Flexible Artificial Synapses with a Biocompatible Maltose-Ascorbic Acid Electrolyte Gate for Neuromorphic Computing

Biocompatible Neuromorphic Device Array Based on Naturally Sourced Mucin for Implantable Bioelectronics

Although the demand for intelligent implantable bioelectronics is steadily increasing, their progress is hindered by the limited availability of materials with sufficient biocompatibility for implantation. Herein, we propose a neuromorphic device with human brain-inspired biomimetic functionality utilizing naturally sourced mucin as the active layer material. The mucin-based neuromorphic memristor (MNM) array successfully mimics key synaptic behaviors uniformly, including a paired-pulse facilitation index of 122.65%, transition from short-term to long-term memory, long-term potentiation, and long-term depression. In addition to the effect of the defect-rich mucin active layer, these behaviors are enhanced by the presence of a MgOx interfacial layer formed at its interface with the Mg top electrode. The cell cytotoxicity test results demonstrate the superior biocompatibility of the MNM array, which shows a relative cell viability of 108.46% after 72 h of cell culture. Moreover, the artificial neural network simulation demonstrates a recognition rate of 89.93% after 125 training epochs, which suggests that naturally sourced materials, including mucin, can be used in implantable bioelectronics for advanced medical healthcare applications.

Advances in 2D Molybdenum Disulfide Transistors for Flexible and Wearable Electronics

As the trajectory of developing advanced electronics is shifting towards wearable electronics, various methods for implementing flexible and bendable devices capable of conforming to curvilinear surfaces have been widely investigated. In particular, achieving high-performance and stable flexible transistors remains a significant technical challenge, as transistors are fundamental components of electronics, playing a key role in overall performance. Among the wide range of candidates for flexible transistors, two-dimensional (2D) molybdenum disulfide (MoS2)-based transistors have emerged as potential solutions to address these challenges. Unlike other 2D materials, the 2D MoS2 offers numerous advantages, such as high carrier mobility, a tunable bandgap, superior mechanical strength, and exceptional chemical stability. This review emphasizes the novel techniques of the fabrication process, structure, and material to achieve flexible MoS2 transistor-based applications. Furthermore, the distinctive feature of this review is its focus on studies published in high-impact journals over the past decade, emphasizing their methods for developing MoS2 transistors into various applications. Finally, the review addresses technical challenges and provides an outlook for flexible and wearable MoS2 transistors.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

Toward Intelligent Display with Neuromorphic Technology

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing

Materials science research has garnered extensive attention from industry, society, policy, and academia. However, understanding the research landscape and extracting strategic insights are challenging due to the increasing diversity and volume of publications. This study proposes a natural language processing-based protocol for extracting text-encoded topics from a large volume of scientific literature, uncovering research interests of scientific communities, as well as convergence trends. We report a topic map, representing the materials science research landscape with text-mined 257 topics regarding biocompatible materials, structural materials, electrochemistry, or photonics. We analyze the topic map in terms of national research interests in materials science, revealing competitive positions and strategies of active nations. For example, it is found that the increasing trend of research interest in machine learning topic was captured in the United States earlier than other nations. Similarly, our journal-level analyses serve as reference information for journal recommendations and trend guidance, showing that the main topics and research interests of materials science journals slightly changed over time. Moreover, we build the topic association network which can highlight the status and future potential of interdisciplinary research, revealing research fields with high centrality in the network such as machine learning-enabled composite modeling, energy policy, or wearable electronics. This study offers insightful results on current and near-future materials science research landscapes, facilitating the understanding of stakeholders, amidst the fast-evolving and diverse knowledge of materials science.

Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems

Natural organic materials such as protein and carbohydrates are abundant in nature, renewable, and biodegradable, desirable for the construction of artificial synaptic devices for emerging neuromorphic computing systems with energy efficient operation and environmentally friendly disposal. These artificial synaptic devices are based on memristors or transistors with the memristive layer or gate dielectric formed by natural organic materials. The fundamental requirement for these synaptic devices is the ability to mimic the memory and learning behaviors of biological synapses. This paper reviews the synaptic functions emulated by a variety of artificial synaptic devices based on natural organic materials and provides a useful guidance for testing and investigating more of such devices.

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

In Situ Synthesis of Copper Nanoparticles on Dielectric Barrier Discharge Plasma-Treated Polyester Fabrics at Different Reaction pHs

Polyester (PET) fabrics are widely applied in functional textiles due to their outstanding properties such as high strength, dimensional stability, high melting point, low cost, recyclability, and flexibility. Nevertheless, the lack of polar groups in the PET structure makes its coloration and functionalization difficult. The present work reports the one-step in situ synthesis of copper nanoparticles (CuNPs) onto the PET fabric employing sodium hypophosphate and ascorbic acid as reducing and stabilizing agents, at acidic (pH 2) and alkaline pH (pH 11). This synthesis (i) used safer reagents when compared with traditional chemicals for CuNP production, (ii) was performed at a moderate temperature (85 °C), and (iii) used no protective inert gas. The dielectric barrier discharge (DBD) plasma was used as an environmentally friendly method for the surface functionalization of PET to enhance the adhesion of CuNPs. The size of the CuNPs in an alkaline reaction (76-156 nm for not treated and 93.4-123 nm for DBD plasma-treated samples) was found to be smaller than their size in acidic media (118-310 nm for not treated and 249-500 nm for DBD plasma-treated samples), where the DBD plasma treatment promoted some agglomeration. In acidic medium, metallic copper was obtained, and a reddish color became noticeable in the textile. In alkaline medium, copper(I) oxide (Cu2O) was detected, and the PET samples exhibited a yellow color. The PET samples with CuNPs presented improved ultraviolet protection factor values. Finally, a minimal concentration of copper salt was studied to obtain the optimized antibacterial effect against Staphylococcus aureus and Escherichia coli. The functionalized samples showed strong antibacterial efficacy using low-concentration solutions in the in situ synthesis (2.0 mM of copper salt) and even after five washing cycles. The DBD plasma treatment improved the antibacterial action of the samples prepared in the alkaline medium.

Surface Properties of Alkyldi(oxyethylene) β-D-Maltoside

A series of nonionic disaccharide-based surfactants alkyldi(oxyethylene) β-d-maltosides (4a-4h, n = 6-16) were synthesized, and their physicochemical properties were further investigated. Six β-D-maltosides (4c-4h, n = 8-16) exhibited a fan-shaped texture feature, whereas hexyldi(oxyethylene) β-D-maltoside (4a) had the strongest hygroscopicity. Owing to the incorporation of the hydrophilic dioxyethyl spacer (-(OCH₂CH₂)₂-), the related water solubility improved significantly. Tetradecyldi(oxyethylene) β-D-maltoside (4g) had good water solubility, whereas hexadecyldi(oxyethylene) β-D-maltoside (4h) had weak water solubility. Meanwhile, the surface tension of β-D-maltosides (4a-4g, n = 6-14) had a decreasing tendency with increasing the alkyl chain length, whereas 4g had the best surface activity. Furthermore, decyldi(oxyethylene) β-D-maltoside (4e) had the best foaming ability and foam stability. Dodecyldi(oxyethylene) β-D-maltoside (4f) had the best emulsifying property in the rapeseed oil/water system. In contrast, both ammonium dodecyl sulfate (NH₄DS)/4f and cetyltrimethylammonium chloride (CTAC)/4f binary surfactant systems showed a synergistic effect in surface activity because the C_CMC/CMC^id_mix was <1. NaCl impacted the surface activity of the aqueous 4f solution through salt-surfactant synergistic effects. The results showed that such surfactants should have potential applications in the related field in the future.