Wave optical model for tomographic volumetric additive manufacturing

Tomographic Volumetric Additive Manufacturing (TVAM) allows printing of mesoscopic objects within seconds or minutes. Tomographic patterns are illuminated onto a rotating glass vial which contains a photosensitive resin. Current pattern optimization is based on a ray optical assumption which ultimately leads to limited resolution around $20\mu\textrm{m}$ and varying throughout the volume of the 3D object. In this work, we introduce a rigorous wave-based optical amplitude optimization scheme for TVAM which shows that high-resolution printing is theoretically possible over the full volume. The wave optical optimization approach is based on an efficient angular spectrum method of plane waves with custom written memory efficient gradients and allows for optimization of realistic volumes for TVAM such as $(100\mu\textrm{m})^3$ or $(10\textrm{mm})^3$ with $550^3$ voxels and 600 angles. Our simulations show that ray-optics start to produce artifacts when the desired features are $20\mu\textrm{m}$ and below and more importantly, the amplitude modulated TVAM can reach micrometer features when optimizing the patterns using a full wave model

Xeno Nucleic Acid Nanosensors for Enhanced Stability Against Ion-Induced Perturbations

The omnipresence of salts in biofluids creates a pervasive challenge in designing sensors suitable for in vivo applications. Fluctuations in ion concentrations have been shown to affect the sensitivity and selectivity of optical sensors based on single-walled carbon nanotubes wrapped with single-stranded DNA (ssDNA–SWCNTs). We herein observe fluorescence wavelength shifting for ssDNA–SWCNT-based optical sensors in the presence of divalent cations at concentrations above 3.5 mM. In contrast, no shifting was observed for concentrations up to 350 mM for sensors bioengineered with increased rigidity using xeno nucleic acids (XNAs). Transient fluorescence measurements reveal distinct optical transitions for ssDNA- and XNA-based wrappings during ion-induced conformation changes, with XNA-based sensors showing increased permanence in conformational and signal stability. This demonstration introduces synthetic biology as a complementary means for enhancing nanotube optoelectronic behavior, unlocking previously unexplored possibilities for developing nanobioengineered sensors with augmented capabilities

Programming the scalable optical learning operator with spatial-spectral optimization

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic counterparts and with reduced energy consumption. Here, we use the optical reservoir computing framework we have previously described (Scalable Optical Learning Operator or SOLO) to program the spatial-spectral output of the light after nonlinear propagation in a multimode fiber. The novelty in the current paper is that the system is programmed through an output sampling scheme, similar to that used in hyperspectral imaging in astronomy. Linear and nonlinear computations are performed by light in the multimode fiber and the high dimensional spatial-spectral information at the fiber output is optically programmed before it reaches the camera. We then used a digital computer to classify the programmed output of the multi-mode fiber using a simple, single layer network. When combining front-end programming and the proposed spatial-spectral programming, we were able to achieve 89.9% classification accuracy on the dataset consisting of chest X-ray images from COVID-19 patients. At the same time, we obtained a decrease of 99% in the number of tunable parameters compared to an equivalently performing digital neural network. These results show that the performance of programmed SOLO is comparable with cutting-edge electronic computing platforms, albeit with a much-reduced number of electronic operations

Data for "Resonant Fully dielectric metasurfaces for ultrafast Terahertz pulse generation"

Dataset to accompany the paper"Resonant Fully dielectric metasurfaces for ultrafast Terahertz pulse generation."The dataset is provided in a .mat format,. Separated into folders for each metasurface denoted in the paper. Then each file is labelled by by input wavelength of the laser. Each metasurface also has a power folder as measured by the opo. Each file is broken into two sets, the reading of the lock-in amplifier and the time axis.Inside the theoretical modelling folder is the .mat data for the theoretical modelling, separated by metasurface and then inside the file separated by energy, thz frequency and wavelength.</p

Resonant Fully dielectric metasurfaces for ultrafast Terahertz pulse generation

Metasurfaces represent a new frontier in materials science paving for unprecedented methods of controlling electromagnetic waves, with a range of applications spanning from sensing to imaging and communications. For pulsed terahertz generation, metasurfaces offer a gateway to tuneable thin emitters that can be utilised for large-area imaging, microscopy and spectroscopy. In literature THz-emitting metasurfaces generally exhibit high absorption, being based either on metals or on semiconductors excited in highly resonant regimes. Here we propose the use of a fully dielectric semiconductor exploiting morphology-mediated resonances and inherent quadratic nonlinear response. Our system exhibits a remarkable 40-fold efficiency enhancement compared to the unpatterned at the peak of the optimised wavelength range, demonstrating its potential as scalable emitter design