Nanoparticle Networks Research Articles

From Solid to Fluid: Novel Approaches in Neuromorphic Engineering.

Neuromorphic engineering is rapidly developing as an approach to mimicking processes in brains using artificial memristors, devices that change conductivity in response to the electrical field (resistive switching effect). Memristor-based neuromorphic systems can overcome the existing problems of slow and energy-inefficient computing that conventional processors face. In the Introduction, the basic principles of memristor operation and its applications are given. The history of switching in sandwich structures and granular metals is reviewed in the Historical Overview. Particular attention is paid to the fundamental articles from the pre-memristor era (the 1960s-70s), which demonstrated the first evidence of resistive switching and predicted the filamentary mechanism of switching. Multi-dimensionality in neuromorphic systems: Despite the powerful computational abilities of traditional memristor arrays, they cannot repeat many organizational characteristics of biological neural networks, i.e., their multi-dimensionality. This part reviews the unconventional nanowire- and nanoparticle-based neuromorphic systems that demonstrate incredible potential for use in reservoir computing due to the unique spiking change in conductance similar to firing in neurons. Liquid-based neuromorphic devices: The transition of neuromorphic systems from solid to liquid state broadens the possibilities for mimicking biological processes. In this section, ionic current memristors are reviewed and, the working principles of which bring us closer to the mechanisms of information transmittance in real synapses. Nanofluids: A novel direction in neuromorphic engineering linked to the application of nanofluids for the formation of reconfigurable nanoparticle networks with memristive properties is given in this section. The Conclusion t summarizes the bullet points of the Review and provides an outlook on the future of liquid-state neuromorphic systems.

Probing Spatial Energy Flow in Plasmonic Catalysts from Charge Excitation to Heating: Nonhomogeneous Energy Distribution as a Fundamental Feature of Plasmonic Chemistry.

Plasmonic catalysts use light to drive chemical reactions. One critical question is how light energy moves at nanoscales in these complex systems, leading to chemical transformations. In this contribution, we map out this energy flow by developing approaches to measure spatial temperature distributions in heterogeneous plasmonic catalysts, consisting of three-dimensional networks of plasmonic nanoparticles anchored on an oxide support. We survey the local temperatures of molecules adsorbed on catalytically active plasmonic nanoparticles, the nanoparticles themselves, and the catalyst support, under steady-state continuous-wave illumination. We reveal the existence of large temperature gradients, in which the local temperatures of the molecules, nanoparticles, and the surrounding environment can vary greatly. We show that these temperature gradients are a natural consequence of plasmon relaxation, involving the interconversion between electromagnetic light energy, electronic excitations, and heating of various entities as these electronic excitations relax. The presence of these gradients is a fundamental and unique feature of gas-phase plasmonic catalysis.

Integer Factorization with Stochastic Spiking in Percolating Networks of Nanoparticles.

As growth in global demand for computing power continues to outpace ongoing improvements in transistor-based hardware, novel computing solutions are required. One promising approach employs stochastic nanoscale devices to accelerate probabilistic computing algorithms. Percolating Networks of Nanoparticles (PNNs) exhibit stochastic spiking, which is of particular interest as it meets criteria for criticality which is associated with a range of computational advantages. Here, we show several ways in which spiking PNNs can be used as the core stochastic components of coupled networks that allow successful factorization of integers up to 945. We demonstrate asynchronous operation and show that a single device is sufficient to solve all factorization tasks and to generate multiple solutions simultaneously.

