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Within the realm of optical neural interfaces, the exploration of plasmonic resonances to interact with neural cells has captured increasing attention among the neuroscience community. The interplay of light with conduction electrons in nanometer-sized metallic nanostructures can induce plasmonic resonances, showcasing a versatile capability to both sense and trigger cellular events. We describe the perspective of generating propagating or localized surface plasmon polaritons on the tip of an optical neural implant, widening the possibility for neuroscience labs to explore the potential of plasmonic neural interfaces. [ABSTRACT FROM AUTHOR]

We report studies of the coalescence of pairs of picolitre aerosol droplets manipulated with holographic optical tweezers, probing the shape relaxation dynamics following coalescence by simultaneously monitoring the intensity of elastic backscattered light (EBL) from the trapping laser beam (time resolution on the order of 100 ns) while recording high frame rate camera images (time resolution <10 μs). The goals of this work are to: resolve the dynamics of droplet coalescence in holographic optical traps; assign the origin of key features in the time-dependent EBL intensity; and validate the use of the EBL alone to precisely determine droplet surface tension and viscosity. For low viscosity droplets, two sequential processes are evident: binary coalescence first results from the overlap of the optical traps on the time scale of microseconds followed by the recapture of the composite droplet in an optical trap on the time scale of milliseconds. As droplet viscosity increases, the relaxation in droplet shape eventually occurs on the same time scale as recapture, resulting in a convoluted evolution of the EBL intensity that inhibits quantitative determination of the relaxation time scale. Droplet coalescence was simulated using a computational framework to validate both experimental approaches. The results indicate that time-dependent monitoring of droplet shape from the EBL intensity allows for robust determination of properties such as surface tension and viscosity. Finally, the potential of high frame rate imaging to examine the coalescence of dissimilar viscosity droplets is discussed. [ABSTRACT FROM AUTHOR]

Creating plasmonic nanoparticles on a tapered optical fiber (TF) tip enables a remote surface-enhanced Raman scattering (SERS) sensing probe, ideal for challenging sampling scenarios like biological tissues, site-specific cells, on-site environmental monitoring, and deep brain structures. However, nanoparticle patterns fabricated from current bottom-up methods are mostly random, making geometry control difficult. Uneven statistical distribution, clustering, and multilayer deposition introduce uncertainty in correlating device performance with morphology. Ultimately, this limits the design of the best-performance remote SERS sensing probe. Here we employ a tunable solid-state dewetting method to create densely packed monolayer Au nanoislands with varied geometric parameters in direct contact with the silica TF surface. These patterns exhibit analyzable nanoparticle sizes, densities, and uniform distribution across the entire taper surface, enabling a systematic investigation of particle size, density, and analyte effects on the SERS performance of the through-fiber detection system. The study is focused on the SERS response of a widely employed benchmark molecule, rhodamine 6G (R6G), and serotonin, a highly relevant neurotransmitter for the neuroscience field. The numerical simulations and limit of detection (LOD) experiments on R6G show that the increase of the total near-field enhancement volume promotes the SERS sensitivity of the probe. However, we observed a different behavior for serotonin linked to its interaction with the nanoparticle's surface. The obtained LOD is as low as 10 -7 M, a value not achieved so far in a through-fiber detection scheme. Therefore, our work offers a strategy to design nanoparticle-based remote SERS sensing probes and provides new clues to discover and understand intricate plasmonic-driven chemical reactions., Competing Interests: The authors declare the following competing financial interest(s): M.D.V. and Fe.P. are founders and hold private equity in Optogenix, a company that develops, produces, and sells technologies to deliver light into the brain., (© 2024 The Authors. Published by American Chemical Society.)

Artificial intelligence has emerged as promising tool to decode an image transmitted through a multimode fiber (MMF) by applying deep learning techniques. By transmitting thousands of images through the MMF, deep neural networks (DNNs) are able to decipher the seemingly random output speckle patterns and unveil the intrinsic input-output relationship. High fidelity reconstruction is obtained for datasets with a large degree of homogeneity, which underutilizes the capacity of the combined MMF-DNN system. Here, we show that holographic modulation can encode an additional layer of variance on the output speckle pattern, improving the overall transmissive capabilities of the system. Operatively, we have implemented this by adding a holographic label to the original dataset and injecting the resulting phase image into the fiber facet through a Fourier transform lens. The resulting speckle pattern dataset can be clustered primarily by holographic label, and can be reconstructed without loss of fidelity. As an application, we describe how color images may be segmented into RGB components and each color component may then be labelled by distinct hologram. A ResUNet architecture was then used to decode each class of speckle patterns and reconstruct the color image without the need for temporal synchronization between sender and receiver.