arXiv subjects

Sébastien Pecqueur

Publications and source records attributed to Sébastien Pecqueur.

12 recordsLinked to original sources

arXiv2026

Storing Sensor Events in the Interconnection Strength of Conducting Polymer Dendrites

If electronics drives only electrons to charge electrodes, natural systems learn by moving matter to evolve. Morphogenesis in sessile organisms can be seen both as a fabrication process and as an operative mechanism. However, intricating manufacturing and programming functionalities in electronic hardware is not conventional. In this study, we experimentally implement such a concept of an evolutionary electrical system using a neuro-inspired electronic nose as a model, to store a history of sensory data in the physical properties of the electrical interconnects of sensing elements. Triggered only by volatile molecule exposures, different sensing elements change instantaneously and reversibly their impedance, so pulse voltages enable the electrochemical growth of conducting polymer dendrites. The strength of the evolving interconnects is specific to the sensing materials and to the nature of volatile molecules to which they are exposed. The dendritic growths occur exclusively when exposed to volatile samples, and stop immediately after interrupting the exposure. The capability of such "passive memory" was also assessed by simulating a network architecture, which showed that this way of storing information should greatly diminish the fabrication complexity of a highly dense sensing array while realistically enabling its calibration to classify user-specific environment exposures. By demonstrating that memory in electronics can be a concept linked to manufacturing like in living organisms, this study shows that low material resources and low energy activation can be exploited for practical electronic applications in future-emerging sensing technologies.

physics.chem-ph↗

arXiv2026

On Electropolymerized Fingerprints and their Potential for Identification and Encryption

While human technology is ruled by determinism, biological systems exploit a subtle balance of control and stochasticity. This balance, evident in the morphogenesis of textural patterns imprinted on leaves, fur or skin can help hierarchize organisms both as a representative of their species and as unique individuals. In this study, we identified that, by exploiting electrochemistry, it is possible to generate such versatile but specific textures, to imprint patterns of a conducting polymer on a conducting substrate. It is shown that the 1D morphogenesis of conducting polymer dendrites on wires translates, on 2D surfaces, as highly heterogeneous coatings of dark spots, rosettes or marbled patterns. Despite their inherent stochasticity, these patterns are characteristic of the physical conditions they grew in, and particularly of the chemical content of the electroactive solution used for their electropolymerization. A statistical study demonstrates that these patterns could be used as fingerprints to physically tag the identity of a solution within a specific class. By the identification of a new electrochemical process which allows generating physical fingerprints with optical, electrical and chemical contrast on an electrode, this research paves the way toward a disruptive low-cost technology which could allow any end-user to generate personal tags on a glass slide or on a micro-chip, to engrave physically-encrypted personal information for various applications.

physics.chem-ph↗

arXiv2025

Chemical Control for the Morphogenesis of Conducting Polymer Dendrites in Water

Conducting polymer dendrite (CPD) morphogenesis is an electrochemical process that unlocks the potential to implement in materio evolving intelligence in electrical systems: As an electronic device experiences transient voltages in an open-space wet environment, electrically conductive structures physically change over time, programming the filtering properties of an interconnect as a non-linear analog device. Mimicking the self-preservation strategies of some sessile organisms, CPDs adapt their morphology to the environment they grow in. Either studied as an electrochemical experiment or as neuromorphic devices, the dependence of CPDs' electrical properties on the chemical nature of their environment is still unreported, despite the inter-dependence between the electrical properties of the electrogenerated material and the chemical composition of their growth medium. In this study, we report on the existing intrication between the nature and concentration of the electrolytes, electroactive compounds and co-solvents and the electrical and the electrochemical properties of CPDs in water. CPDs exhibit various chemical sensitivities in water: their morphology is highly dependent on the nature of the chemical resources available in their environment. The selection of these resources therefore critically influence morphogenesis. Also, concentrations have different impacts on growth dynamics, conditioning the balance between thermodynamic and kinetic control on polymer electrosynthesis. By correlating the dependencies of these evolving objects with the availability of the chemical resources in an aqueous environment, this study proposes guidelines to tune the degree of evolution of electronic materials in water. Such hardware is envisioned to exploit the chemical complexity of real world environments as part of information processing technologies.

