Topological Considerations in Biomolecular Condensation

Biomolecular condensation and phase separation are increasingly understood to play crucial roles in cellular compartmentalization and spatiotemporal regulation of cell machinery implicated in function and pathology. A key aspect of current research is to gain insight into the underlying physical mechanisms of these processes. Accordingly, concepts of soft matter and polymer physics, the thermodynamics of mixing, and material science have been utilized for understanding condensation mechanisms of multivalent macromolecules resulting in viscoelastic mesoscopic supramolecular assemblies. Here, we focus on two topological concepts that have recently been providing key mechanistic understanding in the field. First, we will discuss how percolation provides a network-topology-related framework that offers an interesting paradigm to understand the complex networking of dense 'connected' condensate structures and, therefore, their phase behavior. Second, we will discuss the idea of entanglement as another topological concept that has deep roots in polymer physics and important implications for biomolecular condensates. We will first review some historical developments and fundamentals of these concepts, then we will discuss current advancements and recent examples. Our discussion ends with a few open questions and the challenges to address them, hinting at unveiling fresh possibilities for the modification of existing knowledge as well as the development of new concepts relevant to condensate science.

Wound Healing Properties of Pelargonium Graveolens L'Hér Extract Lipogel: In-Vivo Evaluation in an Animal Burn Model

BACKGROUND

Pelargonium graveolens L'Hér has traditionally been used to reduce skin inflammation, and recent studies have confirmed antioxidant compounds in the plant's extract. The present study aimed to prepare a lipogel formulation from P. graveolens hydroalcoholic extract and evaluate its efficacy on the wound healing process in an animal model.

MATERIAL AND METHODS

The aerial part extract of P. graveolens was prepared through percolation. Additionally, plastibase was prepared by mixing 5% of low-molecular-weight polyethylene with hot mineral oil (130°C). The extract (5%) was levigated in the mineral oil (5-15%) and dispersed in the cooled plastibase. The physical properties of the lipogel, thermal stability, and microbial limits were tested. Further, the effect of the lipogel in the wound healing rate was examined among male Wistar rats, and skin tissue samples were assessed histologically.

RESULTS AND DISCUSSION

The results represented the best rheological and thermal stability characteristics in the formulation with 5% mineral oil (as the levigator). The lipogel-treated group had the least burn area compared to the silver sulfadiazine and negative control groups (p<0.05). The microscopic examination of tissue samples revealed increased collagen fiber production and maturation and significantly also faster epithelial repair among lipogel-treated rats than in the other two groups(p<0.05).

CONCLUSION

The results indicated the significant therapeutic effects of P. graveolens lipogelon burn healing. The suitable physicochemical properties and the low lipogel production cost facilitate further scale-up studies.

Predicting alveolar ventilation heterogeneity in pulmonary fibrosis using a non-uniform polyhedral spring network model

Pulmonary Fibrosis (PF) is a deadly disease that has limited treatment options and is caused by excessive deposition and cross-linking of collagen leading to stiffening of the lung parenchyma. The link between lung structure and function in PF remains poorly understood, although its spatially heterogeneous nature has important implications for alveolar ventilation. Computational models of lung parenchyma utilize uniform arrays of space-filling shapes to represent individual alveoli, but have inherent anisotropy, whereas actual lung tissue is isotropic on average. We developed a novel Voronoi-based 3D spring network model of the lung parenchyma, the Amorphous Network, that exhibits more 2D and 3D similarity to lung geometry than regular polyhedral networks. In contrast to regular networks that show anisotropic force transmission, the structural randomness in the Amorphous Network dissipates this anisotropy with important implications for mechanotransduction. We then added agents to the network that were allowed to carry out a random walk to mimic the migratory behavior of fibroblasts. To model progressive fibrosis, agents were moved around the network and increased the stiffness of springs along their path. Agents migrated at various path lengths until a certain percentage of the network was stiffened. Alveolar ventilation heterogeneity increased with both percent of the network stiffened, and walk length of the agents, until the percolation threshold was reached. The bulk modulus of the network also increased with both percent of network stiffened and path length. This model thus represents a step forward in the creation of physiologically accurate computational models of lung tissue disease.

