Abstract: Conducting Polymer Dendrites (CPD) can engrave sophisticated patterns of electrical interconnects in their morphology, networking input with output nodes, from low-voltage spikes and with very minimal amounts of resources: they may unlock in operando man ufacturing functionalities for an electronics framework using metamorphism conjointly with electron transport as part of the information processing. The relationship between their structure and the information transport is still however very 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. By the means of electrochemical impedance spectro scopy (EIS), multiple distributions of relaxation times (DRT) are evidenced and evolve specifically upon growth. Correlations are established between the dispersive capacitance of dendritic morphologies and their growth duration, independently from exoge nous physical variables, such as distance, multi-component evaporation or aging. Deviation of the anomalous capacitance from the conventional Debye dielectric relaxation can be programmed within the morphology, as the growth controls the dispersion coeff icient 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 electrogenerated CPD morphogenesis as an in materio learning mec hanism. This study confirms the perspective of using electrochemistry for unconventional electronics, engraving information with low voltage events in the physics of conducting polymer objects, and storing information in their morphology, accessible by i mpedance spectral analysis.

Abstract: Gas detection technologies are essential tools in maintaining safety and environmental standards across various applications. Through advanced sensors and analytical techniques, these systems aim to quickly detect and classify molecular content in an env ironment, providing valuable insights for early warning and effective response to incidents. In this work, we present the development of miniaturized, multiplexed, and connected electronic nose (e-nose) based on impedance spectroscopy technology. Our pla tform has been tested and optimized to process electrical responses of 15 conductimetric cells, each cell is tuned using a drop-casted conducting polymer poly(3-hexylthiophene) and 14 different triflate salts. The recognition of various solvents vapor (a cetone, methanol, isopropanol, water, ethanol and blends of the last two at various concentrations) relies on a Deep-Convolutional Neural Network based on a back propagation algorithm with two hidden layers of 64 and 32 neurons respectively. The achieved experimental results show an effective classification for the e-nose data to discriminate the alcoholic blends by type and composition, with high classification accuracy (~96%).

Abstract: Advances on System-On-Chip and organic sensors allows the development of miniaturized impedance measurement hardware for gas monitoring in IoT. In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance sp ectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. Our designed circuit is built from low cost and low power consumption microelectronics components providing real time acquisition. The proposed system is based on ESP32 Microcontroller enabling the management of an impedance network analyzer AD5933 (Analog Devices, Norwood, MA, USA) through its I2C interface. Our system benefits from two multiplexer components allowing calibration process and the interface of 15 conductimetric sensors with fast acquisition (less than 90 ms per acquisition). The paper describes the microelectronics design, the impedance respon se over time, the measurement's sensitivity and accuracy and the testing of the platform with embedded chemical sensors for gas classification and recognition.

Abstract: Contributions of organic semiconducting materials to electronics are particularly hard to assess: As macromolecular organizations, they have low enthalpy so they can be processed in soft conditions and they have resilience to deformation. However, for th e same reason, they have also broader density of energy states and more instabilities than silicon in ambient. Controlling matter's order at low scale and its properties for as long as possible were always golden standards for microelectronics. Neverthel ess, in a time where brain functioning rises even more as a source of inspiration, shall it still be so? Here are presented clues on how physical property dispersions may be relevant features for information generator nodes to recognize patterns. In a co ntext where the information to recognize is not trivial to physically define, no model can rule sensors' classification a priori. Despite this, broadening the conducting polymer temporal responses in a sensing array allows recognizing dynamical voltage p atterns, or broadening conducting polymer's chemistry in a sensing array enlarges a classifier's perception field to recognize solvent vapors in air. By the nature of property dispersions in regards to the information to recognize, physical variabilities (structural and chemical) can be assets to exploit for pattern recognition and not necessarily drawbacks to bypass for hardware manufacturing. The brain architecture is also transient: a part of the processed information is engraved in its topology, sho wing that a hardware classifier can make use of physical instabilities as part of its programing, by forming new connections in a nodal architecture. Some evidences are also presented here, on how dendritic morphogenesis of a conducting polymer can be a mean to store past voltage experiences in the impedance between nodes in a topology. Very distinct electrochemical features appear in the readout impedance information after growth and these features are to be associated with the shape of a voltage wave inputted on the junction. By the physical implementation of materials' disorder and transience in electronics devices, it is expected that organic semiconductors will integrate essential ingredients in future-emerging information generator nodes beyond s ensors: from embedded random information generating resources to evolving abilities in information classification architectures.

Abstract: Advances on System-On-Chip and organic sensors allows the development of miniaturized impedance measurement hardware for gas monitoring in IoT. In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance sp ectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. Our designed circuit is built from low cost and low power consumption (250 mW) microelectronics components that achieve long duration operability (5 days and 16 HRS) without compromising on sensor measurement accuracy and precision. We used the well-known impedance network analyzer AD5933 (Analog Devices, Norw ood, MA, USA) chip which can measure a spectrum of impedances in the range 5 Hz to 100 kHz. The proposed system is based on ESP32-C3 Microcontroller enabling the management of the AD5933 through its I2C interface. Our system benefits from two multiplexer components CD74HC4067 allowing calibration process and the interface of 15 conductimetric sensors with real time acquisition (less than 90 ms per acquisition). The system is capable of relaying information through the network for data analysis and stora ge. The paper describes the microelectronics design, the impedance response over time, the measurement's sensitivity and accuracy and the testing of the platform with embedded chemical sensors for gas classification and recognition.

