Mechanistic Insights into Li+ Transport Enabled by Isolated Sulfur Species in Li3PS4 Glasses

Authors: J. Pawelko, X. Rocquefelte, A. Tetenoire, D. Le Coq, L. Calvez, E. Furet

Published: February 26, 2026

All-solid-state lithium-ion batteries have renewed interest in high-performance solid electrolytes. Li3PS4 (Li2S-P2S5) glasses are among the most studied due to their high ionic conductivity, traditionally ascribed to rotational motion of polyhedral units facilitating Li+ migration. Using ab initio molecular dynamics, we investigate Li-ion diffusion in Li3PS4 glass, demonstrating that our structural model reproduces experimental neutron and X-ray diffraction patterns and conductivity measurements. Importantly, we identify a previously unrecognized diffusion mechanism: Li+ ions near isolated sulfur species (Sn with n = 1, 3) display significantly enhanced mobility, with atomic displacements up to 1.7 greater than those associated with bulkier polyhedral units. These results highlight the critical role of free sulfur species in promoting fast ionic transport, providing insights for the rational design of glass compositions with optimized conductivity for solid-state battery applications

Detection of circumstellar disks around nearby young brown dwarf candidates

Authors: N. Phan-Bao, M. S. Bessell, E. L. Martín

Published: February 26, 2026

It is important to detect and study circumstellar disks around late-M and brown dwarfs of nearby young associations to understand how these very low-mass objects form and how rocky planets form around them. The detection of new very low-mass members of nearby young associations will also significantly improve our current understanding of young associations. We searched for new young very low-mass members with circumstellar disks in a sample of 3928 candidates. We constructed spectral energy distributions of all candidates using observational photometric data from DENIS, 2MASS, and WISE and trigonometric parallaxes from Gaia to detect infrared excess emission that indicates the presence of circumstellar disks. We then followed up spectroscopic observations of candidates to search for lithium to confirm their youth. The H_alpha emission line was used to detect accretion. We detected 23 among the 3928 candidates with circumstellar disks: Ten objects are new, and 13 were previously reported in the literature. Our mass estimates also indicate that 21 are brown dwarf candidates and 2 are very low-mass stars. DENIS J0534552-104808 has a Gaia distance of 238 pc and might be the first brown dwarf candidate member of a foreground population in front of the Orion D cloud. This foreground population is probably associated with the supergiant kappa Ori. Based on our spectroscopic observations, we detected lithium in 11 candidates. We also identified seven accretors and one potential accretor. The intense long-lived accretion detected in DENIS-P J0500245-333042, a 20 Myr old brown dwarf candidate may be additional evidence to favor the formation of rocky planets around very low-mass objects.

Quantum squeezing in an all-resonant periodically poled lithium niobate microresonator

Authors: Xinyi Ren, Reshma Kopparapu, Tushar Sanjay Karnik, Chun-Ho Lee, Kiwon Kwon, Clayton Cheung, Yue Yu, Shi-Yuan Ma, Bo-Han Wu, Ran Yin, Lian Zhou, Quntao Zhuang, Dirk Englund, Zaijun Chen, Mengjie Yu

