Circular-polarization-selective perfect reflection from chiral superconductors
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Viral but not bacterial community successional patterns reflect extreme turnover shortly after rewetting dry soils
As central members of soil trophic networks, viruses have the potential to drive substantial microbial mortality and nutrient turnover. Pinpointing viral contributions to terrestrial ecosystem processes remains a challenge, as temporal dynamics are difficult to unravel in the spatially and physicochemically heterogeneous soil environment. In Mediterranean grasslands, the first rainfall after seasonal drought provides an ecosystem reset, triggering microbial activity during a tractable window for capturing short-term dynamics. In this report we simulated precipitation in microcosms from four distinct dry grassland soils and generated 144 viromes, 84 metagenomes and 84 16S ribosomal RNA gene amplicon datasets to characterize viral, prokaryotic and relic DNA dynamics over 10 days. Vastly different viral communities in each soil followed remarkably similar successional trajectories. Wet-up triggered a significant increase in viral richness, followed by extensive compositional turnover. Temporal succession in prokaryotic communities was much less pronounced, perhaps suggesting differences in the scales of activity captured by viromes (representing recently produced, ephemeral viral particles) and total DNA. Still, differences in the relative abundances of Actinobacteria (enriched in dry soils) and Proteobacteria (enriched in wetted soils) matched those of their predicted phages, indicating viral predation of dominant bacterial taxa. Rewetting also rapidly depleted relic DNA, which subsequently reaccumulated, indicating substantial new microbial mortality in the days after wet-up, particularly of the taxa putatively under phage predation. Production of abundant, diverse viral particles via microbial host cell lysis appears to be a conserved feature of the early response to soil rewetting, and results suggest the potential for ‘Cull-the-Winner’ dynamics, whereby viruses infect and cull but do not decimate dominant host populations.
54 ENVIRONMENTAL SCIENCES↗
Early-Career Reflections: Curiosity Saves the Researcher
Defining your own research questions and working with a clear direction in mind is key to a successful career in academia. Evangelia Spyrou explains how following her thirst for knowledge in her specific area of interest prepared her for when research opportunities arose.
electricity↗
Multi-cation perovskites prevent carrier reflection from grain surfaces
The composition of perovskite has been optimized combinatorially such that it often contains six components (A x B y C 1-x-y PbX z Y 3-z ) in state-of-art perovskite solar cells. Questions remain regarding the precise role of each component, and the lack of a mechanistic explanation limits the practical exploration of the large and growing chemical space. Here, aided by transient photoluminescence microscopy, we find that, in perovskite single crystals, carrier diffusivity is in fact independent of composition. In polycrystalline thin films, the different compositions play a crucial role in carrier diffusion. In this work, we report that methylammonium (MA)-based films show a high carrier diffusivity of 0.047 cm 2 s -1 , while MA-free mixed caesium-formamidinium (CsFA) films exhibit an order of magnitude lower diffusivity. Elemental composition studies show that CsFA grains display a graded composition. This curtails electron diffusion in these films, as seen in both vertical carrier transport and surface potential studies. Incorporation of MA leads to a uniform grain core-to-edge composition, giving rise to a diffusivity of 0.034 cm 2 s -1 in CsMAFA films. A model that invokes competing crystallization processes allows us to account for this finding, and suggests further strategies to achieve homogeneous crystallization for the benefit of perovskite optoelectronics.
36 MATERIALS SCIENCE↗
Root exudate composition reflects drought severity gradient in blue grama (Bouteloua gracilis)
Abstract Plant survival during environmental stress greatly affects ecosystem carbon (C) cycling, and plant–microbe interactions are central to plant stress survival. The release of C-rich root exudates is a key mechanism plants use to manage their microbiome, attracting beneficial microbes and/or suppressing harmful microbes to help plants withstand environmental stress. However, a critical knowledge gap is how plants alter root exudate concentration and composition under varying stress levels. In a greenhouse study, we imposed three drought treatments (control, mild, severe) on blue grama ( Bouteloua gracilis Kunth Lag. Ex Griffiths), and measured plant physiology and root exudate concentration and composition using GC–MS, NMR, and FTICR. With increasing drought severity, root exudate total C and organic C increased concurrently with declining predawn leaf water potential and photosynthesis. Root exudate composition mirrored the physiological gradient of drought severity treatments. Specific compounds that are known to alter plant drought responses and the rhizosphere microbiome mirrored the drought severity-induced root exudate compositional gradient. Despite reducing C uptake, these plants actively invested C to root exudates with increasing drought severity. Patterns of plant physiology and root exudate concentration and composition co-varied along a gradient of drought severity.
59 BASIC BIOLOGICAL SCIENCES↗
Molecular diversity of dissolved organic matter reflects macroecological patterns in river networks
Deciphering dissolved organic matter (DOM) molecular complexity is crucial for understanding ecosystem function. Using the continental-scale Worldwide Hydrobiogeochemistry Observation Network for Dynamic Rivers Systems (WHONDRS) Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) dataset, we reveal fundamental scaling patterns of DOM chemodiversity with watershed characteristics. Analysis of 54 river sites shows local and regional watershed features significantly influence DOM chemodiversity (2500–8718 unique formulae), exhibiting consistent scaling patterns across compound classes and a novel latitudinal gradient (decreasing diversity with increasing latitude). Scaling relationships for DOM composition vary by compound class. Crucially, the scaling parameters (B, baseline chemodiversity; Z, sensitivity) are linearly interrelated. This B–Z relationship is most robust for potentially bio-labile carbohydrates (coefficient of determination R 2 ≈ 0.85), diminishing for recalcitrant, plant-derived molecules (such as lignin), and indicates (potential) biolability-dependent coupling between baseline diversity and environmental responsiveness. These quantitative scaling relationships, with scaling exponents ranging from − 2.1 to 2.2 across compound classes, enable prediction of DOM composition across watersheds, offering a framework to understand ecosystem responses to environmental change. This research bridges biogeochemistry and ecology, providing tools to anticipate molecular transformations across scales.