Abstract: Unlike living organisms, electronics is not metamorphic: devices are mass-produced in series, normalized with well-defined standards, aim at being highly stable during operation and are rebutted with poor recyclability when a better version of themselves can be deployed. Software can also be upgraded on the same hardware with no waste, but not the hardware. In nature, systems grow, evolve, copy, adapt to the environment they sense, scavenge only the least natural resources they need to operate and degra de to biomass. Very often, their degree of intelligence is associated to their plasticity and ability to adapt to an environment, proliferating on a substrate in the harshest conditions.-Today, electrochemistry can be a building block for a field of elec tronics which aims at mimicking natural intelligence by growing electro-conductive topologies as a learning mechanism. Specifically, electropolymerization allows on demand neurogenesis of sensitive inputs with chemospecific semiconductors, and morphogene sis of conducting polymer dendritic topologies. As bottom-up strategy, it is a mask-less patterning technique for microelectrodes and devices with high control of thickness/roughness. Like additive-manufacturing, it consumes only what it needs of the mat erial resources available in an electroactive electrolyte environment, to manufacture semiconductors at various doping levels and bandgaps with no precious mineral and ore, and with low energy consumption manufactured in ambient. Fab-less, dynamic electr opolymerization can be used as in operando mechanism to program the generation of sensing elements, the routing of array of devices, in a way brain cells, micellia or roots grow topologies on specific landscapes to achieve their purpose. In an era where new computing paradigms are needed, with manufacturing methods adopting the most care for the environment, electropolymerization inspires from nature to propose new ways hardware can form and embed new learning functionalities by physically evolving.

Abstract: CLD (Chlordecone) is a persistent organic pollutant (POP) of great concerns due toxicity and environmental persistence. To this end, we study organic electrochemical transistors (OECT) featuring biological probes to detect CLD. Here, we report a strategy for immobilizing a D09 VHH receptor, which detects CLD-biot in water. WCA (Water Contact Angle), AFM, XPS and FTIR confirm the successful immobilization of D09 on a surface operating as gate electrode sensitive to the presence of CLD-biot in the transfe r characteristic.

Abstract: In the quest to change the way we envision computing, the ability to create connections in between computing nodes on demand offers the very exciting possibility to explore bottom-up strategies when it comes to thinking the design of electronic devices a nd circuits. Recently, the growth of conductive polymer fibers via electropolymerization (most notably poly(3,4-ethylenedioxythiophene) doped with poly(styrenesulfonate), abbreviated as PEDOT:PSS) was employed in the realm of neuromorphic hardware, first as a way to tune the resistance of a connection, and then to perform computing tasks, such as biosignal classification trough reservoir computing, thus establishing the advantages of the method.In this work, we propose to demonstrate that electropolymer ization is a useful tool for the creation of sensors that are able to realize computing tasks in an aqueous environment. By taking advantage of the morphology of the fibers, we show that PEDOT:PSS dendrites can discriminate between different types of vol tage pulses emitted in their vicinity by a local gate electrode, thus performing in materio classification.In addition, we discuss the growth of networks of polymer fibers on 2D substrates. The ability to create structures that cover several microelectro des allows us to study the behavior of the whole system instead of a single dendrite, and it highlights the relationship that relates the morphology of the object and its electrical properties. In particular, we show that dendrites present an asymmetric non-linear behavior due to the volume of polymer that increases the capacitance of the device albeit not participating in conduction. Moreover, because of the ionoelectronic coupling that exists within an electrolyte, two active devices working concomita ntly will influence one another. We demonstrate that it is a blessing for the realization of computing tasks, such as logic, and that it can also be used to program the dendrites into non-volatile conductance states. Therefore, dendritic networks could c onstitute a new building block for non-conventional information processing that fits into the larger framework of brain-inspired computing.

Abstract: Neuromorphic computing and engineering is capitalizing heavily on the new physical properties offered by nantechnologies to engineer biological processes. At the frontiers in between bio-mimetism and bio-inspiration, various solutions have been proposed for synaptic plasticity or neuronal features based on discrete memory elements, bistable switches or transistors circuits. One missing element that has been missing in the neuromorphic toolbox is the ability to reproduce the complex 3D interconnections o bserved in biological neural networks. Here, we propose to take advantage of bipolar electropolymerization of PEDOT dendritic fibers to reproduce the ability of neural networks to generate complex topologies. The electropolymerization mechanism is used t o realize structural plasticity based on Hebbian-like plasticity rules. We explore how such bottom-up process can find optimal topologies for specific computing tasks. We demonstrate that such optimal topologies results in a drastic reduction of intercon nects for classification and reconstruction tasks, thus offering an interesting option for neural network design.

Abstract: In electronics, circuits are predefined for a lifetime. Fabricating always the same with highly stable components is a real advantage for mass production. However when technologies are no longer up to date, electronic devices can only be discarded, poorl y recycled, because they cannot adapt. Living organisms, on the other hand, are constantly evolving. They learn and interact with their environment, and when doing so, brain-neurons or tree-roots grow with appearent disorder but a surprising way to learn to exploit local ressources. The way they branch out can be described as morphogenesis and is the symbol of a natural intelligence, too complex to modelize. While conventional electronics seeks to eliminate disorder and variability, we hypothesize that it is possible to make use of it in a novel electronics that uses electrochemistry to mimic biological processes for adaptation. In this study, we will discuss on morphogenesis of a conducting polymer (PEDOT:PSS) through the AC electropolymerization of E DOT in water. The dendritic objects exhibit various morphologies, differing in their thicknesses and number of branches. Their growth mechanism involves diffusion and electromigration of charged species within the solution. We also present the first resu lts characterizing a connection of those objects in the frequency domain, where various dynamics can be observed due to specific mechanisms at the different interfaces. The electropolymerization of EDOT offers an inexpensive way to grow directed connecti ons with a specific impedance to connect components in a system by voltage activation. It could be used to address the limits of the current electronics in terms of cost and flexibility while taking a form that is closer to what can be found in nature.