Quantification of nanoparticle-molecule interaction at a single-molecule level remains a daunting challenge, mainly due to ultra-weak emission from single molecules and the perturbation of the local environment. Here we report the rational design of an intraparticlesurface energy transfer (i-SET) process, analogous to high doping concentration-induced surface quenching effects, to realize single-molecule sensing by nanoparticle probes. This design, based on a Tb 3+-activator-rich core-shell upconversion nanoparticle, enables a muchimproved spectral response to fluorescent molecules at single-molecule levels through enhanced non-radiative energy transfer with a rate over an order of magnitude faster than conventional counterparts. We demonstrate a quantitative analysis of spectral changes of one to four fluorophores tethered on a single nanoparticle through i-SET spectroscopy. Our results provide opportunities to identify photoreaction kinetics at single-molecule levels and provide direct information for understanding behaviors of individual molecules with unprecedented sensitivity.

Volatilization of surface moieties and sintering of the particles both contribute to thermally activated enhancement of upconversion emission.

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Core−shell structures, which employ an optically inert shell to physically separate the emitting core from the surface quenchers, are often designed to optimize the emission efficiency of nanoscale emitters. However, it remains unclear that at what distance the effects of different surface quenchers, such as defects and adsorbed moieties, can be completely screened by the shell. Here, in a model upconversion system, we examine the interaction distance of surface quenchers in core−shell nanoparticles by using upconversion spectroscopy. Steady-state as well as time-resolved spectra show that the quenching effect of surface-adsorbed hydroxyl (OH) group diminishes at a distance (shell thickness) of 3.5 nm in diameter and 8.0 nm in length, which is larger than that for oleate-capped counterparts. With the increase of pumping density, the interaction distance of the surface quenchers does not apparently change, whereas saturation of the surface-related states notably reduces the optimal shell thickness for surface passivation.

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We introduce here the idea of Meta Learning for training EEG BCI decoders. Meta Learning is a way of training machine learning systems so they learn to learn. We apply here meta learning to a simple Deep Learning BCI architecture and compare it to transfer learning on the same architecture. Our Meta learning strategy operates by finding optimal parameters for the BCI decoder so that it can quickly generalise between different users and recording sessionsthereby also generalising to new users or new sessions quickly. We tested our algorithm on the Physionet EEG motor imagery dataset. Our approach increased motor imagery classification accuracy between 60 to 80%, outperforming other algorithms under the little-data condition. We believe that establishing the meta learning or learning-to-learn approach will help neural engineering and human interfacing with the challenges of quickly setting up decoders of neural signals to make them more suitable for daily-life.

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Thermal quenching effect caused by the increased multi-phonon assisted non-radiative relaxation possibility greatly restricts the application of luminescent materials. Herein, a modified sol-gel method where the gels are achieved by...