Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumptio...
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Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cel...
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Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external *** force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external ***,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium *** results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of ***,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge *** integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate *** study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.
Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that c...
Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that conflict with reality due to the unreliable distribution of facts within their training data, which is particularly critical for applications requiring high credibility and accuracy [3].
The structural transformation from a liquid into a crystalline solid is an important subject in condensed matter physics and materials science. In the present study, first-principles molecular dynamics calculations ar...
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The structural transformation from a liquid into a crystalline solid is an important subject in condensed matter physics and materials science. In the present study, first-principles molecular dynamics calculations are performed to investigate the structure and properties of aluminum during the solidification which is induced by cooling and compression. In the cooling process and compression process, it is found that the icosahedral short-range order is initially enhanced and then begin to decay, the face-centered cubic short-range order eventually becomes dominant before it transforms into a crystalline solid.
Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-...
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The requirement to protect the authenticity and accuracy of images motivates the implementation of water-marking on these images. Any alteration or interference with medical images might lead to erroneous diagnosis or...
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Developing an easy ammonia（NH3） production method to circumvent the demanding conditions of the HaberBosch process is a significant stride towards self-sufficiency in NH3production and environment *** pursuit of thi...
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Developing an easy ammonia（NH3） production method to circumvent the demanding conditions of the HaberBosch process is a significant stride towards self-sufficiency in NH3production and environment *** pursuit of this goal, we carried out a theoretical approach to investigate the electrocatalytic N2reduction reaction（eN2RR） using the magnetic La-doped Ti3C2O2（La-Ti3C2O2） MXene electrocatalyst. The first principle calculations of the DFT, conducted using the Vienna Ab-Initio Storage Package（VASP） were instrumental in assessing the performance of ferromagnetic（FM） and antiferromagnetic（AFM） configurations of *** Ti3C2O2reveals limitations in eN2RR efficiency attributed to its suboptimal surface reactivity, both FM and AFM structures of La-Ti3C2O2exhibit enhanced electronic properties, enabling improved electron transfer features. La-Ti3C2O2demonstrates heightened N2adsorption capabilities and reduced energy barriers for transitional species towards NH3production, presenting superior performance to Ti3C2O2. The density of states（DOS） analysis of La-Ti3C2O2provided outcomes supporting the AFM as the credible magnetic configuration, a statement reinforced by the superior N2conversion performance in the AFM structure compared to FM. During this process of eN2RR, a study focused on the favorable pathway with less energy consumption is directed.
The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Meth...
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Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanob...
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Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation *** models typically fall into two categories:data-driven models and physical mode...
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For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation *** models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing ***-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational *** study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity,electrode capacities,and lithium inventory *** proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public *** results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 *** demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
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