Submicron-scale Au-decorated TiO2 mesoporous spheres for enhanced photon harvesting in DSSCs through near-field enhancement, light scattering, and dye loading

Light harvesting materials are crucial for capturing the sunlight in a device such as a solar cell for better efficiency. In this study, we developed high surface area, submicron-sized TiO2 spheres (MTS) incorporated with anisotropic Au nanoparticles (Au_MTS) to create highly light-absorbing photoanodes for enhanced dye-sensitized solar cell (DSSC) efficiency. The high surface area of MTS (∼125 m2/g) allows for increased dye-loading, while their submicron size (150–300 nm) provides superior light-scattering capabilities for significantly enhancing the photoanode’s light absorption. Furthermore, incorporating of anisotropic Au nanoparticles enables broadband surface plasmon resonance (SPR) coupling, synergistically boosting photon harvesting in the Au_MTS photoanodes. The interconnected tiny TiO2 nanoparticle network in MTS supports charge carrier generation and transport, providing ample sites for dye adsorption and efficient electron pathways. Au_MTS with varying amounts of Au nanoparticles synthesized by a greener microwave-assisted synthesis method and DSSC devices were fabricated and compared with devices made from pristine MTS and P25 nanoparticles. The optimal Au_MTS device, containing ∼1.3 wt% Au nanoparticles, achieved a maximum power conversion efficiency (PCE) of ∼7.7%, representing improvements of ∼40% and ∼60% over pristine MTS (PCE of ∼5.2%) and P25 nanoparticles (PCE of ∼4.71%), respectively. Overall, this work demonstrates the effectiveness of plasmonic mesoporous photoanodes in enhancing DSSC performance through improved photo response, light scattering, and dye loading.

Second-order mean-field approximation for calculating dynamics in Au-nanoparticle networks.

Exploiting physical processes for fast and energy-efficient computation bears great potential in the advancement of modern hardware components. This paper explores nonlinear charge tunneling in nanoparticle networks, controlled by external voltages. The dynamics are described by a master equation, which expresses the time-evolution of a distribution function over the set of charge occupation numbers. The driving force behind this evolution is charge tunneling events among nanoparticles and their associated rates. We introduce two mean-field approximations to this master equation. By parametrization of the distribution function using its first- and second-order statistical moments, and a subsequent projection of the dynamics onto the resulting moment manifold, one can deterministically calculate expected charges and currents. Unlike a kinetic Monte Carlo approach, which extracts samples from the distribution function, this mean-field approach avoids any random elements. A comparison of results between the mean-field approximation and an already available kinetic Monte Carlo simulation demonstrates great accuracy. Our analysis also reveals that transitioning from a first-order to a second-order approximation significantly enhances the accuracy. Furthermore, we demonstrate the applicability of our approach to time-dependent simulations, using Eulerian time-integration schemes.

Frequency and voltage-modulation multistable wave plate based on liquid crystals doped with silica nanoparticles

In this study, we demonstrate an all-range multistable liquid crystal (LC) wave plate. Its multistability arises from the formation of silica nanoparticle networks within the LC cell on the substrates, which stabilize LC molecules and induce varying phase retardation. Modulating the phase retardation of the LC wave plate can be achieved through the application of a DC pulse or by adjusting the frequency of the pulse. This multistable characteristic not only conserves energy but also enhances its versatility across diverse domains.

Dynamic Covalent Polymer–Nanoparticle Networks as High-Performance Green Lubricants: Synergetic Effect in Load-Bearing Capacity

Dynamic Covalent Polymer–Nanoparticle Networks as High-Performance Green Lubricants: Synergetic Effect in Load-Bearing Capacity

Programmable Analog Circuits with Neuromorphic Nanostructured Platinum Films

AbstractSelf‐assembled nanoparticle or nanowire networks have recently come under the spotlight as systems able to emulate brain‐like data processing performances by exploiting the memristive character and the wiring of the junctions connecting the nanostructured network building blocks. Recently it is demonstrated that nanostructured Au films, fabricated by the assembling of gold clusters produced in the gas phase, have non‐linear and non‐local electric conduction properties caused by the extremely high density of grain boundaries and the resulting complex arrangement of nanojunctions. These observations suggest that nanostructured metallic films can be explored and exploited as a novel class of neuromorphic systems for unconventional electronic devices. Here it is reported that nanostructured cluster‐assembled Pt films show resistive switching activity, negative differential resistance, and reversible memory effects. These properties allow the use of nanostructured Pt films in programmable analog circuits, such as gain amplifiers and relaxation oscillators with fast response.

