Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions.

Ion transport through nanoscale channels enables advanced functionalities, such as ionic current rectification (ICR), with promising applications in neuromorphic computing and biomimetic signal processing. However, the fundamental mechanisms controlling the ion dynamics under nanoconfinement remain poorly understood. Using atomistic molecular dynamics simulations and free energy calculations, we demonstrate that multilayered Janus graphene oxide nanopores exhibit exceptional and tunable ICR performance mediated by interlayer coupling. These structures achieve a rectification ratio enhancement of up to 2 orders of magnitude─from ∼2 in a single layer to over 2000 at 3.5 V/nm in multilayered configurations─and a shift of the peak rectification field from 0.7 to 3.5 V/nm with increasing layer number. Ion distribution analyses reveal distinctive ionic enrichment-depletion behavior unique to multilayered architectures. Thermodynamically, we unveil that synergistic interlayer coupling fundamentally reshapes the free energy landscape, creating a highly asymmetric profile with multiple energy barriers and wells due to entropy─enthalpy competition. Importantly, entropy is identified to play a critical role in stabilizing energy wells and facilitating directional ion transport─a mechanism absent in single-layer systems. These insights provide a mechanistic basis for ion rectification and establish design principles, such as interlayer spacing or number control, for developing high-performance ionic membranes and nanofluidic devices.

Human brain is capable of optimizing information flow and processing without energy-intensive data shuttling between processor and memory. At the core of this unique capability are billions of neurons connected through trillions of synapses—basic processing units of the brain. The action potentials or “spikes” based temporal processing using the regulated flow of ions across ion channels in neuron cells allows sparse and efficient transmission of data in the brain. Emerging systems based on confined fluidic systems have provided a framework for a new type of neuromorphic computing with lower energy consumption, hardware-level plasticity, and multiple information carriers that emulate natural processes and mechanisms of human brain. These systems mimic neuronal architectures by harnessing and modulating ion transport along artificial channels. The spikes-induced ion-to-surface interactions within these fluidic systems enables the control of ionic conductivity to achieve synaptic plasticity for the realization of brain-inspired functionalities such as memory effect and signal transmission. Herein, this review provides an overview of recent advances in fluidic devices such as memristors and other computing components, covering their basic operations, materials and architectures, as well as applications in neuromorphic computing. The review concludes with a brief outline of the challenges that these emerging technologies face and an outlook for the development of fluidic-based brain-inspired computing.

An In2O3 synaptic transistor based on lithium doped Gd2O3 (LiGdO) solid electrolyte was fabricated by a solution process, its basic synaptic functions were realized and its application in neuromorphic computing was demonstrated.

Neuromorphic computing has gained a significant amount of attention from industry as well as the research community as a means of overcoming the growing energy demands of machine intelligence. Neuromorphic systems offer promising computation approaches by mimicking the functioning of the human brain’s energy-efficient neural processing. This perspective explores the significance of energy efficiency in artificial intelligence (AI) systems, examines the potential issues associated with traditional AI architectures, and highlights the importance of neuromorphic computing as a sustainable solution. It also provides an overview of the working principle of neuromorphic systems, a spiking neural network (SNN) implementation, and the comparative advantages over conventional computing architectures. This perspective further investigates several real-world applications to assess the potential of neuromorphic computing to perform real-time and energy-efficient operations. Although they offer significant advantages, neuromorphic systems face multiple challenges regarding materials development, fabrication techniques, large-scale deployments, and a lack of standard software tools. Finally, this perspective outlines potential research directions to explore the role of neuromorphic computing in next-generation AI systems.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTFundamental Studies of Nanofluidics: Nanopores, Nanochannels, and NanopipetsDaniel G. Haywood, Anumita Saha-Shah, Lane A. Baker*, and Stephen C. Jacobson*View Author Information Department of Chemistry, Indiana University, Bloomington, Indiana 47405-7102, United States*E-mail: [email protected]*E-mail: [email protected]Cite this: Anal. Chem. 2015, 87, 1, 172–187Publication Date (Web):November 18, 2014Publication History Published online3 December 2014Published inissue 6 January 2015https://doi.org/10.1021/ac504180hCopyright © 2014 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views10349Altmetric-Citations193LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (6 MB) Get e-AlertsSUBJECTS:Ions,Nanochannels,Nanofluidics,Nanoparticles,Nanopores Get e-Alerts

Nanofluidic ion transport holds high promise in bio‐sensing and energy conversion applications. However, smart nanofluidic devices with high ion flux and modulable ion transport capabilities remain to be realised. Herein, we demonstrate smart nanofluidic devices based on oriented two‐dimensional covalent organic framework (2D COF) membranes with vertically aligned nanochannel arrays that achieved a 2–3 orders of magnitude higher ion flux compared with that of conventional single‐channel nanofluidic devices. The surface‐charge‐governed ion conductance is dominant for electrolyte concentration up to 0.01 M. Moreover, owing to the customisable pH‐responsivity of imine and phenol hydroxyl groups, the COF‐DT membranes attained an actively modulable ion transport with a high pH‐gating on/off ratio of ≈100. The customisable structure and rich chemistry of COF materials will offer a promising platform for manufacturing nanofluidic devices with modifiable ion/molecular transport features.

