RESUMO
The emerging field of nanoscale infrared (nano-IR) offers label-free molecular contrast, yet its imaging speed is limited by point-by-point traverse acquisition of a three-dimensional (3D) data cube. Here, we develop a spatial-spectral network (SS-Net), a miniaturized deep-learning model, together with compressive sampling to accelerate the nano-IR imaging. The compressive sampling is performed in both the spatial and spectral domains to accelerate the imaging process. The SS-Net is trained to learn the mapping from small nano-IR image patches to the corresponding spectra. With this elaborated mapping strategy, the training can be finished quickly within several minutes using the subsampled data, eliminating the need for a large-labeled dataset of common deep learning methods. We also designed an efficient loss function, which incorporates the image and spectral similarity to enhance the training. We first validate the SS-Net on an open stimulated Raman-scattering dataset; the results exhibit the potential of 10-fold imaging speed improvement with state-of-the-art performance. We then demonstrate the versatility of this approach on atomic force microscopy infrared (AFM-IR) microscopy with 7-fold imaging speed improvement, even on nanoscale Fourier transform infrared (nano-FTIR) microscopy with up to 261.6 folds faster imaging speed. We further showcase the generalization of this method on AFM-force volume-based multiparametric nanoimaging. This method establishes a paradigm for rapid nano-IR imaging, opening new possibilities for cutting-edge research in materials, photonics, and beyond.
RESUMO
Tip-enhanced Raman spectroscopy (TERS) can provide correlated topographic and chemical information at the nanoscale, with great sensitivity and spatial resolution depending on the configuration of the TERS probe. The sensitivity of the TERS probe is largely determined by two effects: the lightning-rod effect and local surface plasmon resonance (LSPR). While 3D numerical simulations have traditionally been used to optimize the TERS probe structure by sweeping two or more parameters, this method is extremely resource-intensive, with computation times growing exponentially as the number of parameters increases. In this work, we propose an alternative rapid theoretical method that reduces computational loading while still achieving effective TERS probe optimization through the inverse design method. By applying this method to optimize a TERS probe with four free-structural parameters, we observed a nearly 1 order of magnitude improvement in enhancement factor (|E/E0|2), in contrast to a parameter sweeping 3D simulation that would take â¼7000 hours of computation. Our method, therefore, shows great promise as a useful tool for designing not only TERS probes but also other near-field optical probes and optical antennas.
RESUMO
Proton transfer is crucial for electrocatalysis. Accumulating cations at electrochemical interfaces can alter the proton transfer rate and then tune electrocatalytic performance. However, the mechanism for regulating proton transfer remains ambiguous. Here, we quantify the cation effect on proton diffusion in solution by hydrogen evolution on microelectrodes, revealing the rate can be suppressed by more than 10 times. Different from the prevalent opinions that proton transport is slowed down by modified electric field, we found water structure imposes a more evident effect on kinetics. FTIR test and path integral molecular dynamics simulation indicate that proton prefers to wander within the hydration shell of cations rather than to hop rapidly along water wires. Low connectivity of water networks disrupted by cations corrupts the fast-moving path in bulk water. This study highlights the promising way for regulating proton kinetics via a modified water structure.