A supervised machine learning approach to classify traffic-derived PM sources based on their magnetic properties.

Environmental magnetism techniques are increasingly used to map the deposition of particulate pollutants on any type of accumulative surfaces. The present study is part of a collective effort that begun in recent years to evaluate the efficiency of these techniques involving a large range of measurements to trace the source signals. Here we explore the possibilities provided by the very simple but robust k-near-neighbors algorithm (kNN) for classification in a source-to-sink approach. For this purpose, in a first phase, the magnetic properties of the traffic-related sources of particulate matter (tire, brake pads, exhaust pipes, etc.) are used to parameterize and train the model. Then, the magnetic parameters measured on accumulating surfaces exposed to a polluted air as urban plant leaves and passive filters are confronted to the model. The results are very encouraging. The algorithm predicts the dominant traffic-related sources for different kinds of accumulative surfaces. The model predictions are generally consistent according to the sampling locations. Its resolution seems adequate since different dominant sources could be identified within one street. We demonstrate the possibility to trace traffic-derived pollutants from sources to sinks based only on magnetic properties, and to eventually quantify their contributions in the total magnetic signal measured. Because magnetic mapping has a high-resolution efficiency, these results open the opportunity to complement conventional methods used to measure air quality and to improve the numerical models of pollutant dispersion.

Bad neighbour, good neighbour: how magnetic dipole interactions between soft and hard ferrimagnetic nanoparticles affect macroscopic magnetic properties in ferrofluids.

Fluids responding to magnetic fields (ferrofluids) offer a scene with no equivalent in nature to explore long-range magnetic dipole interactions. Here, we studied the very original class of binary ferrofluids, embedding soft and hard ferrimagnetic nanoparticles. We used a combination of X-ray magnetic spectroscopy measurements supported by multi-scale experimental techniques and Monte-Carlo simulations to unveil the origin of the emergent macroscopic magnetic properties of the binary mixture. We found that the association of soft and hard magnetic nanoparticles in the fluid has a considerable influence on their inherent magnetic properties. While the ferrofluid remains in a single phase, magnetic interactions at the nanoscale between both types of particles induce a modification of their respective coercive fields. By connecting the microscopic properties of binary ferrofluids containing small particles, our findings lay the groundwork for the manipulation of magnetic interactions between particles at the nanometer scale in magnetic liquids.

Direct evidence for an interdiffused intermediate layer in bi-magnetic core-shell nanoparticles.

Core-shell nanoparticles attract continuously growing interest due to their numerous applications, which are driven by the possibility of tuning their functionalities by adjusting structural and morphological parameters. However, despite the critical role interdiffused interfaces may have in the properties, these are usually only estimated in indirect ways. Here we directly evidence the existence of a 1.1 nm thick (Fe,Mn)3O4 interdiffused intermediate shell in nominally γ-Fe2O3-Mn3O4 core-shell nanoparticles using resonant inelastic X-ray scattering spectroscopy combined with magnetic circular dichroism (RIXS-MCD). This recently developed magneto-spectroscopic probe exploits the unique advantages of hard X-rays (i.e., chemical selectivity, bulk sensitivity, and low self-absorption at the K pre-edge) and can be advantageously combined with transmission electron microscopy and electron energy loss spectroscopy to quantitatively elucidate the buried internal structure of complex objects. The detailed information on the structure of the nanoparticles allows understanding the influence of the interface quality on the magnetic properties.

The Emperor Seamounts: southward motion of the Hawaiian hotspot plume in Earth's mantle.

The Hawaiian-Emperor hotspot track has a prominent bend, which has served as the basis for the theory that the Hawaiian hotspot, fixed in the deep mantle, traced a change in plate motion. However, paleomagnetic and radiometric age data from samples recovered by ocean drilling define an age-progressive paleolatitude history, indicating that the Emperor Seamount trend was principally formed by the rapid motion (over 40 millimeters per year) of the Hawaiian hotspot plume during Late Cretaceous to early-Tertiary times (81 to 47 million years ago). Evidence for motion of the Hawaiian plume affects models of mantle convection and plate tectonics, changing our understanding of terrestrial dynamics.