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As conventional electronic materials approach their physical limits, the application of ultrafast optical fields to access transient states of matter cap- tures imagination. The inversion symmetry governs the optical parity selection rule, differentiating between accessible and inaccessible states of matter. To circumvent parity-forbidden transitions, the common practice is to break the inversion symmetry by material design or external fields. Here we report how the application of femtosecond ultraviolet pulses can energize a parity-forbidden dark exciton state in black phosphorus while maintaining its intrinsic material symmetry. Unlike its conventional bandgap absorption in visible-to-infrared, femtosecond ultraviolet excitation turns on efficient Coulomb scattering, promoting carrier multiplication and electronic heating to ~3000 K, and consequently populating its parity-forbidden states. Interfero- metric time- and angle-resolved two-photon photoemission spectroscopy reveals dark exciton dynamics of black phosphorus on ~100 fs time scale and its anisotropic wavefunctions in energy-momentum space, illuminating its potential applications in optoelectronics and photochemistry under ultraviolet optical excitation. 

The solid electrolyte interphase (SEI) layer plays a critical role in the aging and degradation of lithium-ion batteries (LIBs), directly influencing their performance and longevity. This paper presents a physics-based model that quantitatively characterizes SEI layer growth in cylindrical LIBs by incorporating ionic current density as a governing parameter. The presented approach captures localized SEI dynamics by coupled state-space Eqs. (SSEs) within an convex optimization framework. The model accounts for both uniform and nonlinear SEI growth phases, predicting capacity fade and impedance evolution over cycling aging. Validation against experimental charge-discharge profiles, electrochemical impedance spectroscopy (EIS) characterization, and equivalent circuit modeling demonstrates the model’s precision in tracking SEI-related degradation. The proposed framework offers a robust, interpretable, and computationally efficient tool for battery diagnostics and lifetime prediction. 

Molecular materials offer a boundless design palette for light absorption and charge transport in both natural photosynthesis and engineered photovoltaics. They function in combination as chromophores, donors, conductors, and acceptors, enabling the excitation and charge carrier transport through space and wire-like intramolecular pathways. Although quantum coher- ence is believed to enhance photoexcitation and photoinduced charge transfer, fluctuating and inhomogeneous environments accelerate decoherence. Here, we assemble a nanoporous medium consisting of a templated bipyridyl ethylene (BPE) molecule array on a Ag(111) surface that functions as an exceptional intermolecular nonnuclear quantum well conductor of coherent electron waves spanning over 20 Å length. Time-periodic driving of the Ag/BPE interface by femtosecond pulses promotes electrons into a ladder of Floquet quasi-energy donor states, where intermolecular quantum well states act as a resonant doorway for coherent electron transport into BPE/vacuum image potential acceptor states. The bifurcation of electron passage between the Floquet donor ladder and the charge transfer acceptor channel is recorded by projecting the active electrons into the photoemission continuum in an interferometric time- and angle-resolved multiphoton photoemission experiment. We find that exceptional decoupling of electrons from the metal substrate by the molecule- dressed vacuum preserves the coherence on the ∼150 fs time scale. This offers a new paradigm for quantum state design where a molecule-dressed vacuum mediates coherent electron transport in nanoporous molecular architectures. 

Cross-Document Event Coreference (CDEC) annotation is challenging and difficult to scale, resulting in existing datasets being small and lacking diversity. We introduce a new approach leveraging large language models (LLMs) to decontextualize event mentions, by simplifying the document-level annotation task to sentence pairs with enriched context, enabling the creation of Richer EventCorefBank (RECB), a denser and more expressive dataset annotated at faster speed. Decontextualization has been shown to improve annotation speed without compromising quality and to enhance model performance. Our baseline experiment indicates that systems trained on RECB achieve comparable results on the EventCorefBank(ECB+) test set, showing the high quality of our dataset and its generalizability on other CDEC datasets. In addition, our evaluation shows that the strong baseline models are still struggling with RECB comparing to other CDEC datasets, suggesting that the richness and diversity of RECB present significant challenges to current CDEC systems. 

Molecular constructs define the elementary units in porous materials for efficient CO2 capture. The design of appropriate interpore and intermolecular space is crucial to stabilize CO2 molecules and maximize the capacity. While the molecular construct usually has a fixed dimension, whether its intermolecular space could be self-adjustable during CO2 capture and release, behaving as a balloon, has captured imagination. Here we report a flexible intermolecular space of the double chain structure of self-assembled 1,4-phenylene diisocyanide (PDI) molecules on Ag(110) surface, which dynamically broadens and recovers during the CO2 capture and release. The incipient PDI double chains organize along the [001] direction of Ag(110), in which individual PDI molecules stand up in a zigzag order with the interchain width defined by twice the Ag lattice distance along [11¯0] direction (2α[11¯0]). When CO2 molecules are introduced, they assemble to occupy the interchain spaces, expanding the interchain width to 3α[11¯0], 4α[11¯0] and 5α[11¯0]. Warming up the sample leads to the thermally-driven CO2 desorption that recovers the original interchain space. High-resolution scanning tunneling microscopy (STM) jointly with density functional theory (DFT) calculations determine the structural and electronic interactions of CO2 molecules with the dynamical PDI structures, providing a molecular-level perspective for the design of a self-adjustable metal-organic construct for reversible gas capture and release. 

A parameterized mathematical model for Lithium-ion battery cell is presented in this paper for performance analysis with a particular focus on battery discharge behavior and electrochemical impedance spectroscopy profile. The model utilizes various physical properties as input and consists of two major sub-models in a complementary manner. The first sub-model is an adapted Doyle-Fuller-Newman (DFN) framework to simulate electrochemical, thermodynamic, and transport phenomena within the battery. The second sub-model is a calibrated solid-electrolyte interphase (SEI) layer formation model. This model emphasizes the electrical dynamic response in terms of the reaction process, layer growth, and conductance change. The equivalent circuit component values are derived from the outputs of both sub-models, reflecting the battery’s changing physical parameters. The simulated discharge curves and electrochemical impedance spectroscopy (EIS) profiles are then provided with a comparison against empirical results for validation, which exhibit good agreement. This modeling methodology aims to bridge the gap between the physical model and the equivalent circuit model (ECM), enabling more accurate battery performance predictions and operation status tracking. 

We explore using LLMs, GPT-4 specifically, to generate draft sentence-level Chinese Uniform Meaning Representations (UMRs) that human annotators can revise to speed up the UMR annotation process. In this study, we use few-shot learning and Think-Aloud prompting to guide GPT-4 to generate sentence-level graphs of UMR. Our experimental results show that compared with annotating UMRs from scratch, using LLMs as a preprocessing step reduces the annotation time by two thirds on average. This indicates that there is great potential for integrating LLMs into the pipeline for complicated semantic annotation tasks.