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Northern peatlands are experiencing more frequent and severe fire events as a result of changing climate conditions. Recent studies show that such a fire-regime change imposes a direct climate-warming impact by emitting large amounts of carbon into the atmosphere. However, the fires also convert parts of the burnt biomass into pyrogenic carbon. Here, we show a potential climate-cooling impact induced by fire-derived pyrogenic carbon in laboratory incubations. We found that the accumulation of pyrogenic carbon reduced post-fire methane production from warm (32 °C) incubated peatland soils by 13–24%. The redox-cycling, capacitive, and conductive electron transfer mechanisms in pyrogenic carbon functioned as an electron snorkel, which facilitated extracellular electron transfer and stimulated soil alternative microbial respiration to suppress methane production. Our results highlight an important, but overlooked, function of pyrogenic carbon in neutralizing forest fire emissions and call for its consideration in the global carbon budget estimation. Warmer and drier conditions are increasing the frequency of forest fires, which in turn produce pyrogenic carbon. Here the authors show that accumulation of pyrogenic carbon can suppress post-fire methane production in northern peatlands and can effectively buffer fire-derived greenhouse gas emissions.

Surface functional groups constitute major electroactive components in pyrogenic carbon. However, the electrochemical properties of pyrogenic carbon matrices and the kinetic preference of functional groups or carbon matrices for electron transfer remain unknown. Here we show that environmentally relevant pyrogenic carbon with average H/C and O/C ratios of less than 0.35 and 0.09 can directly transfer electrons more than three times faster than the charging and discharging cycles of surface functional groups and have a 1.5 V potential range for biogeochemical reactions that invoke electron transfer processes. Surface functional groups contribute to the overall electron flux of pyrogenic carbon to a lesser extent with greater pyrolysis temperature due to lower charging and discharging capacities, although the charging and discharging kinetics remain unchanged. This study could spur the development of a new generation of biogeochemical electron flux models that focus on the bacteria–carbon–mineral conductive network. Electron transfer reactions govern most biogeochemical processes, yet we have a limited knowledge of the electrochemistry of pyrogenic carbon, a major component of organic matter. Here, the authors quantify electron transfers between pyrogenic carbon and mineral phases under different pyrolysis temperatures.

This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm−3). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value.

Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a novel self-standing mission dedicated to observing the solar wind–magnetosphere coupling via simultaneous in situ solar wind/magnetosheath plasma and magnetic field measurements, soft X-ray images of the magnetosheath and polar cusps, and UV images of global auroral distributions. While analyzing the observed images after the launch of SMILE, it will be a challenging task to reconstruct the 3-dimensional surface of the magnetopause from 2-dimensional images. Therefore, one of the most important key issues about SMILE is the reconstruction of magnetopause from X-ray images. This paper will review four main approaches have been developed so far, namely, the boundary fitting approach (BFA), the tangent fitting approach (TFA), the tangential direction approach (TDA), and the computed tomography approach (CTA). We will discuss their scope of application and pros and cons, and hopefully inspire future efforts.

Imaging has been an important strategy for exploring space weather. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint Chinese Academy of Sciences (CAS) and European Space Agency (ESA) mission, aiming at studying the interaction between Earth’s magnetosphere and solar wind near the subsolar point via soft X-ray imaging. As the boundary of Earth’s magnetosphere, magnetopause is a significant detection target to mirror solar wind’s change for the SMILE mission. In preparation for inverting three-dimensional magnetopause, we proposed an OESA-UNet model to detect the magnetopause position. The model obtains magnetopause with a U-shaped structure, in an end-to-end manner. Inspired by attention mechanisms, these blocks are integrated into ours. OESA-UNet captures low and high-level feature maps by adjusting the receptive field for precise localization. Adaptively pre-processing the image provides a prior for the network. Availability metrics are designed to determine whether it can serve three-dimensional inversion. Lastly, we provided ablation and comparison experiments by qualitative and quantitative analysis. Our recall, precision, and f1 score are 93.8%, 92.1%, and 92.9%, respectively, with an average angle deviation of 0.005 under the availability metrics. Results indicate that OESA-UNet outperforms other methods. It can better serve the purpose of magnetopause tracing from an X-ray image.

We propose to use the Moon as a platform to obtain a global view of Earth’s magnetosphere by a Lunar-based Soft X-ray Imager (LSXI). LSXI is a wide field-of-view Soft X-ray telescope, which can obtain X-ray images of Earth’s magnetosphere based on the solar wind charge exchange (SWCX) X-ray emission. Global perspective is crucial to understand the overall interaction of the solar wind with magnetosphere. LSXI is capable of continuously monitoring the evolution of geospace conditions under the impact of the solar wind by simultaneous observation of the bow shock, magnetosheath, magnetopause and cusps for the first time. This proposal is answering the call for the Chinese Lunar Exploration Program Phase IV.

Extracting the peak value of the X-ray signal in the original magnetopause detection method of soft X-ray imaging (SXI) for the SMILE satellite is problematic because of the unclear interface of the magnetosphere system under low solar wind density and the short integration time. Herein, we propose a segmentation algorithm for soft X-ray images based on depth learning, we construct an SXI simulation dataset, and we segment the magnetospheric system by learning the spatial structure characteristics of the magnetospheric system image. Then, we extract the maximum position of the X-ray signal and calculate the spatial configuration of the magnetopause using the tangent fitting approach. Under a uniform universe condition, we achieved a pixel accuracy of the maximum position of the photon number detected by the network as high as 90.94% and contained the position error of the sunset point of the 3D magnetopause below 0.2 RE. This result demonstrates that the proposed method can detect the peak photon number of magnetospheric soft X-ray images with low solar wind density. As such, its use improves the segmentation accuracy of magnetospheric soft X-ray images and reduces the imaging time requirements of the input image.

In this paper, we summarize the research on photodarkening in optical fibers. The causes of photodarkening in fiber, the influence of photodarkening on fiber laser, the experimental device of photodarkening, and the mathematical model used to study the phenomenon of photodarkening are described in detail. At the end of the paper, we summarize the means and methods to suppress photodarkening.

Observations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen & Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (>3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays.

Mid-infrared (MIR) ultrashort laser pulses have a wide range of applications in the fields of environmental monitoring, laser medicine, food quality control, strong-field physics, attosecond science, and some other aspects. Recent years have seen great developments in MIR laser technologies. Traditional solid-state and fiber lasers focus on the research of the short-wavelength MIR region. However, due to the limitation of the gain medium, they still cannot cover the long-wavelength region from 8 to 20 µm. This paper summarizes the developments of 8–20 μm MIR ultrafast laser generation via difference frequency generation (DFG) and reviews related theoretical models. Finally, the feasibility of MIR power scaling by nonlinear-amplification DFG and methods for measuring the power of DFG-based MIR are analyzed from the author’s perspective.