Published: February 26, 2026

Quantum noise limits the sensitivity of optical measurements, but squeezed states of light enable quantum-enhanced metrology, sensing, and information processing. Most on-chip squeezed-light sources rely on Kerr ($χ^{(3)}$) nonlinearities, remain limited by pump power and excess loss constraints. Quadratic ($χ^{(2)}$) platforms instead provide stronger parametric interactions, lower pump power requirements, and greater spectral engineering flexibility. Here, we demonstrate strong, broadband squeezed-light generation on a thin-film lithium niobate (TFLN) photonic chip using a dual-resonant optical parametric amplifier implemented in a single periodically poled LN (PPLN) microresonator. Near-full-depth domain inversion is achieved simultaneously with highly over-coupled resonances, exhibiting escape efficiencies exceeding 90% and intrinsic quality factors above 2.5 million in a 0.6 mm$^2$ X-cut TF-PPLN resonator, enabling efficient squeezing at 1587 nm when pumped at 793.5 nm. Operating in the continuous-wave regime, we directly measure -0.81 dB of squeezing below the shot-noise limit with a pump power of 27 mW, together with +4.29 dB of anti-squeezing. From these measurements, we infer an on-chip squeezing level of -7.52 dB $\pm$ 0.22 dB (95% confidence interval: [-7.96,-7.10] dB), and an on-chip anti-squeezing level of +9.62 dB $\pm$ 0.25 dB. We demonstrate single-mode squeezing at degeneracy with a squeezed-light spectrum exceeding 10.3 THz. This work reports the highest squeezing ratio among integrated $χ^{(2)}$ cavity platforms and the first quasi-phase matched, fully resonant $χ^{(2)}$ cavity squeezer on chip, establishing a scalable route to fully integrated power-efficient squeezed-light sources for quantum-enhanced sensing and metrology.

Identify ~20,000 Li-rich Giants in the LAMOST Low-Resolution Survey

Authors: Ming-Yi Ding, Liang Wang, Jian-Rong Shi, Hong-Liang Yan, Chun-Qian Li, Jing Chen, Kai Zhang, Yu-Lu Liang, Zi-Chong Zhang, Xu-Hang Yin, Qin-Yang Dong

Published: February 26, 2026

Li-rich giants serve as valuable tracers of stellar evolution and surface enrichment processes, for which a statistically large and homogeneous sample is crucial. Using the massive low-resolution ($R \sim 1800$) spectroscopic dataset from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey, we systematically search for Li-rich giants by determining their stellar lithium abundances through template matching of the Li I 6708 Å absorption line with a grid of synthetic spectra generated from ATLAS9 model atmospheres. The derived abundances are validated against previous high-resolution studies, showing good consistency with a mean absolute error of about 0.15 dex. Therefore, we adopt a threshold of $A(\rm{Li}) > 1.65$ dex to select Li-rich candidates, followed by visual inspection to ensure the reliability of each detection. Eventually, more than 20,000 Li-rich giants are identified, corresponding to approximately 2.5% of all giants in LAMOST DR9. We also investigate the occurrence rate of Li-rich giants in different evolutionary stages. This work presents a large and homogeneous catalog of Li-rich giants derived from the LAMOST low-resolution survey, which provides a reliable and valuable dataset for future studies of stellar evolution and lithium enrichment in evolved stars.

Using Lithium and Beryllium to Study Structure and Evolution of Rotating Stars: Spite Plateau of Halo Stars

Authors: Wuming Yang, Shuya Dou, Xiangcun Meng, Yaqian Wu, Shaolan Bi

Published: February 26, 2026

The observed lithum (Li) abundance of Galactic halo stars mainly fall within the range of 2.0--2.4 dex. This nearly constant value, known as the Spite plateau, is approximately a factor of three lower than the value predicted from cosmic microwave background measurements and standard Big Bang Nucleosynthesis (BBN) calculations. This discrepancy -- referred to as the cosmological Li problem -- is considered a potential indication of new physics or astrophysical processes. We employed models incorporating gravitational settling, diffusion, rotation, and magnetic fields to explain the Spite plateau. The rotating models predict that Li abundances in stars with ages of roughly 8--13 Gyr and effective temperatures between 6400 and 5900 K generally fall within 2.0--2.4 dex, forming a well-defined Li plateau, followed by a sharp decline in Li abundance down to about 5200 K. The Li plateau results from the combined effects of variations in convection zone depth, gravitational settling, diffusion, rotation, and magnetic fields. For red giant branch stars with $T_{\mathrm{eff}} \lesssim$ 5200 K, the rotating models predict another Li plateau with an abundance of about 1.0 dex. These results are in good agreement with observations. Moreover, the initial Li abundance of 2.72 dex adopted in the models matches the BBN prediction, implying that the Li problem arises from stellar Li depletion. Furthermore, the rotating models also reproduce the Li and Be distributions of the sample that exhibit the Spite plateau meltdown and Be deviation.