Novel Graphene-Based Reversible Physisorption Materials for High Hydrogen Uptake

Carbon-based nanomaterials (graphene, carbon nanotube, microporous carbon, graphitic carbon etc.) are excellent H2 storage materials due to high molecular H2 uptake at the micropores and large H2 adsorption at the graphene network via the formation of dipoles in H2 molecules through London dispersion forces. On the other hand, incorporation of transition metal nanoparticles within the nanocarbon network facilitates dissociation of H2 molecules into constituents H-atoms over the metal nanoparticles, followed by diffusion into the carbon nano-matrix via ‘spill-over’ effect for polarization/hybridization of metal-hydrogen to create high H2 binding energy for high hydrogen adsorption. Therefore, a nanocomposite of graphene-microporous carbon-metal nanoparticle network is expected to deliver excellent H2 uptake capacity and acts as a novel reversible physisorption material for hydrogen storage applications, which is very important for sustainable renewable energy integration.A simple, cost effective sputtering method is used to fabricate metal incorporated graphitic microporous carbon film and differential resistance measurement showed high H2 uptake. Average number of graphene layer formation is dependent on sputtering parameters, which manifest bi-to-multilayer graphene. With increase in graphene layers, H2 adsorption also increases due to a fourfold effect: higher molecular hydrogen uptake at the graphene layers, more active sites into the micropores, promotion of molecular dissociation into atomic hydrogen by the metal nanoparticles, and the formation of hydrogenated carbon via destruction of the π-bonds.

Thermodynamic anti-agglomeration based selenium-doped tin oxide network: Enhancing cycling stability of high-rate lithium storage

In this work, the grain line-cutting effect induced by selenium doping into SnO2 aggregation was proposed to prepare uniformly distributed SnO2/Se nanoparticles (SnO2/Se NPs) network with thermodynamic anti-agglomeration force during high-temperature sintering. These SnO2/Se NPs offers abundant active sites, reduced volume expansion and fast Li+ diffusion ability, especially the enhanced electronic conductivity due to oxygen vacancies introduced by doping. More importantly, the thermodynamic anti-agglomeration of SnO2/Se NPs effectively suppresses the Sn coarsening and Li2O/Sn phase segregation during repeated charge/discharge processes. Consequently, the SnO2/Se NPs anode delivers excellent reversible capacity of 515 mAh g−1 after 500 cycles at 1 A g−1, and the SnO2/Se NPs||LiFePO4 full cells maintains a high-rate capacity of 109 mAh g−1 at 0.3 A g−1. The SnO2/Se NPs anode is compatible with high concentration polymer electrolyte (HCPE), which shows a capacity of 520 mAh g−1 after 100 cycles at 0.2 A g−1 in solid state batteries. This study provides a new insight for improving the cyclic stability of conversion-alloying anodes, offering their promising prospects for the full batteries and solid-state batteries.

Nanocarriers Loaded with Danshensu for Treating Ischemic Stroke by Reducing Oxidative Stress and Glial Overactivation.

Ischemic stroke is a complex health condition that can cause ischemia and necrosis of brain tissue. Subsequently, the excessive activation of glial cells can result in various inflammatory and oxidative stress reactions that exacerbate ischemic brain injury. In this paper, we propose the targeted self-assembly of a three-dimensional nanoparticle network containing Danshensu to rescue ischemic penumbra by reducing oxidative stress and glial overactivation. The network comprises nanoparticles composed of chitosan, thiol ketone, and carboxymethyl-β-cyclodextrin as the core wrapped by the Pro-His-Ser-Arg-Asn (PHSRN) peptide sequence as the outer layer and loaded with Danshensu. The PHSRN-peptide-modified nanoparticles bind to integrin α5β1 overexpressed on the damaged blood-brain barrier and accumulate in the damaged brain in a rat model of ischemia/reperfusion. When stimulated by reactive oxygen species, thiol ketone bonded to the nanoparticles was hydrolyzed, facilitating responsive drug release while consuming the reactive oxygen species. Subsequently, the released Danshensu scavenged the reactive oxygen species to prevent oxidative stress and inhibited the activation of astrocytes, thereby suppressing proinflammatory cytokine secretion, improving the inflammatory brain microenvironment and reducing neuronal apoptosis.

