Luminescent Oil-Soluble Carbon Dots toward White Light Emission: A Spectroscopic Study

Carbon dots (C-dots) are emerging as new emitting nanomaterials for optoelectronics, bioimaging, and biosensing thanks to their high quantum yield (QY), biocompatibility, low toxicity, and cost-effective sources. Although the origin of their photoluminescence (PL) mechanism (i.e., their strong blue-green emission and excitation dependent fluorescence) is still controversial, it has been demonstrated to depend on the synthetic protocols and experimental conditions, able to modify the surface properties. Here oil-dispersible C-dots, synthesized by carbonization of citric acid in the presence of hexadecylamine in high boiling organic solvent, are thoroughly investigated by systematically controlling the synthetic reaction parameters. Similarly to what was found for water-soluble C-dots, citric acid in the presence of amine-containing passivating agents improves the PL emission of C-dots via the formation of molecular fluorescent derivatives alongside the carbonization process. We demonstrate that at growth temperature of 200 °C such C-dots exhibit an interesting and intense white emission, when excited in the blue region, thus resulting in a biocompatible colloidal white emitting single nano-objects. The incorporation of the nanoparticles in a poly(methyl methacrylate) (PMMA) host matrix, to obtain free-standing nanocomposite films, is demonstrated not to affect the color point, which still falls in the white color region of the 1931 CIE diagram. Remarkably, the emission properties are retained even after several months of films exposure to air and sunlight, thus confirming the color stability of the nanoparticles against aging

Colloidal inorganic nanocrystal based nanocomposites: Functional materials for micro and nanofabrication

The unique size- and shape-dependent electronic properties of nanocrystals (NCs) make them extremely attractive as novel structural building blocks for constructing a new generation of innovative materials and solid-state devices. Recent advances in material chemistry has allowed the synthesis of colloidal NCs with a wide range of compositions, with a precise control on size, shape and uniformity as well as specific surface chemistry. By incorporating such nanostructures in polymers, mesoscopic materials can be achieved and their properties engineered by choosing NCs differing in size and/or composition, properly tuning the interaction between NCs and surrounding environment. In this contribution, different approaches will be presented as effective opportunities for conveying colloidal NC properties to nanocomposite materials for micro and nanofabrication. Patterning of such nanocomposites either by conventional lithographic techniques and emerging patterning tools, such as ink jet printing and nanoimprint lithography, will be illustrated, pointing out their technological impact on developing new optoelectronic and sensing devices. © 2010 by the authors

Machine learning regression for QoT estimation of unestablished lightpaths

Estimating the quality of transmission (QoT) of a candidate lightpath prior to its establishment is of pivotal importance for effective decision making in resource allocation for optical networks. Several recent studies investigated machine learning (ML) methods to accurately predict whether the configuration of a prospective lightpath satisfies a given threshold on a QoT metric such as the generalized signal-To-noise ratio (GSNR) or the bit error rate. Given a set of features, the GSNR for a given lightpath configuration may still exhibit variations, as it depends on several other factors not captured by the features considered. It follows that the GSNR associated with a lightpath configuration can be modeled as a random variable and thus be characterized by a probability distribution function. However, most of the existing approaches attempt to directly answer the question is a given lightpath configuration (e.g., with a given modulation format) feasible on a certain path? but do not consider the additional benefit that estimating the entire statistical distribution of the metric under observation can provide. Hence, in this paper, we investigate how to employ ML regression approaches to estimate the distribution of the received GSNR of unestablished lightpaths. In particular, we discuss and assess the performance of three regression approaches by leveraging synthetic data obtained by means of two different data generation tools. We evaluate the performance of the three proposed approaches on a realistic network topology in terms of root mean squared error and R2 score and compare them against a baseline approach that simply predicts the GSNR mean value. Moreover, we provide a cost analysis by attributing penalties to incorrect deployment decisions and emphasize the benefits of leveraging the proposed estimation approaches from the point of view of a network operator, which is allowed to make more informed decisions about lightpath deployment with respect to state-of-The-Art QoT classification techniques

Au Nanoparticles Decorated Graphene-Based Hybrid Nanocomposite for As(III) Electroanalytical Detection