59 BASIC BIOLOGICAL SCIENCES↗
Resonant anomalous X-ray reflectivity for revealing ion distributions near electrodes
Energy storage devices–batteries, for example–consist of an electrolyte sandwiched between two charged electrodes through which current can pass. The metal ion organization at the electrode–electrolyte interface, commonly referred to as the electric double layer, determines the efficiency and stability of the device. There are two major challenges with experimentally characterizing the metal ion distribution at the interface.
42 ENGINEERING↗
AI reflections in 2019
There is no shortage of opinions on the impact of artificial intelligence and deep learning. For this work, we invited authors of Comment and Perspective articles that we published in roughly the first half of 2019 to look back at the year and give their thoughts on how the issue they wrote about developed.
97 MATHEMATICS AND COMPUTING↗
New reflections on hard X-ray photon-in/photon-out spectroscopy
Analysis of the electronic structure and local coordination of an element is an important aspect in the study of the chemical and physical properties of materials. This is particularly relevant at the nanoscale where new phases of matter may emerge below a critical size. X-ray emission spectroscopy (XES) at synchrotron radiation sources and free electron lasers has enriched the field of X-ray spectroscopy. Additionally, the spectroscopic techniques derived from the combination of X-ray absorption and emission spectroscopy (XAS–XES), such as resonant inelastic X-ray scattering (RIXS) and high energy resolution fluorescence detected (HERFD) XAS, are an ideal tool for the study of nanomaterials. New installations and beamline upgrades now often include wavelength dispersive instruments for the analysis of the emitted X-rays. With the growing use of XAS–XES, scientists are learning about the possibilities and pitfalls. We discuss some experimental aspects, assess the feasibility of measuring weak fluorescence lines in dilute, radiation sensitive samples, and present new experimental approaches for studying magnetic properties of colloidal nanoparticles directly in the liquid phase.
47 OTHER INSTRUMENTATION↗
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
We report the findings of a hackathon focused on exploring the diverse applications of large language models in molecular and materials science.
36 MATERIALS SCIENCE↗
: a selection tool for asymmetric reflections of spherically bent crystal analyzers for high resolution X-ray spectroscopy
is an open-source program to select asymmetric geometries of spherically bent crystal analyzers for X-ray spectroscopy, thus achieving improved energy resolution and operational flexibility.
Abramson, Jared E. [Physics Department, University↗
Brewster angle optical reflection observation of self-limiting nanoparticle monolayer self-assembly at a liquid/liquid interface
Explore the source record for details and available documents.
99 GENERAL AND MISCELLANEOUS↗
Andreev reflection between aluminum and graphene across van der Waals barriers
We present planar aluminum superconductor–graphene junctions whose hybrid interface is engineered for couplings ranging from tunneling to the strongly coupled regime by employing an atomically thin van der Waals tunneling barrier. Without the vdW barrier, we find Al makes strongly coupled contacts with the fully proximities graphene channel underneath. Using a large band gap hexagonal boron nitride (hBN) barrier, we find the junctions always remain in the weak coupling regime, exhibiting tunneling characteristics. Using monolayer semi-conducting transition metal dichalcogenides (TMDs) such as MoS2, we realize intermediate coupling with enhanced junction conductance due to the Andreev process. In this intermediate regime, we find that junction resistance changes in discrete steps when sweeping a perpendicular magnetic field. The period of the resistance steps in the magnetic field is inversely proportional to the junction area, suggesting the physical origin of our observations is due to magnetic-field-induced vortex formation in the planar junction.
Physics↗
Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration
This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles in a dispersed multiphase setup using convolutional neural network (CNN). The considered problem involves rotational symmetry about the mean velocity (streamwise) direction. Thus, this work enforces this symmetry using SE(3)-equivariant, special Euclidean group of dimension 3, CNN architecture, which is translation and three-dimensional rotation equivariant. This study mainly explores the generalization capabilities and benefits of a SE(3)-equivariant network. Accurate synthetic flow fields for Reynolds number and particle volume fraction combinations spanning over a range of [86.22, 172.96] and [0.11, 0.45], respectively, are produced with careful application of symmetry-aware data-driven approach.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Transducerless time domain reflectance measurement of semiconductor thermal properties
The dominant techniques for laser-based thermal measurement, time-domain thermoreflectance (TDTR) and frequency-domain thermoreflectance, generally require that samples be coated with a metal transducer layer. The properties of the transducer and its interface to the sample are then important to the interpretation of results, and heat conduction in the transducer layer can limit measurements of ultra-thin samples or in-plane conductivity. Here, we demonstrate the feasibility of measuring semiconductor samples using standard TDTR instrumentation, without the use of a metal transducer. A mathematical model accounting for non-zero laser penetration depth as well as both thermal and charge carrier effects is presented, and the ability of the model to describe and fit to experimental data for silicon and germanium samples is demonstrated. Though at this stage measurement uncertainties are larger than for traditional TDTR, we anticipate that the use of this technique will expand the range of samples that can be measured with existing TDTR setups.
36 MATERIALS SCIENCE↗
A five-year milestone: reflections on advances and limitations in GeoAI research
The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.
97 MATHEMATICS AND COMPUTING↗