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  1. Abstract Artificial synaptic devices are the essential hardware component in emerging neuromorphic computing systems by mimicking biological synapse and brain functions. When made from natural organic materials such as protein and carbohydrate, they have potential to improve sustainability and reduce electronic waste by enabling environmentally‐friendly disposal. In this paper, a new natural organic memristor based artificial synaptic device is reported with the memristive film processed by a honey and carbon nanotube (CNT) admixture, that is, honey‐CNT memristor. Optical microscopy, scanning electron microscopy, and micro‐Raman spectroscopy are employed to analyze the morphology and chemical structure of the honey‐CNT film. The device demonstrates analog memristive potentiation and depression, with the mechanism governing these functions explained by the formation and dissolution of conductive paths due to the electrochemical metal filaments which are assisted by CNT clusters and bundles in the honey‐CNT film. The honey‐CNT memristor successfully emulates synaptic functionalities such as short‐term plasticity and its transition to long‐term plasticity for memory rehearsal, spatial summation, and shunting inhibition, and for the first time, the classical conditioning behavior for associative learning by mimicking the Pavlov's dog experiment. All these results testify that honey‐CNT memristor based artificial synaptic device is promising for energy‐efficient and eco‐friendly neuromorphic systems. 
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  2. Free, publicly-accessible full text available July 11, 2026
  3. Free, publicly-accessible full text available July 11, 2026
  4. Free, publicly-accessible full text available December 1, 2025
  5. Nowadays, non-volatile memory technologies have been widely applied in different areas. Of these memory technologies, non-volatile resistive random access memory (ReRAM) is attractive because of its simple device architecture and fabrication process, high scalability and data density, good performances in terms of switching speed, high power efficiency and reasonably wide memory window. In order to address the issues of disposable and degradation of electronic waste by typical ReRAM with the active layer made of inorganic oxide materials and fossil-fuel based polymeric materials, a green and sustainable strategy has been adopted in producing ReRAM by using natural organic-based materials based on protein and carbohydrate, such as honey, fructose, aloe vera, etc. Among these materials, pectin-polysaccharide thin film has demonstrated promising resistive switching characteristics. The two ranges of pectin concentrations that have been investigated are ³5 mg/ml and £1.5 mg/ml, and it showed that pectin with concentration <1.5 mg/ml reveals a higher ON/OFF ratio. However, the resistive switching characteristics with pectin concentration between 1.5 mg/ml and 5 mg/ml have yet been explored and reported. In this work, pectin with concentrations of 1.5~5 mg/ml were prepared from pectin-polysaccharide solution into the active switching layer, and ReRAM devices with such pectin resistive switching layer were fabricated. The pectin-polysaccharide solution, pectin resistive film, and ReRAM devices were systematically investigated. Surface tension and contact angle of pectin-polysaccharide precursor solutions as a function of pectin concentration on the substrate were measured by a goniometer. Surface topography of solidified thin films was characterized by an atomic force microscope (AFM) and a field-emission scanning electron microscope (FE-SEM). Chemical functional groups of the pectin-polysaccharide precursor solutions and solidified thin films were examined by a Fourier transform infrared (FTIR) spectroscopy. The resistive switching behaviors were characterized and compared by electrical measurement. The results show that 4 mg/ml recorded the highest ON/OFF ratio compared to ever reported values, as well as desirable memory window, non-volatility in retention, and stability over 100 cycles. This study proves that pectin-polysaccharide is a promising green and sustainable bio-organic material for non-volatile ReRAM for electronic applications such as in emerging neuromorphic computing systems. 
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    Free, publicly-accessible full text available November 22, 2025
  6. Free, publicly-accessible full text available November 1, 2025
  7. Neuromorphic computing is considered to have the potential to overcome the limitations of traditional von Neumann architecture due to its high efficiency, low energy consumption, and fault-tolerance. Hardware components that can emulate the synaptic plasticity of neurons, i.e. artificial synaptic devices, are required by neuromorphic systems. New devices have been examined for such components, such as phase-change artificial synapse, ferroelectric artificial synapse, and memristor synapses. Among them, memristor, a two-terminal metal-insulator-metal structure that are analogous to a biological synapse with presynaptic neuron (top electrode), postsynapticneuron (bottom electrode), and synaptic cleft (memristive film), is a promising device technology because of its tunable resistance, scalability, 3D integration compatibility, low power consumption, and relatively high speed. In contrary to inorganic materials such as metal oxides, natural organic materials have attracted interest to form the memristive layer because they are renewable, biodegradable, sustainable, biocompatible, and environmentally friendly. In this paper, honey solution embedded with carbon nanotubes (CNTs) was processed into the memristive layer by a low cost solution-based process, with synaptic plasticity of the final honey-CNT memristors characterized, including forget and relearn, spike-rate-dependent plasticity, spike-voltage-dependent plasticity, short-term to long-term memory transition, paired pulse facilitation, and spatial supra-linear summation behaviors. The successful emulation of these essential biological synaptic behaviors demonstrates the potential of honey-CNT memristors as a viable hardware component in neuromorphic computing systems. 
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