A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate

Highly-integrable analogue reservoir circuits based on a simple cycle architecture

Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.

Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating

Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.

Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity

Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed via the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching (Vth ∼ 1-2 V) with an ultra-low energy requirement of ∼1.3 fJ per synaptic event. Several configurations of the order of hierarchy in the device architecture are studied comprehensively to identify the importance of the individual metallic components in contributing to the threshold switching and energy-minimization. The emerging potentiation behavior of the conductance (G) profile under electrical stimulation and its permanence beyond are realized over a wide current compliance range of 0.25 to 300 μA, broadly classifying the short- and long-term potentiation grounded on the characteristics of filamentary structures. The scale-free correlation of potentiation in the device hosting metallic filaments of diverse shapes and strengths could provide an ideal platform for understanding and replicating the complex behavior of the brain for neuromorphic computing.

Electron-Sponge Nature of Polyoxometalates for Next-Generation Electrocatalytic Water Splitting and Nonvolatile Neuromorphic Devices

Designing next-generation molecular devices typically necessitates plentiful oxygen-bearing sites to facilitate multiple-electron transfers. However, the theoretical limits of existing materials for energy conversion and information storage devices make it inevitable to hunt for new competitors. Polyoxometalates (POMs), a unique class of metal-oxide clusters, have been investigated exponentially due to their structural diversity and tunable redox properties. POMs behave as electron-sponges owing to their intrinsic ability of reversible uptake-release of multiple electrons. In this review, numerous POM-frameworks together with desired features of a contender material and inherited properties of POMs are systematically discussed to demonstrate how and why the electron-sponge-like nature of POMs is beneficial to design next-generation water oxidation/reduction electrocatalysts, and neuromorphic nonvolatile resistance-switching random-access memory devices. The aim is to converge the attention of scientists who are working separately on electrocatalysts and memory devices, on a point that, although the application types are different, they all hunt for a material that could exhibit electron-sponge-like feature to realize boosted performances and thus, encouraging the scientists of two completely different fields to explore POMs as imperious contenders to design next-generation nanodevices. Finally, challenges and promising prospects in this research field are also highlighted.

Theoretical Study on the Open-Shell Electronic Structure and Electron Conductivity of [18]Annulene as a Molecular Parallel Circuit Model

Herein, the electron conductivities of [18]annulene and its derivatives are theoretically examined as a molecular parallel circuit model consisting of two linear polyenes. Their electron conductivities are estimated by elastic scattering Green's function (ESGF) theory and density functional theory (DFT) methods. The calculated conductivity of the [18]annulene does not follow the classical conductivity, i.e., Ohm's law, suggesting the importance of a quantum interference effect in single molecules. By introducing electron-withdrawing groups into the annulene framework, on the other hand, a spin-polarized electronic structure appears, and the quantum interference effect is significantly suppressed. In addition, the total current is affected by the spin polarization because of the asymmetry in the coupling constant between the molecule and electrodes. From these results, it is suggested that the electron conductivity as well as the quantum interference effect of π-conjugated molecular systems can be designed using their open-shell nature, which is chemically controlled by the substituents.

Reconfigurable Logic-in-Memory Constructed Using an Organic Antiambipolar Transistor

We demonstrate an electrically reconfigurable two-input logic-in-memory (LIM) using a dual-gate-type organic antiambipolar transistor (DG-OAAT). The attractive feature of this device is that a phthalocyanine-cored star-shaped polystyrene is used as a nano-floating gate, which enables the electrical switching of individual logic circuits and stores the circuit information by the nonvolatile memory effect. First, the DG-OAAT exhibited Λ-shaped transfer curves with hysteresis by sweeping the bottom-gate voltage. Programming and erasing operations enabled the reversible shift of the Λ-shaped transfer curves. Furthermore, the top-gate voltage effectively tuned the peak voltages of the transfer curves. Consequently, the combination of dual-gate and memory effects achieved electrically reconfigurable two-input LIM operations. Individual logic circuits (e.g., OR/NAND, XOR/NOR, and AND/XOR) were reconfigured by the corresponding programming and erasing operations without any variations in the input signals. Our device concept has the potential to fulfill an epoch-making organic integration circuit with a simple device configuration.

