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Chen, Mengyuan

Publications and source records attributed to Chen, Mengyuan.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Composite interlayer for lithium metal based solid state batteries and the method of making the same

A solid-state electrochemical cell that cycles lithium ions includes a solid-state electrolyte that defines a first major surface and an electrode that defines a second major surface. The solid-state electrochemical cell also includes an interfacial layer disposed between the first major surface of the solid-state electrolyte and the second major surface of the electrode. The interfacial layer may include an ion-conductor disposed in an organic matrix.

Chen, Mengyuan↗
DOE OSTI2023

Lithium metal negative electrode and method of manufacturing the same

A negative electrode for an electrochemical cell of a secondary lithium metal battery is manufactured by a method in which a precursor solution is applied to a major surface of a lithium metal substrate to form a precursor coating thereon. The precursor solution includes an organophosphate, a nonpolar organic solvent, and a lithium-containing inorganic ionic compound dissolved therein. At least a portion of the nonpolar organic solvent is removed from the precursor coating to form a protective interfacial layer on the major surface of the lithium metal substrate. The protective interfacial layer exhibits a composite structure including a carbon-based matrix component and a lithium-containing dispersed component. The lithium-containing dispersed component is embedded in the carbon-based matrix component and includes a plurality of lithium-containing inorganic ionic compounds, e.g., lithium phosphate (Li3PO4) and lithium nitrate (LiNO3).

Xiao, Xingcheng↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2021

Valence Effects of Fe Impurity for Recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 Cathode Materials

Iron impurities are generally included in the obtained leaching liquor solution during the hydrometallurgical recycling method of spent lithium-ion batteries (LIBs) due to the usage of iron in battery casings and machinery parts of recycling equipment, which would definitely affect the physical and electrochemical features of the recovered active materials. In this paper, the effects of iron impurity with different valence states (Fe 2+ and Fe 3+ ) and gradient concentrations (0.2, 1.0, and 5.0 at. %) for the obtained LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) cathodes are fully studied. It is found that Fe3+ impurity could easily lower the tap density and average size of NCM622 particles and even introduce some impurity phases in the NCM622 structure at high concentration (5.0 at. %), leading to much lower specific capacity, worse rate capability, and cycling performance of the Fe 3+ -based NCM622 cathode. On contrast, with certain concentrations of Fe 2+ impurity (0.2 and 1.0 at. %), the NCM622 cathode material exhibits comparable and much better electrochemical properties compared with the virgin NCM622 materials. Based on these results, the valence of Fe impurity should be considered and controlled as well as its concentration during the recycling process design for spent LIBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Understanding fundamental effects of Cu impurity in different forms for recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 cathode materials

When identifying and developing lithium ion battery recycling technologies, understanding the role of impurities is always a concern as they may have an impact on electrochemical performance, material stability, or lifetime for recovered active materials. In this work we show that Cu impurity in different forms could play very different roles on the recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622). Cu metal impurity could easily short the cell, while Cu ion impurity could play a positive role due to partially substitution of the Ni 2+ sites to lower the cation mixing degree. In specific, NCM622 material with certain amount (0.34 at%) of Cu ion impurity delivers high initial specific charge/discharge capacity of 211.8 and 186.0 mAh g -1 at 0.05 C, which are 12.2 and 14.1 mAh g -1 higher than those of virgin NCM622 cathode. Moreover, a superior retention capacity of 171.3 mAh g -1 after 90 cycles at 0.33 C, and striking rate capability are achieved in this NCM622 cathode sample. These results clearly indicate that Cu metal impurity should be avoided and the ionic Cu impurity should be controlled with optimal concentration during recycling process for spent LIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Systematic Study of Al Impurity for NCM622 Cathode Materials

Many recycling processes have been developed for spent Li-ion batteries (LIBs), such as pyrometallurgy, hydrometallurgy, and direct recycling. For all the recycling methods, however, impurities are always introduced from the current collectors or casing materials, especially aluminum (Al), which might lead to negative effects on recovered electrode materials. Therefore, it is significant to determine the impacts of Al impurity on recovered materials. Here, the influence of the Al impurity for the synthesized LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) precursor and cathode is systematically studied. The cell with 0.2 at % Al impurity displays the highest reversible capacities (145.2, 130.5, and 100.3 mAh g -1 from 2, 3, and 5 C, respectively) and striking cycling capability at 2 C after 100 cycles with the highest retention capacity of 138.5 mAh g -1 . Meanwhile, the excess Al ions (5 at %) lead to the Li/Mn superlattice structure and deteriorate electrochemical performance of the synthesized NCM622 cathode.

25 ENERGY STORAGE↗