Nanoparticle Networks Research Articles

A Microwave-Strengthened Supramolecular Adhesive: from Flexible Pressure Sensitive Bonding to Strong and Muti-Reusable Hot Melt Bonding.

A microwave-strengthened supramolecular adhesive by introducing maleic acid amide bonds into the cross-linked networks of catechol-based monomers and iron oxide nanoparticles is reported. Under microwave irradiation, the supramolecular adhesive can be rapidly heated up, causing the transformation from maleic acid amide bonds to maleimide bonds and thus the increase of its cohesive strength. The supramolecular adhesive can flexibly bond substrates like pressure sensitive adhesives during the bonding procedure and shows an adhesion strength of 0.5MPa toward ceramic. After microwave strengthening, it can strongly bond substrates like hot melt adhesives and shows a dramatically increased adhesion strength of 5.0MPa, 10 times stronger than the original value. Moreover, the microwave-strengthened supramolecular adhesive can be reused 3 times with negligible loss in adhesion strength, exhibiting outstanding multiple reusability. By combining both high flexibility and high adhesion strength, it is anticipated that the supramolecular adhesive can be of great potential in cultural relics restoration and microelectronics.

Organic Crosslinked Tin Oxide Mitigating Buried Interface Defects for Efficient and Stable Perovskite Solar Cells

AbstractTin dioxide (SnO2) stands as a promising material for the electron transport layer (ETL) in perovskite solar cells (PSCs) attributed to its superlative optoelectronic properties. The attainment of superior power conversion efficiency hinges critically on the preparation of high‐quality SnO2 thin films. However, conventional nanoparticle SnO2 colloids often suffer from inherent issues such as numerous oxygen vacancy defects and film non‐uniformity. In this study, we report a strategy to homogenize SnO2 with reduced defects for high‐performance PSCs. The commercial SnO2 colloid is modulated with bisphenol S (BPS) crosslinking to achieve a better annealing intermediate state. The phenolic hydroxyl groups on BPS bond with the hydroxyl groups on the SnO2 surface, passivating defects as well as promoting superb regularity of the films by forming a network of the SnO2 nanoparticles. Additionally, the sulfone groups on BPS coordinate with Pb2+, regulating the crystallization of PbI2 and FAPbI3, which leads to better interface contact at the buried interface. The FAPbI3 perovskite solar cells based on BPS‐crosslinked SnO2 layers achieved a champion efficiency of 24.87 % and retained 95 % of their initial PCE after 1000 hours of continuous light soaking under N2 atmosphere.

Nose-to-brain delivery of lithium via a sprayable in situ-forming hydrogel composed of chelating starch nanoparticles.

While bipolar disorder patients can benefit from lithium therapy, high levels of lithium in the serum can induce undesirable systemic side effects. Intranasal (IN) lithium delivery offers a potential solution to this challenge given its potential to facilitate improved lithium transport to brain when delivered to the olfactory mucosa. Herein, a sprayable, in situ forming nanoparticle network hydrogel (NNH) based on Schiff base interactions between chelator-functionalized oxidized starch nanoparticles (SNPs) and carboxymethyl chitosan (CMCh) is reported that can be deployed within the nasal cavity to release ultra-small penetrative SNPs over time. Chelating functional groups including citrate, ethylenediaminetetraacetic acid, and pentetic acid are shown to bind a variety of cations including lithium, magnesium, and calcium, with chelation directly linked to enhancements in the gel mechanics even for monovalent lithium. The hydrogels show high in vitro cytocompatibility with mouse striatal neuron and human primary nasal cell lines. Effective IN delivery of lithium to the brain is demonstrated for the first time, with both solution-based and hydrogel-loaded lithium showing in vivo efficacy in an amphetamine-induced pre-clinical rat bipolar manic phase model; specifically, IN-delivered NNHs can maintain successful attenuation of locomotor activity for up to 6 h while all other tested treatments (drug-only IN or conventional intraperitoneal delivery) failed to retain attenuation for more than two hours at the same lithium dose. As such, in situ-gelling and ion-chelating NNHs represent a new material that can effectively enable metal ion management in biomedical applications.

From Frustration to Order: Role of Fluid-Fluid Interfaces in Precision Assembly of Nanoparticles.

Fluid-fluid interfaces are an attractive platform for self-assembling nanoparticles into low-dimensional materials. In this Perspective, we review recent developments in the use of interfaces to direct the assembly of spherical and anisotropic nanoparticles into diverse and sophisticated architectures. We illustrate how nanoparticle clusters, strings, networks, superlattices, chiral lattices, and quasicrystals can be self-assembled by harnessing the frustration between interfacial and interparticle forces. We highlight the role of polymeric ligands attached to the surface of nanoparticles in modulating assembly behavior by directly altering particle-fluid and particle-particle interactions or by deforming at interfaces and junctions between particles. We conclude by providing a roadmap of key questions and opportunities in this exciting field of interfacial assembly.

Autocatalytic Nucleation and Self-Assembly of Inorganic Nanoparticles into Complex Biosimilar Networks.