Artificial-intelligence-assisted mass fabrication of nanocantilevers from randomly positioned single carbon nanotubes

Nanoscale cantilevers (nanocantilevers) made from carbon nanotubes (CNTs) provide tremendous benefits in sensing and electromagnetic applications. This nanoscale structure is generally fabricated using chemical vapor deposition and/or dielectrophoresis, which contain manual, time-consuming processes such as the placing of additional electrodes and careful observation of single-grown CNTs. Here, we demonstrate a simple and Artificial Intelligence (AI)-assisted method for the efficient fabrication of a massive CNT-based nanocantilever. We used randomly positioned single CNTs on the substrate. The trained deep neural network recognizes the CNTs, measures their positions, and determines the edge of the CNT on which an electrode should be clamped to form a nanocantilever. Our experiments demonstrate that the recognition and measurement processes are automatically completed in 2 s, whereas comparable manual processing requires 12 h. Notwithstanding the small measurement error by the trained network (within 200 nm for 90% of the recognized CNTs), more than 34 nanocantilevers were successfully fabricated in one process. Such high accuracy contributes to the development of a massive field emitter using the CNT-based nanocantilever, in which the output current is obtained with a low applied voltage. We further showed the benefit of fabricating massive CNT-nanocantilever-based field emitters for neuromorphic computing. The activation function, which is a key function in a neural network, was physically realized using an individual CNT-based field emitter. The introduced neural network with the CNT-based field emitters recognized handwritten images successfully. We believe that our method can accelerate the research and development of CNT-based nanocantilevers for realizing promising future applications.

In-materio reservoir working at low frequencies in a Ag2S-island network

A Ag₂S-island network is fabricated with surrounding electrodes to enable it to be used as a reservoir for unconventional computing. Local conductance change occurs due to the growth/shrinkage of Ag filaments from/into each Ag₂S island in the reservoir. The growth/shrinkage of Ag filaments is caused by the drift of Ag⁺ cations in each Ag₂S island, which results in a unique non-linear response as a reservoir, especially at lower frequencies. The response of the reservoir is shown to depend on the frequency and amplitude of the input signals. So as to evaluate its capability as a reservoir, logical operations were performed using the subject Ag₂S-island network, with the results showing an accuracy of greater than 99%.

Electrically Reconfigurable Organic Logic Gates: A Promising Perspective on a Dual-Gate Antiambipolar Transistor

Electrically reconfigurable organic logic circuits are promising candidates for realizing new computation architectures, such as artificial intelligence and neuromorphic devices. In this study, multiple logic gate operations are attained based on a dual-gate organic antiambipolar transistor (DG-OAAT). The transistor exhibits a Λ-shaped transfer curve, namely, a negative differential transconductance at room temperature. It is important to note that the peak voltage of the drain current is precisely tuned by three input signals: bottom-gate, top-gate, and drain voltages. This distinctive feature enables multiple logic gate operations with "only a single DG-OAAT," which are not obtainable in conventional transistors. Five logic gate operations, which correspond to AND, OR, NAND, NOR, and XOR, are demonstrated by adjusting the bottom-gate and top-gate voltages. Moreover, varying the drain voltage makes it possible to reversibly switch two logic gates, e.g., NAND/NOR and OR/XOR. In addition, the DG-OAATs show a high degree of stability and reliability. The logic gate operations are observed even months later. The hysteresis in the transfer curves is also negligible. Thus, the device concept is promising for realizing multifunctional logic circuits with a simple transistor configuration. Hence, these findings are expected to surpass the current limitations in complementary metal-oxide-semiconductor devices.

Neuromorphic behaviour in discontinuous metal films

Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple evaporation processes, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex. We further demonstrate that these signals meet established criteria for criticality. We perform a detailed experimental investigation of the atomic-scale switching processes that are responsible for these signals, and show that they mimic the integrate-and-fire mechanism of biological neurons. Using numerical simulations and a simple circuit model, we show that the characteristic features of the switching events are dependent on the network state and the local position of the switch within the complex network. We conclude that discontinuous films provide an interesting potential platform for brain-inspired computing.

Stochastic Resonance in Organic Electronic Devices

Stochastic Resonance (SR) is a phenomenon in which noise improves the performance of a system. With the addition of noise, a weak input signal to a nonlinear system, which may exceed its threshold, is transformed into an output signal. In the other words, noise-driven signal transfer is achieved. SR has been observed in nonlinear response systems, such as biological and artificial systems, and this review will focus mainly on examples of previous studies of mathematical models and experimental realization of SR using poly(hexylthiophene)-based organic field-effect transistors (OFETs). This phenomenon may contribute to signal processing with low energy consumption. However, the generation of SR requires a noise source. Therefore, the focus is on OFETs using materials such as organic materials with unstable electrical properties and critical elements due to unidirectional signal transmission, such as neural synapses. It has been reported that SR can be observed in OFETs by application of external noise. However, SR does not occur under conditions where the input signal exceeds the OFET threshold without external noise. Here, we present an example of a study that analyzes the behavior of SR in OFET systems and explain how SR can be made observable. At the same time, the role of internal noise in OFETs will be explained.