Core-Shell Engineering of Conductive Fillers toward Enhanced Dielectric Properties: A Universal Polarization Mechanism in Polymer Conductor Composites

Flexible dielectric and electronic materials with high dielectric constant (k) and low loss are constantly pursued. Encapsulation of conductive fillers with insulating shells represents a promising approach, and has attracted substantial research efforts. However, progress is greatly impeded due to the lack of a fundamental understanding of the polarization mechanism. In this work, a series of core-shell polymer composites is studied, and the correlation between macroscopic dielectric properties (across entire composites) and microscopic polarization (around single fillers) is investigated. It is revealed that the polarization in polymer conductor composites is determined by electron transport across multiple neighboring conductive fillers-a domain-type polarization. The formation of a core-shell filler structure affects the dielectric properties of tpolymer composites by essentially modifying the filler-cluster size. Based on this understanding, a novel percolative composite is prepared with higher-than-normal filler concentration and optimized shell's electrical resistivity. The developed composite shows both high-k due to enlarged cluster size and low loss due to restrained charge transport simultaneously, which cannot be achieved in traditional percolative composites or via simple core-shell filler design. The revealed polarization mechanism and the optimization strategy for core-shell fillers provide critical guidance and a new paradigm, for developing advanced polymer dielectrics with promising property sets.

Dewetting Process of Silver Thin Films and Its Application on Percolative Pressure Sensors with High Sensitivity

This work reports on an innovative dewetting process of silver thin films to realize percolative nanoparticle arrays (NPAs) and demonstrates its application on highly sensitive pressure sensors. The dewetting process, which is a simple and promising technique, synthesizes NPAs by breaking the as-deposited metal film into randomly distributed islands. The NPA properties, such as the mean particle size and the spacing between adjacent particles, can be easily tailored by controlling the dewetting temperature, as well as the as-deposited metal-film thickness. The fabricated NPAs were employed to develop gauge pressure sensors with high sensitivity. The proposed sensor consists of a sealed reference-pressure cavity, a polyimide (PI) membrane patterned with an interdigital electrode pair (IEP), and a silver NPA deposited on the IEP and the PI membrane. The operational principle of the device is based on the NPA percolation effect with deformation-dependence. The fabricated sensors exhibit rapid responses and excellent linearity at around 1 atm. The maximum sensitivity is about 0.1 kPa^-1. The advantages of the proposed devices include ultrahigh sensitivity, a reduced thermal disturbance, and a decreased power consumption. A practical application of this pressure sensor with high resolution was demonstrated by using it to measure the relative floor height of a building.

Computational Micromechanics Investigation of Percolation and Effective Electro-Mechanical Properties of Carbon Nanotube/Polymer Nanocomposites using Stochastically Generated Realizations: Effects of Orientation and Waviness

The electrical and mechanical properties of carbon nanotube/polymer nanocomposites depend strongly upon several factors such as CNT volume fraction, CNT alignment, CNT dispersion and CNT waviness among others. This work focuses on obtaining estimates and distribution for the effective electrical conductivity, elastic constants and piezoresistive properties as a function of these factors using a stochastic approach with numerous CNT/polymer realizations coupled with parallel computation. Additionally, electrical percolation volume fraction and percolation transitional behavior is also studied. The effective estimates and percolation values were found to be in good agreement with experimental works in the literature. It was found that with increasing CNT volume fraction, the mechanical properties improved. However, due to the interaction of CNTs with one another through electrical tunneling, the conductivity and piezoresistivity properties evolved in a more complex manner. While the degree of alignment played a strong role in the effective properties making them anisotropic, the effect of waviness was found to be insubstantial.