In situ-gelling hydrophobized starch nanoparticle-based nanoparticle network hydrogels for the effective delivery of intranasal olanzapine to treat brain disorders

Intranasal (IN) delivery offers potential to deliver antipsychotic drugs with improved efficacy to the brain. However, the solubilization of such drugs and the frequency of required re-application both represent challenges to its practical implementation in treating various mental illnesses including schizophrenia. Herein, we report a sprayable nanoparticle network hydrogel (NNH) consisting of hydrophobically-modified starch nanoparticles (SNPs) and mucoadhesive chitosan oligosaccharide lactate (COL) that can gel in situ within the nasal cavity and release ultra-small penetrative SNPs over time. Hydrophobization of the SNPs enables enhanced uptake and prolonged release of poorly water soluble drugs such as olanzapine from the NNH depot through mucous and ultimately into the brain via the nose-to-brain (N2B) pathway. The hydrogel shows high in vitro cytocompatibility in mouse striatal neuron and human primary nasal cell lines and in vivo efficacy in an amphetamine-induced pre-clinical rat schizophrenia model, with IN-delivered NNH hydrogels maintaining successful attenuation of locomotor activity for up to 4 h while all other tested treatments (drug-only IN or conventional intraperitoneal delivery) failed to attenuate at any time point past 0.5 h. As such, in situ-gelling NNHs represent a safe excipient for the IN delivery of hydrophobic drugs directly to the brain using customized SNPs that exhibit high penetration and drug complexing properties to maximize effective drug delivery.

Vapor-Tailored Nanojunctions in Ultraporous ZnO Nanoparticle Networks for Superior UV Photodetection.

High quality nanojunctions are known to effectively improve the conductivity and structural robustness of ultraporous nanoparticle networks, surpassing the performance of natural van der Waals interfaces. Nevertheless, the traditional approach of forming these junctions by thermal annealing is incompatible with thermolabile polymers and slender metal electrodes found in modern wearable technologies. Herein, we present a low temperature, solvent vapor-based method to rapidly elicit high-quality metal-oxide nanojunctions in a fast, effortless, inexpensive, and easily scalable process; capable of generating necked interparticle interfaces in a matter of minutes. When applied to ultraporous-based ZnO Ultraviolet (UV) photodetectors, the vapor-tailoring process produces an incredible 128,000-fold improvement in responsivity (6.6 A.W-1) over untreated structures (51.2µA.W-1), and a 5300-fold improvement in responsivity over thermally annealed structures; all while maintaining exceptionally low dark currents of 140 pA at a low bias voltage of 1V. Most importantly, the exceptional performance enabled by room temperature synthesis suggests high potential adaptability of this process toward wearable UV sensors, shedding lights on the strategy of modifying weakly bonded porous nanostructures for improved physical properties.

Highly Stretchable Thermoelectric Fiber with Embedded Copper(I) Iodide Nanoparticles for a Multimodal Temperature, Strain, and Pressure Sensor in Wearable Electronics

AbstractThermoelectric (TE) fibers have excellent potential for multimodal sensor, which can detect mechanical and thermal stimuli, used in advanced wearable electronics for personalized healthcare system. However, previously reported TE fibers have limitations for use in wearable multimodal sensors due to the following reasons: 1) TE fibers composed of carbon or organic materials have low TE performance to detect thermal variations effectively; 2) TE fibers composed of rigid inorganic materials are not stretchable, limiting their ability to detect mechanical deformation. Herein, the first stretchable TE fiber‐based multimodal sensor is developed using copper(I) iodide (CuI), an inorganic TE material, through a novel fabrication method. The dense CuI nanoparticle networks embedded in the fiber allow the sensor to achieve excellent stretchability (maximum tensile strain of ≈835%) and superior TE performance (Seebeck coefficient of ≈203.6 µV K−1) simultaneously. The sensor exhibits remarkable performances in strain sensing (gauge factor of ≈3.89 with tensile strain range of ≈200%) and pressure sensing (pressure resolution of ≈250 Pa with pressure range of ≈84 kPa). Additionally, the sensor enables independent and simultaneous temperature change, tensile strain, and pressure sensing by measuring distinct parameters. It is seamlessly integrated into a smart glove, demonstrating its practical application in wearable technology.

