Haiyan Lu

The distribution of relaxation times (DRT) resolves electrochemical impedance spectroscopy (EIS) into mechanistically interpretable battery health features, but the deconvolution is severely ill-posed: at the sparse frequencies of rapid or onboard diagnostics, single-spectrum solutions—and the health descriptors built on them—become unstable. Here we present P³-DRT, which constrains this inverse problem with the battery population itself: a score-based prior, learned from 215,250 quality-screened DRT candidates spanning six public datasets, encodes the DRT morphologies real batteries actually exhibit, while the target spectrum's impedance likelihood anchors every posterior solution to the measurement. With only 12 and 8 frequency points, P³-DRT cuts full-spectrum reconstruction RMSE from 1.92% to 0.64% and from 3.37% to 0.70%, and band-integral descriptor errors by up to 86.6%. Posterior descriptors achieve a leave-one-battery-out state-of-health (SOH) RMSE of 4.43%—one third below single-spectrum Bayesian DRT—and transfer to unseen NCA cells without target-domain adaptation, reducing SOH RMSE by 21.5% and 20.6% at 0% and 100% SOC while delineating an SOC-dependent transfer boundary. Population-level empirical priors thus turn sparse impedance measurements into stable, mechanistically interpretable battery diagnostics.

High areal capacity and stable low-temperature operation are critical for the practical implementation of sodium-ion batteries, yet their development is hindered by sluggish charge/mass transfer kinetics at low temperatures and interfacial instability during cycling. Herein, we incorporate the weakly coordinating solvent ethoxy(pentafluoro)cyclotriphosphazene (PFPN) into the conventional NaPF₆-PC/DEC electrolyte. PFPN competitively binds to Na⁺ with PC/DEC through its steric hindrance effect, weakening solvent-ion interactions to enhance Na⁺ migration kinetics at low temperatures. Concurrently, it coordinates with highly electronegative PF₆^- anions, facilitating their redistribution and the formation of a stable anion-derived electrode-electrolyte interphase. At a high areal capacity of 2.0 mAh cm^-2 (N/P = 1.29) and -40 °C, the NaNi_1/3Fe_1/3Mn_1/3O₂∥hard carbon batteries utilizing the PFPN-modified electrolyte exhibit stable cycling with 89.9% capacity retention over 200 cycles, while the pouch cell delivers a high energy density of 112.5 Wh kg^-1. This work offers valuable insights for the design of high-areal-capacity sodium batteries tailored for cold regions.

While bulk indium arsenide (InAs) exhibits excellent electronic properties, its application is hindered by lattice mismatch with substrate and limited surface tunability. These challenges motivate the exploration of few-layer InAs, which offers distinct advantages in flexibility and surface reactivity. In this study, we perform a comprehensive first-principles investigation of how various substitutional dopants and vacancies affect the structural, opto-electronic, mechanical, and chemical properties of monolayer InAs. Transition metal dopants such as Co and Fe induce moderate bandgap increment and reduced in-plane stiffness, while Se and Pd enhance out-of-plane rigidity. These modifications influence electron localization and band alignment. Optical absorption is broadened in the infrared region due to sub-bandgap transitions. Gas adsorption analyses reveal that dopants such as Co, Fe, and P significantly strengthen binding and charge transfer with gas molecules like NO₂ and NH₃. This work highlights dopant-modulated structure-property coupling in 2D InAs and provides design insights for tunable optoelectronic and sensing applications.

The complex degradation kinetics and inconsistency of decay paths in in-service lithium-ion batteries (LIBs) significantly affect the health management capabilities of battery management systems (BMS). To enhance the performance of state-of-health (SOH) estimation, we propose a Physics-Informed Neural Network (PINN) learning framework. A partial differential equation (PDE) is first formulated based on prior knowledge to model battery cell degradation kinetics. When prior knowledge is insufficient, characteristic variables of degradation are extracted from monitoring data to expand the kinetic model into multiple dimensions. To avoid the direct computation of PDEs for multidimensional models, the Deep Hidden Physical Modeling (DeepHPM) technique is used to construct a function approximator within the PINN framework. Furthermore, a loss function weight-balancing strategy is introduced during model training. By minimizing the negative log-likelihood estimation, the weights are adaptively updated to ensure optimal task balance. The proposed framework is validated on a public dataset and compared against typical machine learning methods through comprehensive analysis. Experimental results demonstrate that the proposed method achieves higher accuracy and reliability in SOH estimation for practical applications, outperforming existing approaches.

The use of NbOX2 oxyhalide (X = Cl, Br, I) nanoribbons and nanosheets in next-generation nanoelectronic devices remains unfulfilled because the impact of the fundamental electronic properties of these materials on their practical device applications remains poorly understood. The present work applies first-principles density functional theory calculations to investigate the anisotropic electronic properties and quantum confinement effects of NbOX2 nanoribbons and nanosheets and their performance in field effect transistors. Our results reveal direction-dependent electron transport behaviors, with the most efficient transport occurring along the Nb-X axis. Quantum confinement in nanoribbons leads to bandgap widening, with Nb–O-oriented nanoribbons exhibiting more stable electronic properties. In addition, charge delocalization is confirmed along the Nb-X axis, and it strengthens with increasing halogen constituent mass.