Percolation Effects in Mixed Matrix Membranes with Embedded Carbon Nanotubes

Polymeric membranes with embedded nanoparticles, e.g., nanotubes, show a significant increase in permeability of the target component while maintaining selectivity. However, the question of the reasons for this behavior of the composite membrane has not been unequivocally answered to date. In the present work, based on experimental data on the permeability of polymer membranes based on Poly(vinyl trimethylsilane) (PVTMS) with embedded CNTs, an approach to explain the abnormal behavior of such composite membranes is proposed. The presented model considered the mass transfer of gases and liquids through polymeric membranes with embedded CNTs as a parallel transport of gases through the polymeric matrix and a "percolation" cluster-bound regions around the embedded CNTs. The proposed algorithm for modeling parameters of a percolation cluster of embedded tubular particles takes into account an agglomeration and makes it possible to describe the threshold increase and subsequent decrease permeability with increasing concentration of embedded particles. The numerical simulation of such structures showed: an increase in the particle length leads to a decrease in the percolation concentration in a matrix of finite size, the power of the percolation cluster decreases significantly, but the combination of these effects leads to a decrease in the influence of the introduced particles on the properties of the matrix in the vicinity of the percolation threshold; an increase in the concentration of embedded particles leads to an increase in the probability of the formation of agglomerates and the characteristic size of the elements that make up the percolation cluster, the influence of individual particles decreases and the characteristics of the percolation transition determine the ratio of the sizes of agglomerates and matrix; and an increase in the lateral linear dimensions of the matrix leads to a nonlinear decrease in the proportion of the matrix, which is affected by the introduced particles, and the transport characteristics of such MMMs deteriorate.

Total cannabidiol (CBD) concentrations and yields from traditional extraction methods: Percolation vs. maceration

Medicinal plants have been valued for many generations due to their biosynthetic advantages generating pharmacologically active molecules. This is especially the case when it comes to cannabinoids from Cannabis. In these experiments we mimicked typical herbal home extractions and measured the yield of total decarboxylated CBD ("total CBD") from percolations and macerations done at the common duration of 2 weeks in duplicate independent extractions. Analysis was performed by GC-FID on triplicate samples from each extraction. Results demonstrated a significant extraction superiority of percolation over maceration. Percolation extracted 80.1% of the total CBD in the hemp biomass as compared to the 2-week time point at 63.5% recovery. Our results demonstrate a significant increase in total CBD yield from percolation, as compared to maceration. Highest solvent recovery was also through percolation, but overall solvent recovery was fairly consistent with the maceration method, after pressing. Under these conditions of extracting lipophilic cannabidiol in 95% ethanol, these data demonstrate that percolation is significantly superior to maceration in total CBD yield. These observations will likely apply to the extraction of lipophilic constituents from other herbs and botanical medicines.

Modeling of Electrical Conductivity for Polymer-Carbon Nanofiber Systems

There is not a simple model for predicting the electrical conductivity of carbon nanofiber (CNF)-polymer composites. In this manuscript, a model is proposed to predict the conductivity of CNF-filled composites. The developed model assumes the roles of CNF volume fraction, CNF dimensions, percolation onset, interphase thickness, CNF waviness, tunneling length among nanoparticles, and the fraction of the networked CNF. The outputs of the developed model correctly agree with the experimentally measured conductivity of several samples. Additionally, parametric analyses confirm the acceptable impacts of main factors on the conductivity of composites. A higher conductivity is achieved by smaller waviness and lower radius of CNFs, lower percolation onset, less tunnel distance, and higher levels of interphase depth and fraction of percolated CNFs in the nanocomposite. The maximum conductivity is obtained at 2.37 S/m by the highest volume fraction and length of CNFs.