Exploring the effect of morphology on electrochemical performance of nickel and cobalt metal-organic frameworks and their derivatives for supercapacitor and hydrogen evolution

This study explores the novel approach of investigating the use of binder-free nickel and cobalt metal-organic frameworks and their metal oxide derivatives in 1D, 2D, and 3D morphologies on Ni foam for applications in supercapacitors and hydrogen evolution. This research work involves changing parameters like precursor concentration, organic linkers, and reaction time, leading to the formation of stable one-, two-, and three-dimensional NiO and Co3O4 nanostructures. The morphologies of pristine MOFs and their corresponding oxides were comprehensively verified through FESEM analysis. Significantly, the materials with two-dimensional growth exhibited superior electrochemical performance in supercapacitors and good electrocatalytic performance in hydrogen evolution reaction when compared to their one-dimensional and three-dimensional counterparts. Among them, 2D NiO demonstrated exceptional performance as a supercapacitor electrode, with a specific capacity of 688.13 C g−1 (1376.26 F g−1) at 1 A g−1, surpassing 1D and 3D NiO nanostructures. It also demonstrated good stability by retaining 93.1 % of its initial capacity after 5000 cycles at 10 A g−1. The assembled asymmetric supercapacitor (ASC) achieved a high energy density of 65.27 Wh kg−1 at 375 W kg−1, maintaining 33.18 Wh kg−1 even at 7500 W kg−1. It also maintained 87.8 % retention of its initial capacity up to 5000 cycles at 10 A g−1. Furthermore, when tested for HER, all oxides exhibited better performances with specifically 2D NiO exhibiting good catalytic performance, reaching 10 mA cm−2 at a low overpotential of 129 mV (vs. RHE) and displaying remarkable stability over 24 h. This study upholds the superiority of two-dimensional sheet-like structures with interconnected network of nanoparticles in electrochemical and electrocatalytic performance compared to one-dimensional rod-like and three-dimensional pyramid-like structures.

Physical reservoir application of random network of gold nanoparticles fabricated by multi-step immersion in gold colloidal solution

We fabricated a random network of gold nanoparticles (RN-GNPs) over 12 NiCr/Au electrodes by using a multi-step immersion method, where a sample was immersed in a gold colloid solution three times. Nonlinear current–voltage characteristics due to the Coulomb blockade were observed at 77 K. For demonstration of physical reservoir applications, input–output characteristics of the RN-GNPs were also measured in a one-input, nine-output terminal configuration. Distorted output voltage waveforms were obtained for a sinusoidal voltage input of 100 Hz. The higher-order harmonic components were confirmed in the frequency spectra of the outputs. The waveform reconstruction task and short-term storage capacity estimation were performed by an echo state network model with ridge regression and linear regression, respectively.

Morphology and electrical conductivity of carbon-coated Nickel reinforced high-performance polymer nanocomposites

Novel nanocomposites of poly (ether-ketone) (PEK) reinforced with carbon-coated Nickel nanoparticles (CCNi) were synthesized through a sequential process involving cost-effective ball milling and hot compaction. Scanning electron microscopy revealed an excellent dispersion and a three-dimensional network of CCNi nanoparticles in the matrix, causing a significant improvement in the electrical conductivity and electromagnetic interference shielding effectiveness (SE). Carbon coating of about 5 nm thick over Ni nanoparticle probably helps in uniform dispersion, avoids its oxidation and reduces its agglomeration in the matrix. An exceptionally low percolation threshold of 2.1 vol.% CCNi was found, and eight-orders of magnitude enhancement in the dc-electrical conductivity was achieved. The highest dc- and ac-electrical conductivities achieved were more than 0.01 S cm−1 at 5.89 vol.% CCNi nanoparticles content which were the highest values amongst reported Ni-filled polymer composites and comparable with those of carbon nanotubes filled PEK nanocomposites. Electromagnetic interference SE of the CCNi/PEK nanocomposites was measured in the X-band, and a total SE (SET) of 17.52 dB was obtained for 5.89 vol.% CCNi reinforced PEK nanocomposite.

