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The rapid progression of the COVID‐19 pandemic revealed an inability to meet increased demand for N95 respirators. These respirators are designed to be used once and disposed, but throughout the pandemic, there was a need for their decontamination and reuse. This research investigates the effect of various decontamination methods on the chemical and mechanical properties of N95 mask straps made of natural rubber to explore how these straps change after decontamination and what materials characterization techniques are well‐suited to evaluate these changes. Using results from ozone decontamination, tensile testing of mask strap assemblies is identified as the most reliable way to quantify changes in strap properties with decontamination and reuse when compared to other analytical techniques. Additionally, visible strap degradation often precedes both strap failure and material property changes and can be a reasonable indicator to discontinue use. Aside from ozone, decontamination with other methods such as heat and UV light appears to be less damaging to the tested materials. Beyond the specific results presented, this study provides insight on testing strategies that can be employed to move forward with evaluating new materials and decontamination methods for use in future pandemics or in more resource‐limited regions. 

Vat photopolymerization (VP) additive manufacturing involves selectively curing low‐viscosity photopolymers via exposure to ultraviolet light in a layer‐wise fashion. Dispersing filler materials in the photopolymer enables tailored end‐use properties, but also increases the viscosity and the timescale associated with interparticle network structural recovery postshear. These rheological properties influence self‐leveling and recoating of the liquid photopolymer mixture during VP. Herein, viscosity of photopolymer and rigid spherical glass microparticles (filler) is experimentally determined as a function of filler fraction, filler size distribution (mono‐ and polydisperse), shear rate, and temperature, which are important VP process parameters. Employing existing viscosity models for mono‐ and polydisperse polymer mixtures demonstrates that particle–particle interactions and the formation of nonspherical clusters of particles strongly affect the viscosity of both monodisperse and polydisperse mixtures with particle volume fractions > 0.05 due to agglomeration/deagglomeration of clusters at elevated shear rates. Consequently, unmodified viscosity models, which assume uniformly dispersed, rigid, spherical particles, are applicable only for mixtures with particle volume fractions < 0.05. It is shown that modifying model parameters such as the fluidity limit and intrinsic viscosity, which explicitly account for nonspherical clusters of particles, improves agreement between viscosity models and experiments, in particular when using a fractal approach. 

Abstract Recent analyses have found waves of neural activity traveling across entire visual cortical areas in awake animals. These traveling waves modulate the excitability of local networks and perceptual sensitivity. The general computational role of these spatiotemporal patterns in the visual system, however, remains unclear. Here, we hypothesize that traveling waves endow the visual system with the capacity to predict complex and naturalistic inputs. We present a network model whose connections can be rapidly and efficiently trained to predict individual natural movies. After training, a few input frames from a movie trigger complex wave patterns that drive accurate predictions many frames into the future solely from the network’s connections. When the recurrent connections that drive waves are randomly shuffled, both traveling waves and the ability to predict are eliminated. These results suggest traveling waves may play an essential computational role in the visual system by embedding continuous spatiotemporal structures over spatial maps. 

The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well-studied cortical regions. Further, the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike–field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs, but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical. 

Morphology and function of the dorsolateral prefrontal cortex (dlPFC), and corresponding working memory performance, are affected early in the aging process, but nearly half of aged individuals are spared of working memory deficits. Translationally relevant model systems are critical for determining the neurobiological drivers of this variability. The common marmoset (Callithrix jacchus) is advantageous as a model for these investigations because, as a non-human primate, marmosets have a clearly defined dlPFC that enables measurement of prefrontal-dependent cognitive functions, and their short (∼10 year) lifespan facilitates longitudinal studies of aging. Previously, we characterized working memory capacity in a cohort of marmosets that collectively covered the lifespan, and found age-related working memory impairment. We also found a remarkable degree of heterogeneity in performance, similar to that found in humans. Here, we tested the hypothesis that changes to synaptic ultrastructure that affect synaptic efficacy stratify marmosets that age with cognitive impairment from those that age without cognitive impairment. We utilized electron microscopy to visualize synapses in the marmoset dlPFC and measured the sizes of boutons, presynaptic mitochondria, and synapses. We found that coordinated scaling of the sizes of synapses and mitochondria with their associated boutons is essential for intact working memory performance in aged marmosets. Further, lack of synaptic scaling, due to a remarkable failure of synaptic mitochondria to scale with presynaptic boutons, selectively underlies age-related working memory impairment. We posit that this decoupling results in mismatched energy supply and demand, leading to impaired synaptic transmission. We also found that aged marmosets have fewer synapses in dlPFC than young, though the severity of synapse loss did not predict whether aging occurred with or without cognitive impairment. This work identifies a novel mechanism of synapse dysfunction that stratifies marmosets that age with cognitive impairment from those that age without cognitive impairment. The process by which synaptic scaling is regulated is yet unknown and warrants future investigation. 

Printable feedstocks that can produce lightweight, robust, and ductile structures with tunable and switchable conductivity are of considerable interest for numerous application spaces. Combining the specific properties of commodity thermoplastics with the unique electrical and redox properties of conducting polymers (CPs) presents new opportunities for the field of printed (bio)electronics. Here, we report on the direct ink write (DIW) printing of ink formulations based on polyaniline-dinonylnaphthalene sulfonic acid (PANI-DNNSA), which has been synthesized in bulk quantities (∼400 g). DNNSA imparts solubility to PANI up to 50 mg mL −1 , which allows the use of various additives to tune the rheological behavior of the inks without significantly compromising the electrical properties of the printed structures, which reach conductivities in the range of <10 −7 –10 0 S cm −1 as a function of ink formulation and post treatment used. Fumed silica (FS) and ultra-high molecular weight polystyrene (UHMW-PS) additives are leveraged to endow printability and shape retention to inks, as well as to compare the use of traditional rheological modifiers with commodity thermoplastics on CP feedstocks for tailored DIW printing. We show that the incorporation of UHMW-PS into these ink formulations is critical for obtaining high crack resistance in printed structures. This work serves as a guide for future ink designs of CPs with commodity thermoplastics and their subsequent DIW printing to yield conductive architectures and devices for various applications. 

Abstract Recent analyses have found waves of neural activity traveling across entire visual cortical areas in awake animals. These traveling waves modulate excitability of local networks and perceptual sensitivity. The general computational role for these spatiotemporal patterns in the visual system, however, remains unclear. Here, we hypothesize that traveling waves endow the brain with the capacity to predict complex and naturalistic visual inputs. We present a new network model whose connections can be rapidly and efficiently trained to predict natural movies. After training, a few input frames from a movie trigger complex wave patterns that drive accurate predictions many frames into the future, solely from the network’s connections. When the recurrent connections that drive waves are randomly shuffled, both traveling waves and the ability to predict are eliminated. These results show traveling waves could play an essential computational role in the visual system by embedding continuous spatiotemporal structures over spatial maps.