Conformational Heterogeneity and Interchain Percolation Revealed in an Amorphous Conjugated Polymer

Conjugated polymers are employed in a variety of application areas due to their bright fluorescence and strong biocompatibility. However, understanding the structure of amorphous conjugated polymers on the nanoscale is extremely challenging compared to their related crystalline phases. Using a bespoke classical force field, we study amorphous poly(9,9-di-n-octylfluorene-alt-benzothiadiazole) (F8BT) with molecular dynamics simulations to investigate the role that its nanoscale structure plays in controlling its emergent (and all-important) optical properties. Notably, we show that a giant percolating cluster exists within amorphous F8BT, which has ramifications in understanding the nature of interchain species that drive the quantum yield reduction and bathochromic shift observed in conjugated polymer-based devices and nanostructures. We also show that distinct conformations can be unravelled from within the disordered structure of amorphous F8BT using a two-stage machine learning protocol, highlighting a link between molecular conformation and ring stacking propensity. This work provides predictive understanding by which to enhance the optical properties of next-generation conjugated polymer-based devices and materials by rational, simulation-led design principles.

Effect of Impurity Scattering on Percolation of Bosonic Islands and Superconductivity in Fe Implanted NbN Thin Films

A reentrant temperature dependence of the thermoresistivity ρxx(T) between an onset local superconducting ordering temperature Tloconset and a global superconducting transition at T=Tglooffset has been reported in disordered conventional 3-dimensional (3D) superconductors. The disorder of these superconductors is a result of either an extrinsic granularity due to grain boundaries, or of an intrinsic granularity ascribable to the electronic disorder originating from impurity dopants. Here, the effects of Fe doping on the electronic properties of sputtered NbN layers with a nominal thickness of 100 nm are studied by means of low-T/high-μ0H magnetotransport measurements. The doping of NbN is achieved via implantation of 35 keV Fe ions. In the as-grown NbN films, a local onset of superconductivity at Tloconset=15.72K is found, while the global superconducting ordering is achieved at Tglooffset=15.05K, with a normal state resistivity ρxx=22μΩ·cm. Moreover, upon Fe doping of NbN, ρxx=40μΩ·cm is estimated, while Tloconset and Tglooffset are measured to be 15.1 K and 13.5 K, respectively. In Fe:NbN, the intrinsic granularity leads to the emergence of a bosonic insulator state and the normal-metal-to-superconductor transition is accompanied by six different electronic phases characterized by a N-shaped T dependence of ρxx(T). The bosonic insulator state in a s-wave conventional superconductor doped with dilute magnetic impurities is predicted to represent a workbench for emergent phenomena, such as gapless superconductivity, triplet Cooper pairings and topological odd frequency superconductivity.

High conductivity Sepia melanin ink films for environmentally benign printed electronics

Melanins (from the Greek μέλας, mélas, black) are bio-pigments ubiquitous in flora and fauna. Eumelanin is an insoluble brown-black type of melanin, found in vertebrates and invertebrates alike, among which Sepia (cuttlefish) is noteworthy. Sepia melanin is a type of bio-sourced eumelanin that can readily be extracted from the ink sac of cuttlefish. Eumelanin features broadband optical absorption, metal-binding affinity and antioxidative and radical-scavenging properties. It is a prototype of benign material for sustainable organic electronics technologies. Here, we report on an electronic conductivity as high as 10^-3 S cm^-1 in flexographically printed Sepia melanin films; such values for the conductivity are typical for well-established high-performance organic electronic polymers but quite uncommon for bio-sourced organic materials. Our studies show the potential of bio-sourced materials for emerging electronic technologies with low human- and eco-toxicity.

Quantifying the environmental limits to fire spread in grassy ecosystems

Modeling fire spread as an infection process is intuitive: An ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce nonlinear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires (n = 1,131) in grassy ecosystems across a precipitation gradient (496 to 1,442 mm mean annual precipitation) and evaluating how these scaled regionally (across 533 sites) and across time (1989 to 2012 and 2016 to 2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate nonlinear fire spread thresholds but that linear approximations may sufficiently capture medium-term trends under a stationary climate.

