We report the characterisation of anthropogenic magnetic particulate matter (MPM) collected on leaves from roadside Callistemon trees from Lahore, Pakistan, and on known sources of traffic-related particulates to assess the potential of first-order reversal curve (FORC) diagrams to discriminate between different sources of anthropogenic magnetic particles. Magnetic measurements on leaves indicate the presence of surface-oxidised magnetite spanning the superparamagnetic (< 30 nm) to single-domain (~30-70 nm) to vortex size range (~70-700 nm). Fe-bearing particles are present both as discrete particles on the surface of larger mineral dust or carbonaceous particles and embedded within them, such that their aerodynamic sizes may be decoupled from their magnetic grain sizes. FORC diagrams of brake-pad residue specimens show a distinct combination of narrow central ridge, extending from 0-200 mT, and a low-coercivity, vertically spread signal, attributed to vortex and multi-vortex behaviour of metallic Fe. This is in agreement with scanning electron microscopy results that show the presence of metallic as well as oxidised Fe. Exhaust-pipe residue samples display a more conventional ‘magnetite-like’ signal comprising a lower coercivity central ridge (0-80 mT) and a tri-lobate signal attributed to vortex state and/or magnetostatic interactions. The FORC signatures of leaf samples combine aspects of both exhaust residue and brake-pad endmembers, suggesting that FORC fingerprints have the potential to identify and quantify the relative contributions from exhaust and non-exhaust (brake-wear) emissions. Such measurements may provide a cost-effective way to monitor the changing balance of future particulate emissions as the vehicle fleet is electrified over the coming years.

Stable paleomagnetic information in meteoritic metal is carried by the cloudy zone ~1-10 micron wide regions containing islands of ferromagnetic tetrataenite embedded in a paramagnetic antitaenite matrix. Due to their small size and high coercivity (~2.2 T), the tetrataenite islands carry very stable magnetic remanence. However, these characteristics also make it difficult to image their magnetic state with the necessary spatial resolution and applied magnetic field. Here we describe the first application of X-ray holography to image the magnetic structure of the cloudy zone of the Tazewell IIICD meteorite with spatial resolution down to ~40 nm and in applied magnetic fields up to 1.1 T, sufficient to extract high-field hysteresis data from individual islands. Images were acquired as a function of magnetic fields applied both parallel and perpendicular to the surface of a ~100 nm thick slice of the cloudy zone. Broad distributions of coercivity are observed, including values that likely exceed the maximum applied field. Horizontal offsets in the hysteresis loops indicate an interaction field distribution with half width of ~100 mT between the islands in their room-temperature single-domain state, providing a good match to first-order reversal curve diagrams. The role of interactions during the acquisition of transformation chemical remnant magnetization as the meteorite parent body is cooled, and the implications for extracting quantitative estimates of the paleofield, are discussed

Identification of unknown micro- and nano-sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging datasets, particularly scanning electron microscope - energy dispersive X-ray spectroscopy (SEM-EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non-ideal sample preparation, and the presence of mixed chemical signals generated within the electron-beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine-learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel-wise elemental datapoints modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non-negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM-EDS datasets: a synthetic mixture sample and a real-world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background-subtracted single-phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe-bearing particles and their background-subtracted chemical signals. We demonstrate a robust approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user-friendly Python code with a graphical user interface for ease of use by general users.

The meteorite paleomagnetic record indicates that differentiated (and potentially, partially differentiated) planetesimals generated dynamo fields in the first 6-20 Myr after the formation of calcium-aluminium-rich inclusions (CAIs). This early period of dynamo activity has been attributed to thermal convection in the liquid cores of these planetesimals during an early period of magma ocean convection. To better understand the controls on thermal dynamo generation in planetesimals, we have developed a 1D model of the thermal evolution of planetesimals from accretion through to the shutoff of convection in their silicate magma oceans for a variety of accretionary scenarios. The heat source of these bodies is the short-lived radiogenic isotope, 26Al. During differentiation, 26Al partitions into the silicate portion of these bodies, causing their magmas ocean to heat up and introducing stable thermal stratifications to the tops of their cores, which inhibits dynamo generation. In ‘instantaneously’ accreting bodies, this effect causes a delay on the order of >10 Myr to whole core convection and dynamo generation while this stratification is eroded. However, gradual core formation in bodies that accrete over >0.1 Myr can minimise the development of this stratification, allowing dynamo generation from ~4 Myr after CAI formation. Our model also predicts partially differentiated planetesimals with a core and mantle overlain by a chondritic crust for accretion timescales >1.2 Myr, although none of these bodies generate a thermal dynamo field. We compare our results from thousands of model runs to the meteorite paleomagnetic record to constrain the physical properties of their parent bodies.