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Van der Waals magnets are an emerging material family for investigating light-matter interactions and spin-correlated excitations. Here, we report the discovery of a photo-induced state with a lifetime of 17 ps in the van der Waals antiferromagnet NiPS3, which appears exclusively with resonant pumping at 1.476 eV in the antiferromagnetic state. The long-lived state comes with a negative photoconductivity, a characteristic optical response of population inversion. Our findings demonstrate a promising pathway to potentially achieve long-lived lasing at terahertz frequencies in reduced dimensions. 

Nanoparticle organic hybrid materials (NOHMs) have been proposed as excellent electrolytes for combined CO₂capture and electrochemical conversion due to their conductive nature and chemical tunability. However, CO₂capture behavior and transport properties of these electrolytes after CO₂capture have not yet been studied. Here, we use a variety of nuclear magnetic resonance (NMR) techniques to explore the carbon speciation and transport properties of branched polyethylenimine (PEI) and PEI-grafted silica nanoparticles (denoted as NOHM-I-PEI) after CO₂capture. Quantitative¹³C NMR spectra collected at variable temperatures reveal that absorbed CO₂exists as carbamates (RHNCOO⁻or RR′NCOO⁻) and carbonate/bicarbonate (CO₃²⁻/HCO₃⁻). The transport properties of PEI and NOHM-I-PEI studied using¹H pulsed-field-gradient NMR, combined with molecular dynamics simulations, demonstrate that coulombic interactions between negatively and positively charged chains dominate in PEI, while the self-diffusion in NOHM-I-PEI is dominated by silica nanoparticles. These results provide strategies for selecting adsorbed forms of carbon for electrochemical reduction. 

Abstract It has been widely reported that isoprene emissions from the Arctic ecosystem have a strong temperature response. Here we identify sedges (Carexspp. andEriophorumspp.) as key contributors to this high sensitivity using plant chamber experiments. We observe that sedges exhibit a markedly stronger temperature response compared to that of other isoprene emitters and predictions by the widely accepted isoprene emission model, the Model of Emissions of Gases and Aerosols from Nature (MEGAN). MEGAN is able to reproduce eddy-covariance flux observations at three high-latitude sites by integrating our findings. Furthermore, the omission of the strong temperature responses of Arctic isoprene emitters causes a 20% underestimation of isoprene emissions for the high-latitude regions of the Northern Hemisphere during 2000-2009 in the Community Land Model with the MEGAN scheme. We also find that the existing model had underestimated the long-term trend of isoprene emissions from 1960 to 2009 by 55% for the high-latitude regions. 

Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE models memorize the entity name patterns to make RE predictions while ignoring the textual context. This motivates us to raise the question: are RE models robust to the entity replacements? In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. We observe the 30% - 50% F1 score drops on the state-of-the-art RE models under entity replacements. These results suggest that we need more efforts to develop effective RE models robust to entity replacements. We release the source code at https://github.com/wangywUST/RobustRE. 

Abstract The investigation of twisted stacked few‐layer MoS₂has revealed novel electronic, optical, and vibrational properties over an extended period. For the successful integration of twisted stacked few‐layer MoS₂into a wide range of applications, it is crucial to employ a noninvasive, versatile technique for characterizing the layered architecture of these complex structures. In this work, we introduce a machine learning‐assisted low‐frequency Raman spectroscopy method to characterize the twist angle of few‐layer stacked MoS₂samples. A feedforward neural network (FNN) is utilized to analyze the low‐frequency breathing mode as a function of the twist angle. Moreover, using finite difference method (FDM) and density functional theory (DFT) calculations, we show that the low‐frequency Raman spectra of MoS₂are mainly influenced by the effect of the nearest and second nearest layers. A new improved linear chain model (TA‐LCM) with taking the twist angle into the consideration is developed to understand the interlayer breathing modes of stacked few‐layer MoS₂. This approach can be extended to other 2D materials systems and provides an intelligent way to investigate naturally stacked and twisted interlayer interactions.