Nature-Inspired Polyethylenimine-Modified Calcium Alginate Blended Waterborne Polyurethane Graded Functional Materials for Multiple Water Purification

In recent years, natural disasters such as hurricanes and floods have become more frequent, which usually leads to the pollution of drinking water. Drinking contaminated water may cause public health emergencies. The demand for healthy drinking water in disaster-affected areas is huge and urgent. Therefore, it is necessary to develop a simple water treatment technology suitable for emergencies. Inspired by nature, a fractional spray method was used to prepare graded purification material under mild conditions. The material consists of a calcium alginate isolation layer and a functional layer composed of calcium alginate, polyethylenimine, and water-based polyurethane, which can purify complex pollutants in water such as heavy metals, oils, pathogens, and micro/nano plastics through percolation. It does not require additional energy and can purify polluted water only under gravity. A disposable paper cup model was also designed, which can be used to obtain purified water by immersing in polluted water directly without other filtering devices. The test report shows that the water obtained from the paper cup was deeply purified. This design makes the material user-friendly and has the potential as a strategic material. This discovery can effectively improve the safety of drinking water after disasters and improve people's quality of life.

Thermal Percolation in Well-Defined Nanocomposite Thin Films

Thermal percolation in polymer nanocomposites─the rapid increase in thermal transport due to the formation of networks among fillers─is the subject of great interest in thermal management ranging from general utility in multifunctional nanocomposites to high-conductivity applications such as thermal interface materials. However, It remains a challenging subject encompassing both experimental and modeling hurdles. Successful reports of thermal percolation are exclusively found in high-aspect-ratio, conductive fillers such as graphene, albeit at filler loadings significantly higher than the electrical percolation threshold. This anomaly was attributed to the lower filler-matrix thermal conductivity contrast ratio k_f/k_m ∼10⁴ compared to electrical conductivity ∼10¹²-10¹⁶. In a randomly dispersed composite, the effect of a low contrast ratio is further accentuated by uncertainties in the morphology of the percolating network and presence of other phases such as disconnected aggregates and colloidal dispersions. Thus, the general properties of percolating networks are convoluted as they lack a defined structure. In contrast, a prototypical system with controllable nanofiller placement enables the elucidation of structure-property relations such as filler size, loading, and assembly. Using self-assembled nanocomposites with a controlled 1,2,3-dimension nanoparticle (NP) arrangement, we demonstrate that thermal percolation can be achieved in spite of using spherical, nonconductive fillers (k_f/k_m ∼60) at a low volume fraction (9 vol %). We observe that the effects of volume fraction, interfacial thermal resistance, and filler conductivity on thermal conductivity depart from effective medium approximations. Most notably, contrast ratio plays a minor role in thermal percolation above k_f/k_m ∼60─a common range for semiconducting nanoparticles/polymer ratios. Our findings bring new perspectives and insights to thermal percolation in nanocomposites, where the limits in contrast ratio, interfacial thermal conductance, and filler size are established.

SARS-CoV-2 Detection using Colorimetric Plasmonic Sensors: A Proof-of-Concept Computational Study

Traditional molecular techniques for SARS-CoV-2 viral detection are time-consuming and can exhibit a high probability of false negatives. In this work, we present a computational study of SARS-CoV-2 detection using plasmonic gold nanoparticles. The resonance wavelength of a SARS-CoV-2 virus was recently estimated to be in the near-infrared region. By engineering gold nanospheres to specifically bind with the outer surface of the SARS-CoV-2 virus, the resonance frequency can be shifted to the visible range (380 nm - 700 nm). Moreover, we show that broadband absorption will emerge in the visible spectrum when the virus is partially covered with gold nanoparticles at a specific coverage percentage. This broadband absorption can be used to guide the development of an efficient and accurate colorimetric plasmon sensor for COVID-19 detection. Our observation also suggests that this technique is unaffected by the number of protein spikes present on the virus outer surface, hence can pave a potential path for a label-free COVID-19 diagnostic tool independent of the number of protein spikes.