Encapsulation of Polymetallic Oxygen Clusters in a Mesoporous/Microporous Thorium-Based Porphyrin Metal-Organic Framework for Enhanced Photocatalytic CO2 Reduction

Solar-initiated CO₂ reduction is significant for green energy development. Herein, we have prepared a new mesoporous/microporous porphyrin metal-organic framework (MOF), IHEP-20, loaded with polymetallic oxygen clusters (POMs) to form a composite material POMs@IHEP-20 for visible-light-driven photocatalytic CO₂ reduction. The as-made composite material exhibits good stability in water from pH 0 to 11. After POMs were introduced to IHEP-20, they showed superior activity toward photocatalytic CO₂ reduction with a CO production rate of 970 μmol·g^-1·h^-1, which is 3.27 times higher than that of pristine IHEP-20. This study opens a new door for the design and synthesis of high-performance catalysts for the photocatalytic reduction of CO₂.

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks

Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.

Physical Implementation of Reservoir Computing through Electrochemical Reaction

Nonlinear dynamical systems serving reservoir computing enrich the physical implementation of computing systems. A method for building physical reservoirs from electrochemical reactions is provided, and the potential of chemical dynamics as computing resources is shown. The essence of signal processing in such systems includes various degrees of ionic currents which pass through the solution as well as the electrochemical current detected based on a multiway data acquisition system to achieve switchable and parallel testing. The results show that they have respective advantages in periodic signals and temporal dynamic signals. Polyoxometalate molecule in the solution increases the diversity of the response current and thus improves their abilities to predict periodic signals. Conversely, distilled water exhibits great computing power in solving a second-order nonlinear problem. It is expected that these results will lead to further exploration of ionic conductance as a nonlinear dynamical system and provide more support for novel devices as computing resources.

The Intrinsic Charge Carrier Behaviors and Applications of Polyoxometalate Clusters Based Materials

Polyoxometalates (POMs) are a series of molecular metal oxide clusters, which span the two domains of solutes and solid metal oxides. The unique characters of POMs in structure, geometry, and adjustable redox properties have attracted widespread attention in functional material synthesis, catalysis, electronic devices, and electrochemical energy storage and conversion. This review is focused on the links between the intrinsic charge carrier behaviors of POMs from a chemistry-oriented view and their recent ground-breaking developments in related areas. First, the advantageous charge transfer behaviors of POMs in molecular-level electronic devices are summarized. Solar-driven, thermal-driven, and electrochemical-driven charge carrier behaviors of POMs in energy generation, conversion and storage systems are also discussed. Finally, present challenges and fundamental insights are discussed as to the advanced design of functional systems based upon POM building blocks for their possible emerging application areas.

All-Passive Hardware Implementation of Multilayer Perceptron Classifiers

Bottom-up-fabricated crossbars promise superior circuit density and 3-D integrability compared with the traditional CMOS-based implementations. However, their inherent stochasticity presents difficulties in building complex circuits from components that demand precise patterning and high registration accuracies. With fewer terminals than active devices, passive components offer higher device densities and registration tolerances, making them amenable to bottom-up synthesized nanocrossbars. Motivated by this preference for passivity, we explore, in this article, neuromorphic classifiers based on passive neurons and passive synapses. We demonstrate via SPICE simulations how a shallow network of the diode-resistor-based passive rectifier neurons and resistive voltage summers, despite its inherent inability to buffer, amplify, and negate signals, can recognize MNIST digits with 95.4% accuracy. We introduce weight-to-conductance mappings that enable negative weights to be implemented in hardware without excessive memory overheads. The influences of soft and hard defects on the classification performance are evaluated, and the methods to boost fault-tolerance are proposed. The first-order evaluation of the area, speed, and power consumption of the passive multilayer perceptron classifiers is undertaken, and the results are compared with a benchmark study in neuromorphic hardware.

Information dynamics in neuromorphic nanowire networks

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

The rise of intelligent matter

Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing.

Nanowire networks: how does small-world character evolve with dimensionality?

