The misfolding and aggregation of proteins into amyloid fibrils characterizes many neurodegenerative disorders such as Parkinson’s and Alzheimer’s diseases. We report here a method, termed SAVE (single aggregate visualization by enhancement) imaging, for the ultrasensitive detection of individual amyloid fibrils and oligomers using single-molecule fluorescence microscopy. We demonstrate that this method is able to detect the presence of amyloid aggregates of α-synuclein, tau, and amyloid-β. In addition, we show that aggregates can also be identified in human cerebrospinal fluid (CSF). Significantly, we see a twofold increase in the average aggregate concentration in CSF from Parkinson’s disease patients compared to age-matched controls. Taken together, we conclude that this method provides an opportunity to characterize the structural nature of amyloid aggregates in a key biofluid, and therefore has the potential to study disease progression in both animal models and humans to enhance our understanding of neurodegenerative disorders.

Most quadrupolar molecules designed for large two-photon absorption cross section have been shown to undergo symmetry breaking upon excitation to the S state. This was originally deduced from their strong fluorescence solvatochromism and later visualized in real time using transient infrared spectroscopy. For molecules not containing clear IR marker modes, however, a specific real-time observation of the symmetry breaking process remains lacking. Here we show that this process can be resolved using broadband fluorescence upconversion spectroscopy by monitoring the instantaneous emission transition dipole moment. This approach is illustrated with measurements performed on two quadrupolar molecules, with only one of them undergoing excited-state symmetry breaking in polar solvents.

Time-resolved electronic spectroscopy has grown into a technique that provides hundreds to thousands of electronic spectra with femtosecond time resolution. This enables complex questions to be interrogated, with an obvious cost that the data are more detailed and thus require accurate modelling to be properly reproduced. Data analysis of these data comes in a variety of forms, starting with a variety of assumptions about how the data may be decomposed. Here, four different types of analysis commonly used are discussed: band-shape analysis, global kinetic analysis, lifetime distribution models, and soft-modelling. This review provides a 'user's guide' to these various methods of data analysis, and attempts to elucidate their successes, domains in which they may be useful, and potential pitfalls in their usage.

The radical anion of 9,10-dicyanoanthracene (DCA) has been suggested to be a promising chromophore for photoredox chemistry, due to its nanosecond excited-state lifetime determined from indirect measurements. Here, we investigate...

Time-resolved photoluminescence is one of the most standard techniques to understand and systematically optimize the performance of optical materials and optoelectronic devices. Here, we present a machine learning code to analyze time-resolved photoluminescence data and determine the decay rate distribution of an arbitrary emitter without any a priori assumptions. To demonstrate and validate our approach, we analyze computer-generated time-resolved photoluminescence data sets and show its benefits for studying the photoluminescence of novel semiconductor nanocrystals (quantum dots), where it quickly provides insight into the possible physical mechanisms of luminescence without the need for educated guessing and fitting.