Radiative recombination dynamics in tetrapod-shaped CdTe nanocrystals: Evidence for a photoinduced screening of the internal electric field

We study the radiative recombination processes in CdTe tetrapod nanocrystals at 10K. Two intrinsic emission bands, namely the ground state (GS) and the excited state (EX), decay with three time constants, due to a power dependent Auger-like recombination process (tens of picoseconds), to the intrinsic emission of the two states (hundreds of picoseconds) and to emission from defect states (a few nanoseconds). The existence of an internal electric field originating from the e-h separation induced by the peculiar symmetry of the GS is demonstrated by a dynamical shift of the GS emission energy that is correlated to the EX population

Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. The integrate-and-fire (I&F) models are often adopted, with the simple Leaky I&F (LIF) being the most used. The reason for adopting such models is their efficiency and/or biological plausibility. Nevertheless, rigorous justification for adopting LIF over other neuron models for use in artificial learning systems has not yet been studied. This work considers various neuron models in the literature and then selects computational neuron models that are single-variable, efficient, and display different types of complexities. From this selection, we make a comparative study of three simple I&F neuron models, namely the LIF, the Quadratic I&F (QIF) and the Exponential I&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed. Neuron models are tested within an SNN trained with Spike-Timing Dependent Plasticity (STDP) on a classification task on the N-MNIST and DVS Gestures datasets. Experimental results reveal that more complex neurons manifest the same ability as simpler ones to achieve high levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably more hyper-parameter tuning. However, when the data possess richer Spatio-temporal features, the QIF and EIF neuron models steadily achieve better results. This suggests that accurately selecting the model based on the richness of the feature spectrum of the data could improve the whole system's performance. Finally, the code implementing the spiking neurons in the SpykeTorch framework is made publicly available

Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch

Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation. Software frameworks aid and ease this process by providing a set of ready-to-use tools that researchers can leverage. The recent interest in NM technology has seen the development of several new frameworks that do this, and that add up to the panorama of already existing libraries that belong to neuroscience fields. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. We emphasize the availability of spiking neuron models and learning rules to more easily direct decisions on the most suitable frameworks to carry out different types of research. Furthermore, we present an extension to the SpykeTorch framework that gives users access to a much broader choice of neuron models to embed in SNNs and make the code publicly available

Picosecond photoluminescence decay time in colloidal nanocrystals : The role of intrinsic and surface states

Picosecond time-resolved photoluminescence measurements were performed on CdSe core and CdSe/ZnS core/shell colloidal quantum dots (QDs). Photoluminescence (PL) emission is observed to originate from intrinsic ±1U and ±1L bright states with lifetimes of 60 and 450 ps, respectively, and from a long living component with nanosecond lifetimes. The latter is attribuited to the emission from surface states (ss) approximately 16 and 13 meV below the ±1L state for core and core/shell QDs, respectively. We show that in the temperature range between 15 and 70 K the three recombination processes compete and they are thermally populated through different pathways (±1L → ±1U and ss → ±1L)

Selective reactions on the tips of colloidal semiconductor nanorods

A strategy to access several types of Au-tipped dumbbell-like nanocrystal heterostructures is presented, which involves the selective oxidation of either PbSe or CdTe sacrificial domains, initially grown on CdSe and CdS nanorods, with a Au(III) : surfactant complex. The formation of gold patches is supported by TEM, XRD and elemental analysis. This approach has allowed us to grow Au domains onto specific locations of anisotropically shaped nanocrystals for which direct metal deposition is unfeasible, as for the case of CdS nanorods. We believe that this strategy may be of general utility to create other types of complex colloidal nanoheterostructures, provided that a suitable sacrificial material can be grown on top of the starting nanocrystal seeds

Exciton transitions in tetrapod-shaped CdTe nanocrystals investigated by photomodulated transmittance spectroscopy

The excitonic nature of the optical transitions in tetrapod-shaped colloidal CdTe nanocrystals is assessed by means of photomodulated transmittance spectroscopy. The line-shape analysis of the photomodulation transmittance spectra indicates the photoinduced Stark effect as the dominant modulation mechanism, and the presence of excitonic transitions even at room temperature, with an exciton binding energy of about 25meV, larger than the bulk value

Determination of surface properties of various substrates using TiO2 nanorod coatings with tunable characteristics

We present a novel approach to cover different substrates with thin light-sensitive layers that consist of organic-capped TiO2 nanorods (NRs). Such NR-based coatings exhibit an increasing initial hydrophobicity with increasing NR length, and they demonstrate a surface transition from this highly hydrophobic state to a highly hydrophilic one under selective UV–laser irradiation. This behaviour is reversed under long dark storage. Infrared spectroscopy measurements reveal that light-driven wettability changes are accompanied by a progressive hydroxylation of the TiO2 surface. The surfactant molecules that cover the NRs do not appear to suffer for any significant photocatalytic degradation

Simple and complex spiking neurons : perspectives and analysis in a simple STDP scenario

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience, and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. The integrate-and-fire (I\&F) models are often adopted as considered more suitable, with the simple Leaky I\&F (LIF) being the most used. The reason for adopting such models is their efficiency or biological plausibility. Nevertheless, rigorous justification for the adoption of LIF over other neuron models for use in artificial learning systems has not yet been studied. This work considers a variety of neuron models in the literature and then selects computational neuron models that are single-variable, efficient, and display different types of complexities. From this selection, we make a comparative study of three simple I\&F neuron models, namely the LIF, the Quadratic I\&F (QIF) and the Exponential I\&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed. Neuron models are tested within an SNN trained with Spike-Timing Dependent Plasticity (STDP) on a classification task on the N-MNIST and DVS Gestures datasets. Experimental results reveal that more complex neurons manifest the same ability as simpler ones to achieve high levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably more hyper-parameter tuning. However, when the data possess richer spatio-temporal features, the QIF and EIF neuron models steadily achieve better results. This suggests that accurately selecting the model based on the richness of the feature spectrum of the data could improve the performance of the whole system. Finally, the code implementing the spiking neurons in the SpykeTorch framework is made publicly available