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Abstract: Giant magnetofossils are unusual, micron-sized biogenic magnetite particles found in sediments dating back at least 97 million years. Their distinctive morphologies are the product of biologically controlled mineralisation, yet the identity of their biomineralising organism, and the biological function they serve, remain a mystery. It is currently thought that the organism exploited magnetite’s mechanical properties for protection. Here we explore an alternative hypothesis, that it exploited magnetite’s magnetic properties for the purpose of magnetoreception. We present a three-dimensional magnetic vector tomography study of a giant magnetofossil and assess its magnetoreceptive potential. Our results reveal a single magnetic vortex that displays an optimised response to spatial variations in the intensity of Earth’s magnetic field. This magnetic trait may have conferred an evolutionary advantage to mobile marine organisms, providing an upper age limit on the development of navigational magnetoreception and raising the possibility that earlier evidence of this sense may yet be preserved in the fossil record. More broadly, this work provides a blueprint for assessing the morphological and magnetic evidence for putative biogenic iron oxide particles, which are a key component in the search for early life on Earth and Mars.

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Abstract: The scales of butterflies display a vast array of vivid colors. However, the exact mechanisms behind these colours are not yet fully understood. Butterfly scales consist of intricate nanostructures that in- teract with light through interference, diffraction, and scattering. Additionally, the nanostructures on butterfly scales vary in pigment density across different species. A combination of 'pigment effects' and ‘structural effects’ gives rise to the vivid colors observed on a butterfly’s wings. Variations in pigment density have been correlated with specific nanostructures. However, the interplay between pigmentation and nanostructures - how they influence each other - remains largely unexplored. Hence, our work aims to perform a detailed examination of the distribution of various matrix components within butterfly scales, leading to a deeper understanding of not only their colour, but also their role in guiding nano- structure growth.

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Abstract: ances in high-resolution electron microscopy have enabled remarkable insights into both biological and materials science, particularly in the detailed structural imaging of small particles and complex materials interfaces. Yet, as resolution increases, the effective depth of field (DOF) of Transmission Electron Microscopy (TEM) narrows, creating new challenges for accurately imaging thick samples. Traditional phase retrieval approaches rely on contrast transfer functions (CTF) and projection approximations (PA) that linearize image formation physics. While effective at near-atomic resolutions, these methods lose critical phase information, limiting resolution in cases that require a full 3D reconstruction of complex structures. The maximum achievable contrast in Transmission Electron Microscopy (TEM) for small particles, including biomolecules, is theoretically set by the scattering cross sections of these particles. Our previous work has shown that, under ideal conditions, computational phase retrieval can reach this theoretical limit by precisely modeling electron-matter interactions1. However, the real-world limitations of TEM—such as noise, sample damage, and heterogeneity in imaging conditions—create a gap between theoretical predictions and actual experimental outcomes, especially for challenging samples like small or flexible biomolecules. This theory-experiment gap has historically hindered the ability to fully exploit TEM for resolving fine structural details in complex materials. First-principles simulations struggle to account for the empirical effects present in real data, including variations in ice thickness, specific particle damage, and environmental noise. To address this gap, we integrate these theoretical models into Physics-Informed Neural Networks (PINNs), which incorporate the physical laws governing electron scattering directly into the machine learning framework.

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Abstract: When an object is illuminated by a source, the resulting exit wave is a complex function intricately connected to the object's structure. Generally speaking, the wave's modulus corresponds to the object's transmittance, while its phase represents the cumulative phase shift relative to free space, caused by the object's refractive index as the wave traverses it. Therefore, to fully characterise the object, both the modulus and phase of the exit wave are required. However, detectors only measure the intensities (the modulus squared) of the waves striking the detector. The modulus can thus be easily obtained by taking the square root of the measured intensity. The phase, on the other hand, is much more challenging to determine. Any potential phase configuration could theoretically produce the same measured intensity, leading to what is known as the 'Phase Retrieval Problem', whereby the true phases that were lost during the experiment are not easily recovered. Ptychography offers a possible solution to the phase retrieval problem. Multiple diffraction intensity patterns are obtained by scanning a finite probe over an extended specimen with sufficient overlap between adjacent illumination positions. Combined with suitable iterative reconstruction algorithms, these measured diffraction patterns can be effectively utilised to accurately determine both the amplitude and phase of the object's exit wave, providing a robust approach that significantly enhances the ability to retrieve complete structural information about a specimen. Ptychography can be combined with X-ray absorption spectroscopy by using an X-ray beam of tuneable energies at each scan position. The diffraction patterns are now collected when the X-rays are scanned over absorption edges of specific elements, providing chemical information, in addition to high resolution reconstructions. This method has been dubbed 'Spectro-Ptychography'. Our work aims to push the limits of ptychography by employing it to analyse various butterfly scales. These scales are intricate self-assemblies of proteins that form complex structures. Beyond exhibiting pigment colour—where specific compounds absorb particular wavelengths of light—butterfly scales are also remarkable examples of structural colour, where their unique structures manipulate light in specific ways. We will showcase the application of spectro-ptychography on butterfly scales and demonstrate how we extracted detailed 3D spatial and chemical information from the dataset, aiding in our understanding of both 'pigment colour' and 'structural colour'. Preliminary results reveal distinct chemical variations in the carbon and oxygen signatures across different regions of a butterfly scale. By segmenting the scale into defined classes, we gain deeper insights into the chemical compositions of each specific segment, enhancing our understanding of its complex structure.

Open Access

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Abstract: Nanoparticles, exhibiting functionally relevant structural heterogeneity, are at the forefront of cutting-edge research. Now, high-throughput single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) creates opportunities for recovering the shape distributions of millions of particles that exhibit functionally relevant structural heterogeneity. To realize this potential, three challenges have to be overcome: (1) simultaneous parametrization of structural variability in real and reciprocal spaces; (2) efficiently inferring the latent parameters of each SPI measurement; (3) scaling up comparisons between 105 structural models and 106 XFEL-SPI measurements. Here, we describe how we overcame these three challenges to resolve the nonequilibrium shape distributions within millions of gold nanoparticles imaged at the European XFEL. These shape distributions allowed us to quantify the degree of asymmetry in these particles, discover a relatively stable “shape envelope” among nanoparticles, discern finite-size effects related to shape-controlling surfactants, and extrapolate nanoparticles’ shapes to their idealized thermodynamic limit. Ultimately, these demonstrations show that XFEL SPI can help transform nanoparticle shape characterization from anecdotally interesting to statistically meaningful.