Wisely Designed Phthalocyanine Derivative for Convenient Molecular Fabrication on a Substrate

An axial-substituted silicon phthalocyanine derivative, SiPc(OR)₂ (R = C₄H₉), that is soluble in organic solvent is conveniently synthesized. This silicon phthalocyanine derivative reacts with a hydroxyl group on a substrate and then with another phthalocyanine derivative under mild conditions. The accumulation number of the phthalocyanine molecules on the substrates is easily controlled by the immersion time. On the basis of AFM (atomic force microscopy) images, the surface of the phthalocyanine-modified glass substrate has uneven structures on the nanometer scale. ITO electrodes modified with the composition of the phthalocyanine derivative and PCBM show stable cathodic photocurrent generation upon light irradiation.

Potential Tuning of Nanoarchitectures Based on Phthalocyanine Nanopillars: Construction of Effective Photocurrent Generation Systems

Nanopillars composed of a photoresponsive phthalocyanine derivative have been conveniently fabricated using a continuous silane coupling reaction on a substrate. The chemical potentials of phthalocyanine nanopillars (PNs) are precisely controlled by changing the number of phthalocyanine derivatives on the substrate. In addition, photocurrent generation efficiencies have been strongly influenced by the number of phthalocyanine derivatives. High photocurrent conversion cells in a solid state have been obtained by the combination of PNs and a fullerene derivative.

On atmospheric loss of oxygen ions from earth through magnetospheric processes

In Earth's environment, the observed polar outflow rate for O(+) ions, the main source of oxygen above gravitational escape energy, corresponds to the loss of approximately 18% of the present-day atmospheric oxygen over 3 billion years. However, part of this apparent loss can actually be returned to the atmosphere. Examining loss rates of four escape routes with high-altitude spacecraft observations, we show that the total oxygen loss rate inferred from current knowledge is about one order of magnitude smaller than the polar O(+) outflow rate. This disagreement suggests that there may be a substantial return flux from the magnetosphere to the low-latitude ionosphere. Then the net oxygen loss over 3 billion years drops to approximately 2% of the current atmospheric oxygen content.

Cascade associative memory storing hierarchically correlated patterns with various correlations

In conventional models for storing hierarchically correlated patterns, correlations between ancestors (first-level patterns) and their descendants (second-level ones) are assumed to be uniform, so that the descendants are distributed around their ancestors with equal distances. However, this assumption might be unnatural. We believe that objects are encoded into patterns by preserving the similarity between them. In this case, descendants are distributed around their ancestors with various distances, so that the assumption is invalid and the conventional models become inapplicable. To overcome this, we propose a model CASM3 for storing hierarchically correlated patterns with various correlations. In CASM3, critical load levels vary with the descendants, and become higher with increasing correlations. Increase in load level successively destroys the memories of the descendants in descending order of their correlations. The size of the basins of attraction depends on the range of the correlations, and becomes larger as the correlation range is shifted toward lower levels.

A cascade associative memory model with a hierarchical memory structure

The introduction of a hierarchical memory structure into a cascade associative memory model for storing hierarchically correlated patterns improves the storage capacity and the size of the basins of attraction remarkably. A learning algorithm groups descendants (second-level patterns) according to their ancestors (first-level ones), and organizes the memory structure in a weight matrix where the groups are memorized separately. The weight matrix is, thus, in the form of a pile of covariance matrices, each of which is responsible for recalling only the descendants of each ancestor. Putting it simply, the model is multiplex associative memory. The recalling process proceeds as follows: the model first recalls the ancestor of a target descendant. Then, the dynamics with dynamic threshold combines the ancestor and the weight matrix to activate the covariance matrix for recalling only the descendants of the ancestor. This mechanism suppresses the cross-talk noise generated by the descendants of the other ancestors, and the recalling ability is enhanced.

A neural network model for visual motion detection that can explain psychophysical and neurophysiological phenomena

This paper proposes a new neural network model for visual motion detection. The model can well explain both psychophysical findings (the changes of displacement thresholds with stimulus velocity and the perception of apparent motion) and neurophysiological findings (the selectivity for the direction and the velocity of a moving stimulus). To confirm the behavior of the model, numerical examinations were conducted. The results were consistent with both psychophysical and neurophysiological findings.