In this work, we demonstrate how LaF3 nanoparticles activated with large concentrations (up to 25%) of Nd3+ ions can simultaneously operate as biologically compatible efficient nanoheaters and fluorescent nanothermometers under single beam (808 nm) infrared laser excitation. Nd3+:LaF3 nanoparticles emerge as unique multifunctional agents that could constitute the first step towards the future development of advanced platforms capable of simultaneous deep tissue fluorescence bio-imaging and controlled photo-thermal therapies.

Quantum dot based-thermometry, in combination with double beam confocal microscopy and infrared thermal imaging, has been used to investigate the heating efficiency of multi-walled carbon nanotubes (MWCNTs) under optical excitation within the first (808 nm) and second (1090 nm) biological windows as well as in the spectral region separating them (980 nm). It has been found that for the three excitation wavelengths the heating efficiency of MWCNTs (10 nm in diameter and 1.5 μm in length) is close to 50%. Despite this “flat” heating efficiency, we have found that the excitation wavelength is, indeed, critical during in vivo experiments due to the spectral dependence of both tissue absorption and scattering coefficients. It has been concluded that efficiency and selectivity of in vivo photothermal treatments based on MWCNTs are simultaneously optimized when laser irradiation lies within the first or second biological window.

Energy efficiency has been a leading issue in Wireless Sensor Networks (WSNs) and has produced a vast amount of research. Although the classic tradeoff has been between the quality of gathered data versus the lifetime of the network, most works gave preference to an increased network lifetime at the expense of the data quality. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we explore the joint use of in-network processing techniques and clustering algorithms. This approach seeks both high data quality with a controlled number of transmissions using an aggregation function and an energy efficient network partition, respectively. The aim of this combination is to increase energy efficiency without sacrificing the data quality. We compare the performance of the Second-Order Data-Coupled Clustering (SODCC) and Compressive-Projections Principal Component Analysis (CPPCA) algorithm combination, in terms of both the energy consumption and the quality of the data reconstruction, to other combinations of the state-of-the-art clustering algorithms and in-network processing techniques. Among all the considered cases, the SODCC + CPPCA combination revealed a perfect balance between data quality, energy expenditure, and ease of network management. The main conclusion of this paper is that the design of WSN algorithms must be processing-oriented rather than transmission-oriented, i.e., investing energy on both the clustering and in-network processing algorithms ensures both energy efficiency and data quality.

Self-organizing algorithms (SOAs) for wireless sensor networks (WSNs) usually seek to increase the lifetime, to minimize unnecessary transmissions or to maximize the transport capacity. The goal left out in the design of this type of algorithms is the capability of the WSN to ensure an accurate reconstruction of the sensed field while maintaining the self-organization. In this work, we formulate a general framework where the data from the resulting clusters ensures the well-posedness of the signal processing problem in the cluster. We develop the second-order data-coupled clustering (SODCC) algorithm and the distributed compressive-projections principal component analysis (D-CPPCA) algorithm, that use second-order statistics. The condition to form a cluster is that D-CPCCA does not fail to resolve the Principal Components in any given cluster. We show that SODCC is scalable and has similar or better message complexity than other well-known SOAs. We validate these results with extensive computer simulations using an actual LS-WSN. We also show that the performance of SODCC is, comparative to other state-of-the-art SOAs, better at any compression rate and needs no prior adjustment of any parameter. Finally, we show that SODCC compares well to other energy efficient clustering algorithms in terms of energy consumption while excelling in data reconstruction Average SNR.

The light-to-heat conversion efficiency of gold nanorods (GNRs) with surface plasmon resonances in the first (700–950 nm) and second (1000–1350 nm) biological windows has been studied by Quantum Dot based Fluorescence Nanothermometry. It has been found that red-shifting the GNR longitudinal surface plasmon resonance wavelength (λ_SPR) from the first to the second biological window is accompanied by a remarkable (close to 40%) reduction in their heating efficiency. Based on numerical simulations, we have concluded that this lower heating efficiency is caused by a reduction in the absorption efficiency (ratio between absorption and extinction cross sections). Thermal stability and ex vivo experiments have corroborated that GNRs with λ_SPR at around 800 nm seem to be especially suitable for efficient photothermal therapies with minimum collateral effects.