Electrochemical sensors integrating hybrid nanostructured platforms are a promising alternative to conventional detection techniques for addressing highly relevant challenges of heavy metal determination in the environment. Hybrid nanocomposites based on graphene derivatives and inorganic nanoparticles (NPs) are ideal candidates as active materials for detecting heavy metals, as they merge the relevant physico-chemical properties of both the components, finally leading to a rapid and sensitive current response. In this work, a hybrid nanocomposite formed of reduced graphene oxide (RGO) sheets, surface functionalized by π-π interactions with 1-pyrene carboxylic acid (PCA), and decorated in situ by Au NPs, was synthesized by using a colloidal route. The hybrid nanocomposite was characterized by cyclic voltammetry and electrochemical impedance spectroscopy with respect to the corresponding single components, both bare and deposited as a layer-by-layer junction onto the electrode. The results demonstrated the high electrochemical activity of the hybrid nanocomposite with respect to the single components, highlighting the crucial role of the nanostructured surface morphology of the electrode and the PCA coupling agent at the NPs-RGO interphase in enhancing the nanocomposite electroactivity. Finally, the Au NP-decorated PCA-RGO sheets were tested by anodic stripping voltammetry of As(III) ion—a particularly relevant analyte among heavy metal ions—in order to assess the sensing ability of the nanocomposite material with respect to its single components. The nanocomposite has been found to present a sensitivity higher than that characterizing the bare components, with LODs complying with the directives established by the U.S. EPA and in line with those reported for state-of-the-art electrochemical sensors based on other Au-graphene nanocomposites

A possible role of fzd10 delivering exosomes derived from colon cancers cell lines in inducing activation of epithelial–mesenchymal transition in normal colon epithelial cell line

Exosomes belong to the family of extracellular vesicles released by every type of cell both in normal and pathological conditions. Growing interest in studies indicates that extracellular vesicles, in particular, the fraction named exosomes containing lipids, proteins and nucleic acid, represent an efficient way to transfer functional cargoes between cells, thus combining all the other cell–cell interaction mechanisms known so far. Only a few decades ago, the involvement of exosomes in the carcinogenesis in different tissues was discovered, and very recently it was also observed how they carry and modulate the presence of Wnt pathway proteins, involved in the carcinogenesis of gastrointestinal tissues, such as Frizzled 10 protein (FZD10), a membrane receptor for Wnt. Here, we report the in vitro study on the capability of tumor-derived exosomes to induce neoplastic features in normal cells. Exosomes derived from two different colon cancer cell lines, namely the non-metastatic CaCo-2 and the metastatic SW620, were found to deliver, in both cases, FZD10, thus demonstrating the ability to reprogram normal colonic epithelial cell line (HCEC-1CT). Indeed, the acquisition of specific mesenchymal characteristics, such as migration capability and expression of FZD10 and markers of mesenchymal cells, was observed. The exosomes derived from the metastatic cell line, characterized by a level of FZD10 higher than the exosomes extracted from the non-metastatic cells, were also more efficient in stimulating EMT activation. The overall results suggest that FZD10, delivered by circulating tumor-derived exosomes, can play a relevant role in promoting the CRC carcinogenesis and propagation

Robust optical frequency dissemination with a dual-polarization coherent receiver

Frequency dissemination over optical fiber links relies on measuring the phase of fiber-delivered lasers. Phase is extracted from optical beatnotes and the detection fails in case of beatnotes fading due to polarization changes, which strongly limit the reliability and robustness of the dissemination chain. We propose a new method that overcomes this issue, based on a dual-polarization coherent receiver and a dedicated signal processing that we developed on a field programmable gated array. Our method allowed analysis of polarization-induced phase noise from a theoretical and experimental point of view and endless tracking of the optical phase. This removes a major obstacle in the use of optical links for those physics experiments where long measurement times and high reliability are required

Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

An ultra-fast machine learning based method for accurate predictions of gain and amplified spontaneous emission (ASE) noise profiles of Raman amplifiers is introduced. It is an alternative to high-complexity and time-consuming standard approaches, which are based on the numerical solution of sets of nonlinear differential equations. Main relevance resides on its possible application in real-Time network controllers for future multi-band optical line systems where Raman amplification will be required to cope with capacities beyond the standard C-band. Here we consider as an example the C+L-band scenario with different input load conditions: full load and partial loads. For the case of full load it has been recently shown a neural network (NN) capable of highly accurate predictions. Real optical networks are not usually operated only in full load conditions: The load can dynamically vary over time and the behavior of the Raman amplifier depends on it. In this article we introduce a new NN model and we show its higher accuracy when the line system is not fully loaded: we define it as the load aware neural network. Applying this new approach we can predict both gain and ASE noise profiles in Raman amplifiers with high accuracy under any load conditions: we demonstrate almost 100% of maximum prediction errors to be lower than 0.5 dB