Controlling resonance energy transfer in nanostructure emitters by positioning near a mirror

The ability to control light-matter interactions in quantum objects opens up many avenues for new applications. We look at this issue within a fully quantized framework using a fundamental theory to describe mirror-assisted resonance energy transfer (RET) in nanostructures. The process of RET communicates electronic excitation between suitably disposed donor and acceptor particles in close proximity, activated by the initial excitation of the donor. Here, we demonstrate that the energy transfer rate can be significantly controlled by careful positioning of the RET emitters near a mirror. The results deliver equations that elicit new insights into the associated modification of virtual photon behavior, based on the quantum nature of light. In particular, our results indicate that energy transfer efficiency in nanostructures can be explicitly expedited or suppressed by a suitably positioned neighboring mirror, depending on the relative spacing and the dimensionality of the nanostructure. Interestingly, the resonance energy transfer between emitters is observed to “switch off” abruptly under suitable conditions of the RET system. This allows one to quantitatively control RET systems in a new way

Quantum electrodynamical theory of high-efficiency excitation energy transfer in laser-driven nanostructure systems

A fundamental theory is developed for describing laser-driven resonance energy transfer (RET) in dimensionally constrained nanostructureswithin the framework of quantum electrodynamics. The process of RET communicates electronic excitation between suitably disposed emitter and detector particles in close proximity, activated by the initial excitation of the emitter. Here, we demonstrate that the transfer rate can be significantly increased by propagation of an auxiliary laser beam through a pair of nanostructure particles. This is due to the higher order perturbative contribution to the F¨orster-type RET, in which laser field is applied to stimulate the energy transfer process. We construct a detailed picture of how excitation energy transfer is affected by an off-resonant radiation field, which includes the derivation of second and fourth order quantum amplitudes. The analysis delivers detailed results for the dependence of the transfer rates on orientational, distance, and laser intensity factor, providing a comprehensive fundamental understanding of laser-driven RET in nanostructures. The results of the derivations demonstrate that the geometry of the system exercises considerable control over the laser-assisted RET mechanism. Thus, under favorable conformational conditions and relative spacing of donor-acceptor nanostructures, the effect of the auxiliary laser beam is shown to produce up to 70% enhancement in the energy migration rate. This degree of control allows optical switching applications to be identified

Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis

Australian water infrastructure is more than a hundred years old, thus has begun to show its age through water main failures. Our work concerns approximately half a million pipelines across major Australian cities that deliver water to houses and businesses, serving over five million customers. Failures on these buried assets cause damage to properties and water supply disruptions. We applied Machine Learning techniques to find a cost-effective solution to the pipe failure problem in these Australian cities, where on average 1500 of water main failures occur each year. To achieve this objective, we construct a detailed picture and understanding of the behaviour of the water pipe network by developing a Machine Learning model to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes and other environmental factors. Our results indicate that our system incorporating a nonparametric survival analysis technique called ‘Random Survival Forest’ outperforms several popular algorithms and expert heuristics in long-term prediction. In addition, we construct a statistical inference technique to quantify the uncertainty associated with the long-term predictions