systems. This extension is especially important in the case of an imposed salinity gradient, which gives necessarily rise to heterogeneous devices. Within this formalism we can also incorporate a heterogeneous surface charge due to, for instance, a charge regulating boundary condition, which we show to have a significant impact on the resulting fluxes. The predictions of the Poisson-Nernst-Planck-Stokes theory show good agreement with exact solutions of the governing equations determined using the Finite Element Method under a wide variety of parameters. Having established the validity of the theory, it provides an accessible method to analyse electrokinetic systems in general without the need of extensive numerical methods. As an example, we analyse a Reverse Electrodialysis "blue energy" system, and analyse how the many parameters that characterise such a system affect the generated electrical power and efficiency.

cond-mat.soft↗

arXiv2019

Analysis of electrolyte transport through charged nanopores

We revisit the classical problem of flow of electrolyte solutions through charged capillary nanopores or nanotubes as described by the capillary pore model (also called "space charge" theory). This theory assumes very long and thin pores and uses a one-dimensional flux-force formalism which relates fluxes (electrical current, salt flux, fluid velocity) and driving forces (difference in electric potential, salt concentration, pressure). We analyze the general case with overlapping electric double layers in the pore and a nonzero axial salt concentration gradient. The 3$\times$3 matrix relating these quantities exhibits Onsager symmetry and we report a significant new simplification for the diagonal element relating axial salt flux to the gradient in chemical potential. We prove that Onsager symmetry is preserved under changes of variables, which we illustrate by transformation to a different flux-force matrix given by Gross and Osterle (1968). The capillary pore model is well-suited to describe the nonlinear response of charged membranes or nanofluidic devices for electrokinetic energy conversion and water desalination, as long as the transverse ion profiles remain in local quasi-equilibrium. As an example, we evaluate electrical power production from a salt concentration difference by reverse electrodialysis, using an efficiency vs. power diagram. We show that since the capillary pore model allows for axial gradients in salt concentration, partial loops in current, salt flux or fluid flow can develop in the pore. Predictions for macroscopic transport properties using a reduced model where the potential and concentration are assumed to be invariant with radial coordinate ("uniform potential" or "fine capillary pore" model), are close to results of the full model.

physics.chem-ph↗

arXiv2018

Flow-induced surface charge heterogeneity in electrokinetics due to Stern-layer conductance coupled to reaction kinetics

We theoretically study the electrokinetic problem of a pressure-induced liquid flow through a narrow long channel with charged walls, going beyond the classical Helmholtz-Schmolukowski picture by considering the surprisingly strong combined effect of (i) Stern layer conductance and (ii) dynamic charge-regulating rather than fixed surface charges. We find that the water flow induces, apart from the well-known streaming potential, also a strongly heterogeneous surface charge and zeta-potential on chemically homogeneous channel walls. Moreover, we identify a novel steady state with a nontrivial 3D electric flux with 2D surface charges acting as sources and sinks. For a pulsed pressure drop our findings also provide a first-principles explanation for ill-understood experiments on the effect of flow on interfacial chemistry [D. Lis et al., Science 344, 1138 (2014)].

cond-mat.soft↗

arXiv2014

Density Functional Theory for Chiral Nematic Liquid Crystals

Even though chiral nematic phases were the first liquid crystals experimentally observed more than a century ago, the origin of the thermodynamic stability of cholesteric states is still unclear. In this Letter we address the problem by means of a novel density functional theory for the equilibrium pitch of chiral particles. When applied to right-handed hard helices, our theory predicts an entropy-driven cholesteric phase, which can be either right- or left-handed, depending not only on the particle shape but also on the thermodynamic state. We explain the origin of the chiral ordering as an interplay between local nematic alignment and excluded-volume differences between left- and right-handed particle pairs.

cond-mat.soft↗

arXiv2012

Free Minimization of the Fundamental Measure Theory Functional: Freezing of Parallel Hard Squares and Cubes

Due to remarkable advances in colloid synthesis techniques, systems of squares and cubes, once an academic abstraction for theorists and simulators, are nowadays an experimental reality. By means of a free minimization of the free-energy functional, we apply Fundamental Measure Theory to analyze the phase behavior of parallel hard squares and hard cubes. We compare our results with those obtained by the traditional approach based on the Gaussian parameterization, finding small deviations and good overall agreement between the two methods. For hard squares our predictions feature at intermediate packing fraction a smectic phase, which is however expected to be unstable due to thermal fluctuations. This implies that for hard squares the theory predicts either a vacancy-rich second-order transition or a vacancy-poor weakly first-order phase transition at higher density. In accordance with previous studies, a second-order transition with a high vacancy concentration is predicted for hard cubes.

cond-mat.stat-mech↗

arXiv2011

Polydispersity stabilizes Biaxial Nematic Liquid Crystals

Inspired by the observations of a remarkably stable biaxial nematic phase [E.v.d. Pol et al., Phys. Rev. Lett. 103, 258301 (2009)], we investigate the effect of size polydispersity on the phase behavior of a suspension of boardlike particles. By means of Onsager theory within the restricted orientation (Zwanzig) model we show that polydispersity induces a novel topology in the phase diagram, with two Landau tetracritical points in between which oblate uniaxial nematic order is favored over the expected prolate order. Additionally, this phenomenon causes the opening of a huge stable biaxiality regime in between uniaxial nematic and smectic states.

cond-mat.soft↗