Synaptic devices based neuromorphic computing applications in artificial intelligence

This study evaluates the impact of Artificial Intelligence (AI) applications in power systems on energy security and to determine relevant policy implications. We use a mixed methods approach to analyze the benefits and risks associated with AI implementation on the European power grid, focusing on four key dimensions of energy security: availability, affordability, accessibility, and acceptability. We investigated the benefits of AI using PyPSA, a Python-based model of the European electricity system. Three AI applications were parametrized: load reduction, load shifting, and wind wake steering. We compared scenarios in which these AI applications are widely deployed against a baseline scenario without these applications to determine if AI improves energy security. The study also analyzes risks associated with AI deployment in the power grid. We developed a risk taxonomy centered around six high-level categories: cybersecurity, jurisdictional or sovereignty issues, unexplained or unexpected actions by the model, unethical or illegal decision-making, reliance and trust in decision-making, and supplier dependency and vendor lock-in. Additionally, we conducted a back-casting exercise with subject-matter experts to determine positive and negative future outcomes of AI deployment and identify actions to create positive outcomes and avoid negative ones. The paper presents a set of policy implications for AI on the European synchronous grid. We find that AI applications can improve energy security in power systems. In the scenarios we tested, behind-the-meter applications have a greater impact on energy security than front-of-meter applications. The results suggest that AI applications targeting energy consumption may significantly improve energy security metrics. Keywords: Energy Security, AI, Power Grid, European Electricity System

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

Ion transport in nanopores is crucial for various biological and technological processes, exhibiting unique behaviors compared to bulk solutions. In this study, we systematically explore how polyethylene glycol (PEG) modulates ion transport within a conical nanopore. Our experiments reveal that introducing PEG into the ionic solution induces a reversal in ion current rectification (ICR). We further investigate the impact of PEG concentration, molecular weight, nanopore size, and cation type on ion transport. Additionally, we assess three different configurations of PEG introduction, identifying diffusive flow driven by an asymmetric cation distribution within the nanopore as a dominant transport mechanism. Our results confirm that the interactions between PEG and cations significantly affect ion transport properties. These findings advance our understanding of macromolecular crowding effects on ion transport and suggest potential applications in iontronic devices and biomolecule sensing.

Artificial synaptic simulating pain-perceptual nociceptor and brain-inspired computing based on Au/Bi3.2La0.8Ti3O12/ITO memristor

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

Nanofluidic devices have been widely used for diode-like ion transport and salinity gradients energy conversion. Emerging reverse electrodialysis (RED) nanofluidic systems based on nanochannel membrane display great superiority in salinity gradient energy harvesting. However, the imbalance between permeability and selectivity limits their practical application. Here, a new mesoporous carbon-silica/anodized aluminum (MCS/AAO) nanofluidic device with enhanced permselectivity for temperature- and pH-regulated energy generation was obtained by interfacial super-assembly method. A maximum power density of 5.04 W m-2 is achieved, and a higher performance can be obtained by regulating temperature and pH. Theoretical calculations are further implemented to reveal the mechanism for ion rectification, ion selectivity and energy conversion. Results show that the MCS/AAO hybrid membrane has great superiority in diode-like ion transport, temperature- and pH-regulated salinity gradient energy conversion.

The integration of quantum theory with biological modeling has generated new possibilities for understanding neural network dynamics in complex organisms. Traditional computational neuroscience frameworks rely on classical assumptions of synaptic transmission, electrochemical signaling, and network plasticity. However, these models often struggle to account for nonlinear, emergent, and context-dependent behaviors observed in biological cognition. This study investigates how quantum bio-systems modeling offers an expanded theoretical foundation for explaining neural processes by incorporating principles such as superposition, entanglement, and probability amplitude interference. Through qualitative synthesis of literature in quantum biology, cognitive neuroscience, and computational modeling, the research explores how quantum-level interactions—particularly within microtubules, ion channels, and molecular signaling networks—may influence large-scale neural dynamics. The analysis further examines how quantum-inspired mathematical structures can capture complex patterns such as rapid state transitions, adaptive learning, and multi-scale interactions in neural circuits. The findings suggest that quantum bio-systems modeling not only enhances theoretical understanding of cognition but also holds promise for applications in neuromorphic computing, mental health diagnostics, and bio-inspired artificial intelligence. Ultimately, the study positions quantum-biological frameworks as an emerging frontier for advancing our knowledge of neural complexity, bridging molecular processes with organism-level behavior.

In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.