Interfaces Between Nanoparticle and Biomacromolecular Network: Dynamic Behaviors, Confinement and Entropy

AbstractBiomacromolecular networks, crucial components of biological systems, are of great importance in biological and nanomedical science, in which the interfaces between nanoparticle and macromolecular networks are intriguing and drawing great attention. Herein, this review seeks to present the state‐of‐art progress and perspective of the dynamic behaviors, confinement and entropy at the nanoparticle‐biomacromolecular network interfaces. The basic biomacromolecular networks in living systems, recent research on networks with dynamic bonds and various diffusion behaviors of nanoparticles within biomacromolecular networks are summarized. In particular, entropic effects in the confined environments of biological networks are discussed, along with how they impact the mechanical properties and dynamical behaviors. The interesting research and findings included in this review might offer the scientific community a better understanding of how this field has developed thus far, and are anticipated to trigger more systematic and fundamental research to elucidate the underlying physics and broaden the potential applications of macromolecular networks, biological and artificial.

Bio‐Inspired Magnetic‐Responsive Supramolecular‐Covalent Semi‐Convertible Hydrogel

Bio-inspired magnetic-responsive hydrogel is confined in exceedingly narrow spaces for soft robots and biomedicine in either gel state or magnetofluidic sol state. However, the motion of the gel state magnetic hydrogel will be inhibited in various irregular spaces due to the fixed shape and size and the sol-state magnetofluid gel may bring unpredictable residues in the confined narrow space. Inspired by the dynamic liquid lubricating mechanism of biological systems, novel magnetic-responsive semi-convertible hydrogel (MSCH) is developed through imbedding magnetic-responsive gelatin and amino-modified Fe3O4 nanoparticles network into the covalent network of polyvinyl alcohol, which can be switched between gel state and gel-sol state in response to magnetic stimuli. It can be attributed the disassembly of triple-helix structures of the gelatin under the action of the magnetic field, driven by force from the magnetic particles conjugated on the gelatin chain through electrostatic interactions, while the covalent network retains the hydrogel structural integrity. This leads to a sol layer on the MSCH surface enabling the MSCH to pass effectively through the confined channel or obstacle under magnetic field. The present MSCH will provide an alternative mode for magnetic field-related soft robots or actuators.

A kinetic Monte Carlo approach for Boolean logic functionality in gold nanoparticle networks

Nanoparticles interconnected by insulating organic molecules exhibit nonlinear switching behavior at low temperatures. By assembling these switches into a network and manipulating charge transport dynamics through surrounding electrodes, the network can be reconfigurably functionalized to act as any Boolean logic gate. This work introduces a kinetic Monte Carlo-based simulation tool, applying established principles of single electronics to model charge transport dynamics in nanoparticle networks. We functionalize nanoparticle networks as Boolean logic gates and assess their quality using a fitness function. Based on the definition of fitness, we derive new metrics to quantify essential nonlinear properties of the network, including negative differential resistance and nonlinear separability. These nonlinear properties are crucial not only for functionalizing the network as Boolean logic gates but also when our networks are functionalized for brain-inspired computing applications in the future. We address fundamental questions about the dependence of fitness and nonlinear properties on system size, number of surrounding electrodes, and electrode positioning. We assert the overall benefit of having more electrodes, with proximity to the network’s output being pivotal for functionality and nonlinearity. Additionally, we demonstrate an optimal system size and argue for breaking symmetry in electrode positioning to favor nonlinear properties.