Non-reciprocal electrooptic intermodal scattering with momentum engineered RF waves

Authors: Jieun Yim, Gwan In Kim, Violet Workman, Seho Kim, Omar A. Barrera, Ruochen Lu, Gaurav Bahl

Published: February 25, 2026

Spatiotemporal modulation approaches have been often employed as alternatives for producing optical non-reciprocity without magneto-optic materials. Unidirectional inter-modal scattering, enabled by either acousto-optic or electro-optic (EO) modulation, is a promising method in this category as it can directly modify optical dispersions and even enables linear non-reciprocal photonic devices in the strong coupling limit. While EO approaches are often preferred for their practicality, it is challenging to generate the large spatiotemporal momentum required for inter-modal phase matching without EO drive schemes involving multiple drive stimuli. Here, we demonstrate highly selective non-reciprocal inter-modal EO scattering enabled by a single high-index radiofrequency (RF) traveling wave stimulus. Our experimental demonstration is performed on a thin-film lithium niobate integrated photonics platform, in which we engineer a slow-wave radiofrequency (SWRF) transmission line with an effective RF index > 9 that natively generates the required RF momentum while simultaneously maintaining strong RF-optical mode overlap. By additionally engineering the interaction length, we achieve a directional ~20 dB non-reciprocal scattering contrast. The SWRF architecture provides a scalable route to magnetic-free non-reciprocity and establishes momentum-engineered RF waves as a powerful tool for next-generation, fully integrated non-reciprocal photonic systems.

A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy

Authors: Naoki Wada, Yuta Kimura, Masaichiro Mizumaki, Koji Amezawa, Ichiro Akai, Toru Aonishi

Published: February 23, 2026

Quantitative understanding of coupled reaction and transport processes in lithium-ion battery (LIB) composite electrodes remains challenging because key internal states cannot be measured directly. In this study, we develop a physics-integrated, data-driven analysis pipeline to estimate internal electrochemical states from operando microscopic X-ray absorption fine structure ($μ$-XAFS) hyperspectral data of LIB cathodes with LiPF$_6$ electrolyte. State-of-charge (SOC) maps are first constructed from Co K-edge spectra. To resolve ambiguities in the two-phase reaction region, a physics-regularized three-layer neural network is introduced, enforcing spatial continuity of SOC and current conservation. The inferred SOC dynamics are then incorporated into a Kirchhoff-based Markov random field framework that integrates Kirchhoff's current and voltage laws, Ohm's law, and a symmetric Butler-Volmer relation to estimate interfacial current, ionic current, electrolyte potential, and effective ionic conductivity. Application to composite electrodes with different initial electrolyte concentrations (0.3, 1, and 2M LiPF$_6$) reveals distinct reaction propagation behaviors governed by electrolyte concentration-dependent conductivity. The inferred electrolyte concentration distributions show qualitative agreement with independent operando X-ray transmission imaging performed on LIB composite cathodes employing a LiAsF$_6$ electrolyte. This framework enables quantitative visualization of otherwise inaccessible internal transport phenomena.