Indirect influence in social networks as an induced percolation phenomenon

Increasing empirical evidence in diverse social and ecological systems has shown that indirect interactions play a pivotal role in shaping systems’ dynamical behavior. Our empirical study on collaboration networks of scientists further reveals that an indirect effect can dominate over direct influence in behavioral spreading. However, almost all models in existence focus on direct interactions, and the general impact of indirect interactions has not been studied. We propose a new percolation process, termed induced percolation, to characterize indirect interactions and find that indirect interactions raise a plethora of new phenomena, including the wide range of possible phase transitions. Such an indirect mechanism leads to very different spreading outcomes from that of direct influences.

Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.

Impedance Spectroscopy of Hierarchical Porous Nanomaterials Based on por-Si, por-Si Incorporated by Ni and Metal Oxides for Gas Sensors

Approaches are being developed to create composite materials with a fractal-percolation structure based on intercalated porous matrices to increase the sensitivity of adsorption gas sensors. Porous silicon, nickel-containing porous silicon, and zinc oxide have been synthesized as materials for such structures. Using the impedance spectroscopy method, it has been shown that the obtained materials demonstrate high sensitivity to organic solvent vapors and can be used in gas sensors. A model is proposed that explains the high sensitivity and inductive nature of the impedance at low frequencies, considering the structural features and fractal-percolation properties of the obtained oxide materials.

On Structural Rearrangements during the Vitrification of Molten Copper

We utilise displacement analysis of Cu-atoms between the chemical bond-centred Voronoi polyhedrons to reveal structural changes at the glass transition. We confirm that the disordered congruent bond lattice of Cu loses its rigidity above the glass transition temperature (T_g) in line with Kantor–Webman theorem due to percolation via configurons (broken Cu-Cu chemical bonds). We reveal that the amorphous Cu has the T_g = 794 ± 10 K at the cooling rate q = 1 × 10¹³ K/s and that the determination of T_g based on analysis of first sharp diffraction minimum (FDSM) is sharper compared with classical Wendt–Abraham empirical criterion.

Comparing Percolation and Alignment of Cellulose Nanocrystals for the Reinforcement of Polyurethane Nanocomposites

The reinforcement of polymer nanocomposites can be achieved through alignment or percolation of cellulose nanocrystals (CNCs). Here, we compare the efficacy of these reinforcement mechanisms in thermoplastic polyurethane (PU) elastomer nanocomposites containing thermally stable cotton CNCs. CNC alignment was achieved by melt spinning nanocomposite fibers, while a percolating CNC network was generated by solvent casting nanocomposite films with CNC contents up to 20 wt %. While in films both the CNCs and the PU matrix were entirely isotropic at all concentrations as confirmed by wide-angle X-ray scattering and birefringence analysis, the CNCs in the fibers exhibited a preferential orientation, which improved with increasing CNC concentration. Increasing the CNC concentration in the fibers reduces, however, the alignment of the PU chains, resulting in an entirely isotropic PU matrix at high CNC contents. The mechanical properties of films and fibers were evaluated using stress-strain measurements. Nanocomposite fibers with low CNC content exhibited superior stiffness, extensibility, and strength compared to the films, while the films displayed superior mechanical properties at high CNC concentrations. These findings are rationalized using common semiempirical models describing the reinforcing effects of CNC alignment in fibers (Halpin-Tsai) and CNC percolation in films (percolation model). The formation of a percolating CNC network leads to a stronger reinforcement than CNC alignment, as the reinforcing effect of the latter is limited by the comparably low aspect ratio of CNCs extracted from cotton. As a consequence, above the percolation threshold for cotton CNCs, isotropic nanocomposite PU films show a higher stiffness than aligned nanocomposite PU fibers.