Self-replication of molecules and microdroplets have been explored as models in prebiotic chemistry. An analogous process for inorganic nanomaterials would involve the autocatalytic nucleation of nanoparticles-an area that remains largely uncharted. Demonstrating such systems would be both fundamentally intriguing and practically relevant, especially if the resulting particles self-assemble. Here, we show that autocatalytic nucleation couples with self-assembly occurs with silver nanoparticles. In dispersions containing "hedgehog" particles, these reactions produce complex colloids with hierarchical spike organization. On solid surfaces, autocatalytic nucleation of nanoparticles yields conformal networks with hierarchical organization, including nanoparticle "colonies." We analyzed the complexity of both types of solid-stabilized particle assemblies via graph theory (GT). The complexity index of idealized spiky colloids is comparable to that of complex algal skeletons. The GT analysis of the percolating nanoparticle networks revealed their similarities to the bacterial, but not fungal, biofilms. We conclude that coupling autocatalytic nucleation with self-assembly enables the generation of complex, biosimilar particles and films. This work establishes mathematical and structural parallels between biotic and abiotic matter, integrating self-organization, autocatalytic nucleation, and theoretical description of complex system. Utilization of quantitative descriptors of connectivity patterns opens possibility to GT-based biomimetic engineering of conductive coatings and other complex nanostructures.

Neuromorphic Computing Primitives Using Polymer-Networked Nanoparticles.

Nanoparticle networks have potential applications in brain-like computing yet their ability to adopt different states remains unexplored. In this work, we reveal the dynamics of the attachment of polyelectrolytes onto gold nanoparticles (AuNPs), using a bottom-up two-bead-monomer dissipative particle dynamics (TBM-DPD) model to show the heterogeneity of polymer coverage. We found that the use of one polyelectrolyte homopolymer limits the complexity of the possible engineered nanoparticle networks (ENPNs) that can be built. In addressing this challenge, we first found the commensurability rules between the numbers of AuNPs and poly(allylamine hydrochloride)s (PAHs). This gives rise to a well-defined valency of a AuNP which is the maximum number of PAHs that it can accommodate. We further use an engineered block copolymer, which has a conductive middle block to mediate the distance between a dimer of AuNP. We argue that by controlling the length of conductive block that is connecting the AuNPs and their respective topology, we can have ENPNs potentially adopt multiple states necessary for primitive neuromorphic computing.

Stable Millivolt Range Resistive Switching in Percolating Molybdenum Nanoparticle Networks.

To overcome the limitations of the conventional Von Neumann architecture, inspiration from the mammalian brain has led to the development of nanoscale neuromorphic networks. In the present research, molybdenum nanoparticles (NPs), which were produced by means of gas phase condensation based on magnetron sputtering, are shown to be the constituents of electrically percolating networks that exhibit stable, complex, neuron-like spiking behavior at low potentials in the millivolt range, satisfying well the requirement of low energy consumption. Characterization of the NPs using both scanning electron microscopy and scanning transmission electron microscopy revealed not only pristine shape, size, and density control of Mo NPs but also a preliminary proof of the working mechanism behind the spiking behavior due to filament formations. Furthermore, electrostatics COMSOL Multiphysics simulations of the morphology of the NPs provided evidence that the stable switching is due to the as-deposited stellate Mo NPs creating high electric field strengths, while keeping them separated, not seen before in other percolating networks based on spherical NPs. Hence, our results show the working mechanism behind switching in percolating Mo NP networks and show that they are very promising for realistic neuromorphic systems.

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.

Multifunctional enhancement strategy for MXene-based fire alarm sensors via interlocked “brick/mortar” structure of dimension-hybrid nanoparticle system

MXene-based fire-responsive composites are promising candidates for next-generation fire alarm sensors with high sensitivity and good mechanical properties. However, the low dispersibility and inherent thermal oxidation of MXene combined with the unsuitable spatial scale of polymer particles in hybrid systems impede the rapid response and alarm duration when suffering a fire. Herein, a strategy based on an interlocked “brick/mortar” structure was proposed for integrating faster response, high sensitivity, and longer duration in flames via a dimension-hybrid nanoparticle network, which was assembled by 2D MXene, 1D cellulose nanocrystals (CNCs), and ammonium polyphosphate (APP). The CNCs with a high aspect ratio of 114 and close length to MXene assisted uniform dispersion of MXene and coupled with APP impeding thermal oxidation of MXene. This assembled MXene/CNC@APP sensor can quickly trigger an alarm signal via substance conversion from insulating to conductive within 1.79 s when suffering a high thermal of 260 °C from fire, thus sensitive fire identification and rapid response were obtained. This MXene/CNC@APP film also maintained good mechanical performance with a tensile strength of 52.5 MPa and elongation at a break of 7.6%, which combined excellent flame retardancy leading to a continuous fire alarm signal over 442 s of the sensor in the fire. This multifunctional integrated enhancement strategy for MXene-based sensors may be conducive to the upgrade of fire alarm systems for better safeguarding of life and property.

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.

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.

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.

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.