Number sense, the ability to estimate numerosity, is observed in naïve animals, but how this cognitive function emerges in the brain remains unclear. Here, using an artificial deep neural network that models the ventral visual stream of the brain, we show that number-selective neurons can arise spontaneously, even in the complete absence of learning. We also show that the responses of these neurons can induce the abstract number sense, the ability to discriminate numerosity independent of low-level visual cues. We found number tuning in a randomly initialized network originating from a combination of monotonically decreasing and increasing neuronal activities, which emerges spontaneously from the statistical properties of bottom-up projections. We confirmed that the responses of these number-selective neurons show the single- and multineuron characteristics observed in the brain and enable the network to perform number comparison tasks. These findings provide insight into the origin of innate cognitive functions.

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Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.

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This study introduced hydrophobic silica nanoparticles (SiNPs) into an interface of aqueous and hydrate-forming oil phases and analyzed the inhibition of hydrate crystal growth after seeding the hydrate slurry. The hydrate inhibition performance was quantitatively identified by micro-differential scanning calorimetry (micro-DSC) experiments. Through the addition of 1.0 wt% of SiNPs into the water-oil interface, the hydrate crystal growth only occurred around the seeding position of cyclopentane (CP) hydrate slurry, and the growth of hydrate crystals was retarded. Upon a further increase in the SiNP concentration up to 2.0 wt%, the SiNP-laden interface completely prevented hydrate growth. We observed a hollow conical shape of hydrate crystals with 0.0 and 1.0 wt% of SiNPs, respectively, but the size and shape of the conical crystals was shrunken at 1.0 wt% of silica nanoparticles. However, the conical shape did not appear with an increased nanoparticle concentration of 2 wt%. These findings can provide insight into hydrate inhibition in oil and gas delivery lines, possibly with nanoparticles.

This study addresses the effect of hydrophobic silica nanoparticles (SiNPs) on methane hydrate formation in water-in-decane emulsions. Even though the new concept of hydrophobic particle inhibitors has been proposed in the flat interface of oil and bulk water phases, the mechanism of the particle inhibition has not been investigated for a hydrate-forming emulsion system. Numerous experiments with various amounts of SiNPs applied to water-in-decane emulsions and different temperature and pressure profiles have verified how the nanoparticles interact at the interface for hydrate inhibition or promotion. At lower concentrations of SiNPs, they act as a hydrate inhibitor. However, as the amount of SiNPs increases beyond a certain concentration (0.5 wt %), the formation rate anomalously increases because the oil film of the emulsions containing methane becomes thicker with the increasing amount of SiNPs, and water molecules form capillary bridges along the nanoparticle alignment. Our experimental results and suggested mechanisms provide insights into the interfacial behaviors of SiNPs for preventing hydrate formation in water-in-oil emulsions.

This study introduces an "anti-adhesive force" at the interface of solid hydrate and liquid solution phases. The force was induced by the presence of hydrophobic silica nanoparticles or one of the common anti-agglomerants (AAs), sorbitan monolaurate (Span 20), at the interface. The anti-adhesive force, which is defined as the maximum pushing force that does not induce the formation of a capillary bridge between the cyclopentane (CP) hydrate particle and the aqueous solution, was measured using a microbalance. Both hydrophobic silica nanoparticles and Span 20 can inhibit adhesion between the CP hydrate probe and the aqueous phase because silica nanoparticles have an aggregative property at the interface, and Span 20 enables the hydrate surface to be wetted with oil. Adding water-soluble sodium dodecyl sulfate (SDS) to the nanoparticle system cannot affect the aggregative property or the distribution of silica nanoparticles at the interface and, thus, cannot change the anti-adhesive effect. However, the combined system of Span 20 and SDS dramatically reduces the interfacial tension: emulsion drops were formed at the interface without any energy input and were adsorbed on the CP hydrate surface, which can cause the growth of hydrate particles. Silica nanoparticles have a good anti-adhesive performance with a relatively smaller dosage and are less influenced by the presence of molecular surfactants; consequently, these nanoparticles may have a good potential for hydrate inhibition as AAs.

The inhibition effects of activated carbon particles on hydrate growth at water and oil interfaces was studied. Significantly reduced hydrate growth was seen in the presence of 0.5 wt% AC particles.