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Applications of photon upconverting nanoparticles (UCNPs) in biological imaging and solar energy conversion demand that their anti-Stokes luminescence be both tunable and efficient. Rational design of more efficient UCNPs requires an understanding of energy transfer (ET) between their lanthanide dopants, dynamics that are typically characterized by measuring luminescence lifetimes. Existing knowledge, however, cannot explain basic observations in lifetime experiments, such as their dependence on excitation power, significantly limiting the generality and reliability of lifetime measurements. Here, we elucidate the origins of the ET dynamics and luminescence lifetimes of Yb3+-, Er3+-co-doped NaYF4 UCNPs using time-resolved luminescence and novel applications of rate equations and stochastic simulations. Experiments and calculations consistently show that at high concentrations of Er3+, the luminescence lifetimes of UCNPs decrease as much as six-fold when excitation power densities are increased over 6 orders of magnitude. Since power-dependent lifetimes cannot be explained by intrinsic relaxation rates of individual transitions, we analyze lifetime data by treating each UCNP as a complex ET network. We find that UCNP ET networks exhibit four distinguishing characteristics of complex systems: collectivity, nonlinear feedback, robustness, and history dependence. We conclude that power-dependent lifetimes are the consequence of thousands of minor relaxation pathways that act collectively to depopulate and repopulate Er3+ emitting levels. These ET pathways are dependent on past excitation power because they originate from excited donors and excited acceptors; however, each transition has an unexpectedly small impact on lifetimes due to negative feedback in the network. This robustness is determined by systematically "knocking out", or disabling, ET transitions in kinetic models. Our classification of UCNP ET networks as complex systems explains why UCNP luminescence lifetimes do not match the intrinsic lifetimes of emitting states. In the future, UCNP networks may be engineered to rival the complexity of biological networks that pattern features with unmatched precision. ©

Optical methods for imaging and stimulation of biological events based on the use of visible light are limited to the superficial layers of tissue due to the significant absorption and scattering of light. Here, we demonstrate the design and implementation of passive micro-structured lightbulbs (MLBs) containing bright-emitting lanthanide-doped upconverting nanoparticles (UCNPs) for light delivery deep into the tissue. The MLBs are realized as cylindrical pillars made of Parylene C polymer that can be implanted deep into the tissue. The encapsulated UCNPs absorb near-infrared (NIR) light at λ = 980 nm, which undergoes much less absorption than the blue light in the brain tissue, and then locally emit blue light (1G4→3H6 and 1D2→3F4 transitions) that can be used for optogenetic excitation of neurons in the brain. The 3H4→3H6 transition will result in the emission of higher energy NIR photons at λ = 800 nm that can be used for imaging and tracking MLBs through thick tissue.

There have been numerous emerging next generation solar cell technologies with the potential to disrupt the solar industry. Perovskites in particular have taken the spotlight recently with unprecedented efficiency improvements. To quantify the rate of innovation in next generation solar technologies, we used learning curves to model improvements in efficiency relative to three parameters: Time since first publication, cumulative publications, and cumulative patents. The learning rates found for perovskite solar cells are compared to those of dye-sensitized, and quantum dot solar cells. In doing so, this analysis aims to elicit a better understanding of the drivers of innovation for emerging solar technologies and identify technologies demonstrating extraordinary technological progress.

Ocean acidification (OA) is increasing due to anthropogenic CO2 emissions and poses a threat to marine species and communities worldwide. To better project the effects of acidification on organisms' health and persistence, an understanding is needed of the 1) mechanisms underlying developmental and physiological tolerance and 2) potential populations have for rapid evolutionary adaptation. This is especially challenging in nonmodel species where targeted assays of metabolism and stress physiology may not be available or economical for large-scale assessments of genetic constraints. We used mRNA sequencing and a quantitative genetics breeding design to study mechanisms underlying genetic variability and tolerance to decreased seawater pH (-0.4 pH units) in larvae of the sea urchin Strongylocentrotus droebachiensis. We used a gene ontology-based approach to integrate expression profiles into indirect measures of cellular and biochemical traits underlying variation in larval performance (i.e., growth rates). Molecular responses to OA were complex, involving changes to several functions such as growth rates, cell division, metabolism, and immune activities. Surprisingly, the magnitude of pH effects on molecular traits tended to be small relative to variation attributable to segregating functional genetic variation in this species. We discuss how the application of transcriptomics and quantitative genetics approaches across diverse species can enrich our understanding of the biological impacts of climate change.