Transport in close-packed solids with stacking defects

Authors: C. M. Wilson, R. Ganesh, K. V. Samokhin

Published: February 20, 2026

Lithium and sodium are the only solids that are known to lose crystalline order upon cooling. The seemingly-disordered low-temperature phase shows signatures of various close-packed structures. The lack of order has been attributed to a hidden gauge symmetry that arises when electrons from one layer can hop to a neighbouring layer but not further. It makes all close-packed structures nearly degenerate and leads to ``structural frustration''. In this article, we examine whether this symmetry is reflected in transport signatures. Taking advantage of in-plane translational periodicity, we map the bulk Bloch Hamiltonian to an effective one-dimensional chain, with stacking disorder mapping to random phases of the hopping amplitudes. We derive an explicit analytic form for the Green's function of electrons and use it to calculate conductance of a bulk crystal. When hopping in the effective one-dimensional chain is restricted to nearest neighbours, conductance is completely insensitive to phase disorder, which indicates that all close-packed structures exhibit the same conductance. We show that the leading correction that can differentiate between close-packed structures arises from hopping to the next-nearest-neighbour layer, equivalent to second-neighbour hopping in the chain model. This process appears when a pair of next-neighbour layers are aligned in a certain way, e.g., at an hcp-like stacking fault within an fcc background. With this hopping included, conductance becomes sensitive to the precise arrangement of layers. When multiple stacking faults are present, the conductance decreases with increasing system size, as expected from Anderson localization. Our results are applicable to pressurized lithium and sodium, where conductance measurements can identify and characterize stacking faults.

From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark

Authors: Shuquan Ye, Ben Fei, Hongbin Xu, Jiaying Lin, Wanli Ouyang

Published: February 20, 2026

The Solid Electrolyte Interphase (SEI) is critical to the performance of lithium-ion batteries, yet its analysis via Operando Infrared (IR) spectroscopy remains experimentally complex and expensive, which limits its accessibility for standard research facilities. To overcome this bottleneck, we formulate a novel task, Operando IR Prediction, which aims to forecast the time-resolved evolution of spectral ``fingerprints'' from a single static spectrum. To facilitate this, we introduce OpIRSpec-7K, the first large-scale operando dataset comprising 7,118 high-quality samples across 10 distinct battery systems, alongside OpIRBench, a comprehensive evaluation benchmark with carefully designed protocols. Addressing the limitations of standard spectrum, video, and sequence models in capturing voltage-driven chemical dynamics and complex composition, we propose Aligned Bi-stream Chemical Constraint (ABCC), an end-to-end physics-aware framework. It reformulates MeanFlow and introduces a novel Chemical Flow to explicitly model reaction trajectories, employs a two-stream disentanglement mechanism for solvent-SEI separation, and enforces physics and spectrum constraints such as mass conservation and peak shifts. ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines. ABCC even generalizes to unseen systems and enables interpretable downstream recovery of SEI formation pathways, supporting AI-driven electrochemical discovery.

PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes

Authors: Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang, Pao-Hsiung Chiu, My Ha Dao, Nagarajan Raghavan, Chinchun Ooi

Published: February 20, 2026

Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending operational lifespan. Here, we introduce PINEAPPLE (Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter inference in Lithium-ion battery Electrodes), a novel framework that integrates physics-informed neural networks (PINNs) with an evolutionary search algorithm to enable rapid, scalable, and interpretable parameter inference with potential for application to next-generation batteries. The meta-learned PINN utilizes fundamental physics principles to achieve accurate zero-shot prediction of electrode behavior with test errors below 0.1$\%$ while maintaining an order-of-magnitude speed-up over conventional solvers. PINEAPPLE demonstrates robust parameter inference solely from voltage-time discharge curves across multiple batteries from the open-source CALCE repository, recovering the evolution of key internal state parameters such as Li-ion diffusion coefficients across usage cycles. Notably, the inferred cycle-dependent evolution of these parameters exhibit consistent trends across different batteries without any customized degradation physics-embedded heuristic, highlighting the effective regularizing effect and robustness that can be conferred through incorporation of fundamental physics in PINEAPPLE. By enabling computationally efficient, real-time parameter estimation, PINEAPPLE offers a promising route towards the non-destructive, physics-based characterization of inter-cell and intra-cell variability of battery modules and battery packs, thereby unlocking new opportunities for downstream on-the-fly needs in next-generation battery management systems such as individual cell-scale state-of-health diagnostics.