physics.chem-ph↗

arXiv2025

Organic Electronic Classifiers for Sensing

This monograph describes nine years of research carried out at the Institute for Electronics, Micro-electronics and Nanotechnologies (IEMN), developed around defining a generic concept for detection, filling a void between metrological sensors and biological senses, sensing an environment's qualities along with their measurable properties in information generation technologies. The first chapter introduces fundamental notions of recognition for complex environments, such as for their chemistry, for which organic semiconductors can embed two new functionalities in consumer electronics. The second and third chapters mostly summarize contributions to the state-of-the-art literature on these matters: in the second chapter, on studying conducting polymers as both chemical detectors and conductimetric transducers, and the third chapter, on studying electropolymerization sensitivity to conceptualize evolutionary electronics. The fourth chapter presents several results on the conception of several "classifiers" exploiting both functionalities: in tasks aiming at integrating different sensitivities at a very small scale, at broadening sensing devices' receptive fields based on experience, and at physically engraving the experience data in a sensing hardware. Along with this monograph are also associated four appendices, summarizing different elements related to the context of this research.

cond-mat.dis-nn↗

arXiv2025

Hardware Implementation of Tunable Fractional-Order Capacitors by Morphogenesis of Conducting Polymer Dendrites

Conventional electronics is founded on a paradigm where shaping perfect electrical elements is done at the fabrication plant, so as to make devices and systems identical, "eternally immutable". In nature, morphogenic evolutions are observed in most living organisms and exploit topological plasticity as a low-resource mechanism for in operando manufacturing and computation. Often fractal, the resulting topologies feature inherent disorder: a property which is never exploited in conventional electronics manufacturing, while necessary for data generation and security in software. In this study, we present how such properties can be exploited to implement long-term and evolvable synaptic plasticity in an electronic hardware. The rich topology of conducting polymer dendrites (CPDs) is exploited to program the non-ideality of their electrochemical capacitances containing constant-phase-elements. Their evolution through structural changes alters the characteristic time constants for them to charge and discharge with the applied voltage stimuli. Under a train of voltage spikes, the evolvable current relaxation of the electrochemical systems promotes short-term plasticity with timescales ranging from milliseconds to seconds. This large window depends on the temporality of the voltage pulses used for reading, but also on the structure of a pair of CPDs on two electrodes, grown by voltage pulses. This study demonstrates how relevant physically transient and non-ideal electrochemical components can be exploited for unconventional electronics, with the aim to mimic a universal property of living organisms which could barely be replicated in a silicon monocrystal.

physics.app-ph↗

arXiv2024

Correlation between Electrochemical Relaxations and Morphologies of Conducting Polymer Dendrites

Conducting Polymer Dendrites (CPD) can engrave sophisticated patterns of electrical interconnects in their morphology with low-voltage spikes and few resources: they may unlock in operando manufacturing functionalities for electronics using metamorphism conjointly with electron transport as part of the information processing. The relationship between structure and information transport remains unclear and hinders the exploitation of the versatility of their morphologies to store and process electrodynamic information. This study details the evolution of CPD's circuit parameters with their growth and shape. Through electrochemical impedance spectroscopy, multiple distributions of relaxation times are evidenced and evolve specifically upon growth. Correlations are established between dispersive capacitances of dendritic morphologies and growth duration, independently from exogenous physical variables: distance, evaporation or aging. Deviation of the anomalous capacitance from the conventional Debye dielectric relaxation can be programmed, as the growth controls the dispersion coefficient of the dendrite's constant-phase elements relaxation. These results suggest that the fading-memory time window of pseudo-capacitive interconnects can practically be conditioned using CPD morphogenesis as an in materio learning mechanism. This study confirms the perspective of using electrochemistry for unconventional electronics, engraving information in the physics of conducting polymer objects, and storing information in their morphology, accessible by impedance spectral analysis.

physics.app-ph↗

arXiv2024

A Compact Electrochemical Model for a Conducting Polymer Dendrite Impedance

Conducting Polymer Dendrites (CPD) are truly inspiring for unconventional electronics that shapes topological circuitries evolving upon an application. Driven by electrochemical processes, an electrochemical impedance rules signal propagation from one node to another. However, clear models dictating their behavior in an electroactive electrolyte have not been identified yet. In this study, we investigate on CPD in an aqueous electrolyte by impedance spectroscopy to unify their signal transport with an electrical model, aiming to define a circuit simulation block to integrate these objects in systems for in materio information processing.

physics.app-ph↗

arXiv2023

Steady vs. Dynamic Contributions of Different Doped Conducting Polymers in the Principal Components of an Electronic Nose's Response

