Gisya Abdi

Thickness-dependent interface engineering provides a scalable strategy for tuning the electronic properties of oxide thin films without introducing aliovalent dopants. In this work, sputtered CuO/α-Fe₂O₃ bilayer thin films were fabricated with a fixed 30 nm CuO top layer and systematically varied Fe₂O₃ underlayer thickness (8–30 nm). Structural and chemical characterization by SEM, HRTEM, grazing-incidence XRD, XPS depth profiling and EDX confirmed well-defined heterointerfaces and stoichiometric oxide phases. Electrical transport measurements and gas sensing tests reveal a pronounced non-monotonic dependence of the resistance response on Fe₂O₃ thickness. An optimum underlayer thickness of 13 nm enables detection of 0.8 ppm acetone at 50% RH and 375 °C. The results are consistent with interface-induced electronic sensitization associated with depletion region modulation at the CuO/Fe₂O₃ p–n junction. The study demonstrates that underlayer thickness serves as an effective control parameter for tailoring surface-controlled transport in oxide heterojunction thin films.

Heteroatom engineering in hybrid chlorocuprate memristors tunes interfacial hybridisation and trap-state density, enabling stable Schottky-barrier-driven resistive switching, neuromorphic behaviour, and stochastic hardware random number generation.

Silver( i ) coordination systems based on cyanothiazole ligands provide a versatile platform for tuning structure–property relationships in hybrid semiconducting materials.

Integration of bio-sourced nicotine into organic–inorganic hybrid materials in memristive devices as artificial synapses, emphasizing their potential to bridge biological systems with next-generation computing technologies.

Memristors are promising candidates for neuromorphic systems due to their ability to mimic synaptic behaviour, which is a key feature for the combination of memory and information processing in a single device. In this study, we report the synthesis and characterisation of a series of phenanthroline‐based iodobismuthate(III) complexes with varying cations, such as tetrabutylammonium, tetramethylammonium, and trimethylsulfonium. We have investigated the influence of steric effects on the optical and electrical properties of the compounds. Sandwiched thin layers as memristive devices show analogue, voltage‐dependent potentiation and depression, supporting their synaptic functionality. Along with the decrease in the cation size, the homogeneity of the thin layers on the ITO/glasses and stability of the device were improved. It is noteworthy that devices implemented as a time‐delayed reservoir in the circuit are capable of raw signal classification, successfully classifying environmental signals without any pre‐ or post‐processing steps. These findings advance the design of functional materials for real‐time hardware‐level signal classification, marking a step towards integrated neuromorphic architectures.

This study explores 7‐methylquinolinium halobismuthates (I, Br, and Cl) with a focus on: (1) the impact of halide composition on their structural and semiconducting properties, and (2) their memristive behavior and plasticity for neuromorphic and reservoir computing. Crystallographic analysis reveals that halide substitution leads to the formation of low‐dimensional bismuth‐based frameworks. Optical bandgaps, measured via diffuse reflectance spectroscopy, correlate well with density functional theory calculations. Due to solubility constraints, only bismuth iodide complexes are integrated into electronic devices. Current–voltage measurements reveal pinched hysteresis loops, indicative of memristive behavior. Conductivity versus temperature analysis suggests both ionic and electronic conduction pathways. Given their ability to function as synaptic analogs, further tests on transient conductance (potentiation–depression) and spike‐time‐dependent plasticity are performed. The observed nonlinearity and fading memory characteristics highlight their potential for physical reservoir computing. To evaluate system‐level behavior, a device with 16 gold electrodes (1 input, 15 outputs) is fabricated on a silicon substrate coated with the target compound. The device is assessed through benchmark tasks including waveform generation, NARMA‐2, memory capacity, and noise response under DC/AC signals. Finally, the system demonstrates 82.26% accuracy in MNIST digit classification and 82% accuracy in spoken digit “2” recognition across six different speakers.

We present a hippocampus-inspired neuromorphic system based on a stochastic poly-crystalline nano-fibre mesh. This system takes advantage of the inherent randomness of the material structure to create a dynamically evolving connectivity pattern that mimics the probabilistic nature of biological synaptic networks. The memristive architecture is based on polycrystaline memristive materials derived from acetylsalycilic acid (aspirin). Three different compounds were obtained by adding axial ligands to [Cu2(asp)4]: benzimidazole (bimi), pyridine (py), and 1,4-diazabicyclo[2.2.2]octane (DABCO). The results showed that the memristive properties of devices based on copper complexes can be modulated with various axial ligands. Moreover, theoretical analysis predicts the possibility of mimicking hippocampal architecture via cocrystallization of hydrophobic organic molecular semiconductors with selected copper complexes. Additionally, we demonstrated that controlling crystallization temperature and solvent composition allows precise tuning of resistive switching in methylammonium lead iodide perovskites. These approaches together provide a versatile foundation for implementing dynamic, brain-like memory functions in hardware.

This study investigates 7-methylquinolinium halobismuthates (I, Br, and Cl) in two aspects: (1) their structural and semiconducting properties influenced by anionic composition, and (2) their memristive and plasticity characteristics for neuromorphic and reservoir computing applications. Structural changes induced by halides form low-dimensional halobismuthate fragments, confirmed by crystallographic analysis. Optical band gaps were studied using diffuse reflectance spectroscopy, aligning with density functional theory results. Due to solubility limitations, only bismuth iodide complexes were explored in electronic devices. Current-voltage scans showed pinched hysteresis loops, characteristic of memristors. Conductivity versus temperature study indicates combined ionic and electronic contributions to conductivity of the devices. Given that a memristor can function as a single synapse without the need for programming, aligning with the requirements of neuromorphic computing, the study investigated long-term depression, potentiation, and spike-time-dependent plasticity. As the potentiation-depression plots showed non-linearity with fading memory, these materials can be a good candidate for application in physical reservoir computing. To further assess this material, an electronic device with sixteen gold electrodes was applied, featuring one input and 15 output electrodes deposited on silicon substrate and covered with a layer of studied compound. Basic test to assess the complexity and non-linearity of the devices were conducted through a series of benchmark tasks, including waveform generation, NARMA-2, memory capacity assessment, and noise study under both DC and AC current. The ability of device in MNIST digit classification with 82.26% accuracy and voice classification for digit 2 for six different people with 82 % accuracy has been demonstrated.

Liquid ferrofluid devices emerging as favorable synapses for spiking neuromorphic learning systems.