Networks of nanowires are currently under consideration for a wide range of electronic and optoelectronic applications. Nanowire devices are usually made by sequential deposition, which inevitably leads to stacking of the wires on top of one another. Here we demonstrate the effect of stacking on the topology of the resulting networks. We compare perfectly 2D networks with quasi-3D networks, and compare both nanowire networks to the corresponding Watts Strogatz networks, which are standard benchmark systems. By investigating quantities such as clustering, path length, modularity, and small world propensity we show that the connectivity of the quasi-3D networks is significantly different to that of the 2D networks, a result which may have important implications for applications of nanowire networks.

A protecting group strategy to access stable lacunary polyoxomolybdates for introducing multinuclear metal clusters

Although metal-containing polyoxomolybdates (molybdenum oxide clusters) exhibit outstanding catalytic properties, their precise synthetic method has not yet been developed. This is mainly because the very low stability of the multivacant lacunary polyoxomolybdates limited their use as synthetic precursors. Here, we present a "protecting group strategy" in polyoxometalate synthesis and successfully develop an efficient method for synthesising multinuclear metal-containing polyoxomolybdates using pyridine as a protecting group for unstable trivacant lacunary Keggin-type polyoxomolybdate [PMo₉O₃₄]^9-. Specifically, tetranuclear cubane- and planar-type manganese clusters were selectively synthesised in the polyoxomolybdates using the present method. The importance of this work is that, in addition to being the first practical way of utilizing multivacant lacunary polyoxomolybdates as precursors, this new "protecting group strategy" will make it possible to produce polyoxometalates with unexplored structures and properties.

Astability versus Bistability in van der Waals Tunnel Diode for Voltage Controlled Oscillator and Memory Applications

van der Waals (vdW) tunnel junctions are attractive because of their atomically sharp interface, gate tunability, and robustness against lattice mismatch between the successive layers. However, the negative differential resistance (NDR) demonstrated in this class of tunnel diodes often exhibits noisy behavior with low peak current density and lacks robustness and repeatability, limiting their practical circuit applications. Here, we propose a strategy of using a 1L-WS₂ as an optimum tunnel barrier sandwiched in a broken gap tunnel junction of highly doped black phosphorus (BP) and SnSe₂. We achieve high yield tunnel diodes exhibiting highly repeatable, ultraclean, and gate-tunable NDR characteristics with a signature of intrinsic oscillation, and a large peak-to-valley current ratio (PVCR) of 3.6 at 300 K (4.6 at 7 K), making them suitable for practical applications. We show that the thermodynamic stability of the vdW tunnel diode circuit can be tuned from astability to bistability by altering the constraint through choosing a voltage or a current bias, respectively. In the astable mode under voltage bias, we demonstrate a compact, voltage-controlled oscillator without the need for an external tank circuit. In the bistable mode under current bias, we demonstrate a highly scalable, single-element, one-bit memory cell that is promising for dense random access memory applications in memory intensive computation architectures.

Photoswitching Molecular Junctions: Platforms and Electrical Properties

Remarkable advances in technology have enabled the manipulation of individual molecules and the creation of molecular electronic devices utilizing single and ensemble molecules. Maturing the field of molecular electronics has led to the development of functional molecular devices, especially photoswitching or photochromic molecular junctions, which switch electronic properties under external light irradiation. This review introduces and summarizes the platforms for investigating the charge transport in single and ensemble photoswitching molecular junctions as well as the electronic properties of diverse photoswitching molecules such as diarylethene, azobenzene, dihydropyrene, and spiropyran. Furthermore, the article discusses the remaining challenges and the direction for moving forward in this area for future photoswitching molecular devices.

Local-field-induced current noise in shape-limited self-doped polyaniline

Electronic noise generators are an essential component of molecular neuromorphic devices. To realize molecular noise generators with a high degree of freedom for design and integration into molecular devices, the utilization of the local electric field for the modulation of electrical conduction via a shape-limited conductive polymer is one promising strategy. Herein, a molecular noise generator composed of thin self-doped polyaniline (SPAN) lines is reported. SPAN lines fabricated via fountain pen lithography on SiO₂/Si substrates were found to generate current noise upon laser irradiation. This current noise exhibited white-noise-like power spectral density in the frequency range of 1-25 Hz and was independent of temperature. Multiple independent noise generation on the same substrate was also successfully demonstrated. The present results indicate that the noise generation mechanism involves the local modulation of hopping conduction via SPAN lines owing to the spatial proximity of the conduction path in the SPAN line to the surface photovoltage region of the SiO₂/Si interface. This on-site random noise generation in shape-limited conductive polymers is expected to be beneficial for the realization of molecular neuromorphic devices.

