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Abstract The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from them. We analyze the space-time complexity of Boolean circuits derived from the electrical responses of colloids-specifically ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and their combinations in response to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata-arrays of binary state machines that update their states synchronously according to the same rule-creating the colloid cellular automata. We then analyze the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

Abstract The increasing use of machine learning, with its significant computational and environmental costs, has motivated the exploration of unconventional computing substrates. Liquid substrates, such as colloids, are of particular interest due to their ability to conform to various shapes while exhibiting complex dynamics resulting from the collective behaviour of the constituent colloidal particles. This study explores the potential of using a PEDOT:PSS colloidal suspension as a physical reservoir for reservoir computing in spoken digit recognition. Reservoir computing uses high-dimensional dynamical systems to perform tasks with different substrates, including physical ones. Here, a physical reservoir is implemented that encodes temporal data by exploiting the rich dynamics inherent in colloidal suspensions, thus avoiding reliance on conventional computing hardware. The reservoir processes audio input encoded as spike sequences, which are then classified using a trained readout layer to identify spoken digits. Evaluation across different speaker scenarios shows that the colloidal reservoir achieves high accuracy in classification tasks, demonstrating its viability as a physical reservoir substrate.

Colloid-based computing devices offer remarkable fault tolerance and adaptability to varying environmental conditions due to their amorphous structure. An intriguing observation is that a colloidal suspension of ZnO nanoparticles in dimethylsulfoxide (DMSO) exhibits reconfiguration when exposed to electrical stimulation and produces spikes of electrical potential in response. This study presents a novel laboratory prototype of a ZnO colloidal computer, showcasing its capability to implement various Boolean functions featuring two, four and eight inputs. During our experiments, we input binary strings into the colloid mixture, where a logical ‘True’ state is represented by an impulse of an electrical potential. In contrast, the absence of the electrical impulse denotes a logical ‘False’ state. The electrical responses of the colloid mixture are recorded, allowing us to extract truth tables from the recordings. Through this methodological approach, we demonstrate the successful implementation of a wide range of logical functions using colloidal mixtures. We provide detailed distributions of the logical functions discovered and offer speculation on the potential impacts of our findings on future and emerging unconventional computing technologies. This research highlights the exciting possibilities of colloid-based computing and paves the way for further advancements.

Abstract On account of their excellent properties, 2D nanostructures beyond graphene, such as MoS2, have extensive applications. Given their quantum confinement effects, MoS2 monolayers could efficiently trap electrons as an intermediate layer between friction and electrode in triboelectric nanogenerators (TENGs) and successfully hinder their recombination and air breakdown, increasing their output. With the help of this phenomenon, a TENG called PS+PS/MoS2‐AHSG (PPMA) TENG is fabricated with an open‐circuit voltage of ≈1200 V and a short‐circuit current of 0.74 mA, and a maximum power of 11.27 mW. PPMA TENG consists of transparent polystyrene (PS) and PS/MoS2 as negative contact and storage layers. Also, the positive layer is a novel Alyssum homolocarpum seed gum (AHSG) layer, which is a natural polymer. This TENG could successfully light up 115 commercial light‐emitting diodes. PPMA TENG exhibits exceptional mechanical robustness so that after a decrease in its outputs, heating would activate the self‐healing mechanism, and the surface charge density could reach from 0.428 to 0.874 µC m−2, which is 82% of the initial value. To demonstrate the practical applications of PPMA TENG as a high‐voltage sustainable power source, it is successfully employed to perform a dielectrophoretic assisted welding of silver nanorods.

We are studying the remarkable electrical properties of Proteinoids-ZnO microspheres with the aim of exploring their potential for a new form of computing. Our research has revealed that these microspheres exhibit behavior similar to neurons, generating electrical spikes that resemble action potentials. Through our investigations, we have studied the underlying mechanism behind this electrical activity and proposed that the spikes arise from oscillations between the degradation and reorganization of proteinoid molecules on the surface of ZnO. These findings offer valuable insights into the potential use of Proteinoids-ZnO colloids in unconventional computing and the development of novel neuromorphic liquid circuits.

Abstract The next generation of wearable electronics for internet of things (IoT) systems, and green energy harvesting require electrically conductive materials with high flexibility, conductivity, and being environmentally friendly. In this study, three biopolymers, cow's milk, soy milk, and egg white liquid, are investigated and compared as spin‐coated positive layers in triboelectric nanogenerators (TENGs). Superior results are obtained using egg white liquid as a novel liquid conductor with comparable conductivity and high transparency. After investigating various disposable polymers as substrates, sandpaper is used to improve the output performance of the proposed egg white liquid based TENG (EW‐TENG). The maximum output power density, voltage, and current of the EW‐TENG are 328.84 mW cm−2, 1720 V, and 16.05 mA, respectively. The fabricated EW‐TENG, with an area of 4 × 4 cm2, can directly illuminate 55 high‐power blue LEDs and can adequately perform an electrophoretic deposition of ZnO nanoparticles on copper layers without microcracks. The potential distribution of the EW‐TENG obtained by COMSOL Multiphysics software is consistent with the experimental results. Herein, an eco‐environmentally friendly, flexible, and lightweight electronic device for energy harvesting and electrophoretic deposition applications is proposed.

In this study, carbon aerogel was prepared from sodium alginate via the sol-gel technique. The morphological study clearly revealed that the synthesized aerogel possessed a highly porous structure with a specific surface area of 470 m2/g. The applicability of the aerogel as an adsorbent was examined in methylene blue removal. Adsorption isotherms, kinetics, and thermodynamic studies of Removal of MB from aqueous solutions were conducted in a batch system. The effect of pH and adsorbent dosage on the adsorption of MB was investigated. The equilibrium data were best fitted to the Langmuir model with a maximum adsorption capacity of 70.42 mg/g. The adsorption kinetics of MB followed the pseudo-first-order models. Thermodynamic analysis of the results indicated that adsorption is a physical process and is in agreement with data obtained from the Dubinin−Radushkevich isotherm model. The removal of the MB process was spontaneous and endothermic.