Multivariate data analysis and machine-learning classification become popular tools to extract features without physical models for complex environments recognition. For electronic noses, time sampling over multiple sensors must be a fair compromise between a period sufficiently long to output a meaningful information pattern, and sufficiently short to minimize training time for practical applications. Particularly when reactivity's kinetics differ from thermodynamics' in sensitive materials, finding the best compromise to get the most from data is not obvious. Here, we investigate on the influence of data acquisition to improve or alter data clustering for molecular recognition on a conducting polymer electronic nose. We found out that waiting for the sensors to reach their steady state is not required for classification, and that reducing data acquisition down to the first dynamical information suffice to recognize molecular gases by principal component analysis with the same materials. Particularly for online inference, this study shows that a good sensing array is no array of good sensors, and that new figure-of-merits shall be defined for sensing hardware aiming machine-learning pattern-recognition rather than metrology.

cond-mat.soft↗

arXiv2021

Dendritic organic electrochemical transistors grown by electropolymerization for 3D neuromorphic engineering

One of the major limitation of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, we show how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, we investigate how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. We demonstrate gradual topologies evolution in a multi-electrode configuration. We conduct a detail electrical characterization of the PEDOT:PSS dendrites through DC and impedance spectroscopy measurements and we show how organic electrochemical transistors (OECT) can be realized with these structures. These measurements reveal that quasi-static and transient response of OECTs can be adjust by controlling dendrites' morphologies. The unique properties of organic dendrites are used to demonstrate short-term, long-term and structural plasticity, which are essential features required for future neuromorphic hardware development.

cond-mat.dis-nn↗

arXiv2021

Theoretical modeling of dendrite growth from conductive wire electropolymerization

Electropolymerization is a bottom-up materials engineering process of micro and nano-scale that utilizes electrical signals to deposit conducting dendrites' morphologies by a redox reaction in the liquid phase. It resembles synaptogenesis in the brain, in which electrical stimulation in the brain causes the formation of synapses from the cellular neural composites. The strategy has been recently explored for neuromorphic engineering by establishing link between the electrical signals and the dendrites' shapes. Since the geometry of these structures determines their electrochemical properties, understanding the mechanisms that regulate the polymer assembly under electrically programmed conditions is an important aspect. In this manuscript, we simulate this phenomenon using mesoscale simulations, taking into account the important features of spatial-temporal potential mapping based on the time-varying signal, the motion of charged particles in the liquid due to the electric field, and the attachment of particles on the electrode. The study helps in visualizing the motion of particles in different electrical conditions, which is not possible to probe experimentally. Consistent with the experiments, the higher AC frequency of electrical activities favors linear wire-like growth, while lower frequency leads to more dense and fractal dendrites growth, and voltage offset leads to asymmetrical growth. We find that dendrites' shape and growth process systematically depend on particle concentration and random scattering. We discover that the different dendrites' architectures are associated with different Laplace and diffusion fields, which govern the monomers trajectory and subsequent dendrites' growth. Such unconventional engineering routes could have a variety of applications from neuromorphic engineering to bottom-up computing strategies.

cond-mat.dis-nn↗

arXiv2020

Reservoir computing for sensing: an experimental approach

The increasing popularity of machine learning solutions puts increasing restrictions on this field if it is to penetrate more aspects of life. In particular, energy efficiency and speed of operation is crucial, inter alia in portable medical devices. The Reservoir Computing (RC) paradigm poses as a solution to these issues through foundation of its operation: the reservoir of states. Adequate separation of input information translated into the internal state of the reservoir, whose connections do not need to be trained, allow to simplify the readout layer thus significantly accelerating the operation of the system. In this brief review article, the theoretical basis of RC was first described, followed by a description of its individual variants, their development and state-of-the-art applications in chemical sensing and metrology: detection of impedance changes and ion sensing. Presented results indicate applicability of reservoir computing for sensing and validating the SWEET algorithm experimentally.

cs.ET↗

arXiv2019

The Non-Ideal Organic Electrochemical Transistors Impedance

Organic electrochemical transistors offer powerful functionalities for biosensors and neuroinspired electronics, with still much to understand on the time dependent behavior of this electrochemical device. Here, we report on distributed element modeling of the impedance of such microfabricated device, systematically performed under a large concentration variation for KCl(aq) and CaCl2(aq). We propose a new model which takes into account three main deviations to ideality, that were systematically observed, caused by both the materials and the device complexity, over large frequency range (1 Hz to 1 MHz). More than introducing more freedom degree, the introduction of these non redundant parameters and the study of their behaviors as function of the electrolyte concentration and applied voltage give a more detailed picture of the OECT working principles. This optimized model can be further useful for improving OECT performances in many applications (e.g. biosensors, neuroinspired devices) and circuit simulations.

physics.app-ph↗