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks

Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network. The atomic-scale dynamics emulate leaky integrate and fire (LIF) mechanisms in biological neurons, leading to the generation of critical avalanches of signals. These avalanches are quantitatively the same as those observed in cortical tissue and are signatures of the correlations that are required for computation. We show that the avalanches are associated with dynamical restructuring of the networks which self-tune to balanced states consistent with self-organized criticality. Our simulations allow visualization of the network states and detailed mechanisms of signal propagation.

Nanoscale Patterning of Carbon Nanotubes: Techniques, Applications, and Future

Carbon nanotube (CNT) devices and electronics are achieving maturity and directly competing or surpassing devices that use conventional materials. CNTs have demonstrated ballistic conduction, minimal scaling effects, high current capacity, low power requirements, and excellent optical/photonic properties; making them the ideal candidate for a new material to replace conventional materials in next-generation electronic and photonic systems. CNTs also demonstrate high stability and flexibility, allowing them to be used in flexible, printable, and/or biocompatible electronics. However, a major challenge to fully commercialize these devices is the scalable placement of CNTs into desired micro/nanopatterns and architectures to translate the superior properties of CNTs into macroscale devices. Precise and high throughput patterning becomes increasingly difficult at nanoscale resolution, but it is essential to fully realize the benefits of CNTs. The relatively long, high aspect ratio structures of CNTs must be preserved to maintain their functionalities, consequently making them more difficult to pattern than conventional materials like metals and polymers. This review comprehensively explores the recent development of innovative CNT patterning techniques with nanoscale lateral resolution. Each technique is critically analyzed and applications for the nanoscale-resolution approaches are demonstrated. Promising techniques and the challenges ahead for future devices and applications are discussed.

Carbon dot-based fluorometric optical sensors: an overview

Fluorescent carbon dots (CDs) are a new class of carbon nanomaterials and have demonstrated excellent optical properties, good biocompatibility, great aqueous solubility, low cost, and simple synthesis. Since their discovery, various synthesis methods using different precursors were developed, which were mainly classified as top-down and bottom-up approaches. CDs have presented many applications, and this review article mainly focuses on the development of CD-based fluorescent sensors. The sensing mechanisms, sensor design, and sensing properties to various targets are summarized. Broad ranges of detection, including temperature, pH, DNA, antibiotics, cations, cancer cells, and antibiotics, have been discussed. In addition, the challenges and future directions for CDs as sensing materials are also presented.

Chemical Control of Electronic Coupling between a Ruthenium Complex and Gold Electrode for Resonant Tunneling Conduction

Current-voltage (I-V) nonlinearity is essential for information processing in molecular electronics. We used a nanoparticle bridge junction to investigate the effect of electronic coupling between a Ru complex and electrodes on nonlinear electrical properties. Two types of molecular layers, in which the Ru complex forms different chemical bondings to the electrode, were used for electrical measurements. The chemical bond and the surface potential of the Ru complex on Au electrodes were examined by X-ray photoelectron spectroscopy, infrared ray reflection absorbance spectroscopy and Kelvin probe force microscopy, respectively. The device, in which the Ru complex is directly bound to the Au electrode, indicated the nonlinear I-V characteristics with zero-bias conductance because of the direct tunneling conduction. Another device fabricated by inserting a spacer molecule between the Ru complex and the Au electrode realized nonlinear I-V characteristics with a clear threshold voltage and little zero-bias conductance. The I-V curves were well fitted by the resonant tunneling conduction model. The present results show the significance of controlling the electronic coupling for nonlinear I-V characteristics.

Polyoxometalates as components of supramolecular assemblies

The non-covalent interaction of polyoxometalates (POMs) with inorganic- or organic-based moieties affords hybrid assemblies with specific physicochemical properties that are of high interest for both fundamental and applied studies, including the discovery of conceptually new compounds and unveiling the impact of their intra-supramolecular relationships on the fields of catalysis, molecular electronics, energy storage and medicine. This minireview summarises the recent advances in the synthetic strategies towards the formation of such non-covalent POM-loaded assemblies, shedding light on their key properties and the currently investigated applications. Four main emerging categories according to the nature of the conjugate are described: (i) POMs in metal-organic frameworks, (ii) POMs merged with cationic metal complexes, (iii) architectures generated with solely POM units and (iv) POMs assembled with organic molecular networks.