Magnetic dilution of a honeycomb lattice XY magnet CoTiO3

We report our study of cobalt (II) titanate, CoTiO₃, in which magnetic Co ions are replaced by non-magnetic ions. The antiferromagnetic ordering transition of CoTiO₃around 37 K is described with ferromagnetic honeycomb layers coupled antiferromagnetically along the crystallographicc-direction. The effect of magnetic dilution on the Néel temperature of this material is investigated through the doping of Zn²⁺and Mg²⁺in place of Co²⁺for various dilution levels up tox+y= 0.46 in Co_1-x-yZn_xMg_yTiO₃. Single phase polycrystalline samples have been synthesized and their structural and magnetic properties have been examined. A linear relation between dilution and the Néel temperature is observed over a wide doping range. A linear extrapolation would suggest that the required dilution level to suppress magnetic order is aroundx+y∼ 0.74, well beyond the classical percolation threshold. The implication of this observation for microscopic models for describing CoTiO₃is discussed.

Critical Role of the Subways in the Initial Spread of SARS-CoV-2 in New York City

We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1–2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1–8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1–8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to three connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots.

Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation

There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic spiking behavior that is strikingly similar to that observed in biological neurons. The spiking rate can be controlled by the input stimulus, similar to "rate coding" in biology, and the distributions of times between events are log-normal, providing insights into the atomic-scale spiking mechanism. The stochasticity of the spiking behavior is then used for true random number generation, and the high quality of the generated random bit-streams is demonstrated, opening up promising routes toward integration of neuromorphic computing with secure information processing.

Silver Nanowire Networks: Ways to Enhance Their Physical Properties and Stability

Silver nanowire (AgNW) networks have been intensively investigated in recent years. Thanks to their attractive physical properties in terms of optical transparency and electrical conductivity, as well as their mechanical performance, AgNW networks are promising transparent electrodes (TE) for several devices, such as solar cells, transparent heaters, touch screens or light-emitting devices. However, morphological instabilities, low adhesion to the substrate, surface roughness and ageing issues may limit their broader use and need to be tackled for a successful performance and long working lifetime. The aim of the present work is to highlight efficient strategies to optimize the physical properties of AgNW networks. In order to situate our work in relation to existing literature, we briefly reported recent studies which investigated physical properties of AgNW networks. First, we investigated the optimization of optical transparency and electrical conductivity by comparing two types of AgNWs with different morphologies, including PVP layer and AgNW dimensions. In addition, their response to thermal treatment was deeply investigated. Then, zinc oxide (ZnO) and tin oxide (SnO2) protective films deposited by Atmospheric Pressure Spatial Atomic Layer Deposition (AP-SALD) were compared for one type of AgNW. We clearly demonstrated that coating AgNW networks with these thin oxide layers is an efficient approach to enhance the morphological stability of AgNWs when subjected to thermal stress. Finally, we discussed the main future challenges linked with AgNW networks optimization processes.

In-line monitoring and endpoint determination of percolation process of herbal medicine using ultraviolet spectroscopy combined with convolutional neural network

OBJECTIVES

As a common step in the herbal medicine production process, percolation usually lacks effective process monitoring methods and is often conducted with fixed process parameters. In this study, an in-line ultraviolet (UV) spectroscopy was used for monitoring the Caulis Sinomenii percolation process.

METHODS

The spectra and concentration data of 156 percolation samples from five batches were collected. Convolutional neural networks (CNNs) were used to develop quantitative calibration models. The mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) were compared to select the proper loss function for developing the CNN models. Meanwhile, partial least square regression (PLSR) was also used to develop calibration models for performance comparison.

KEY FINDINGS

The CNN models with MAPE or MAE as the loss function could provide accurate predictions for all samples. However, CNN models adopting MSE as the loss function tended not to predict low-concentration samples accurately. The CNN models mostly achieved satisfactory results without any preprocessing techniques and surpassed PLSR models in all the performance metrics.

CONCLUSIONS

An in-line UV spectroscopy system combining the CNN algorithm was implemented to monitor the percolation process of Caulis Sinomenii. The system can accurately determine the endpoint of the percolation process.