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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Sequentially coupled flow and geomechanical simulation with a discrete fracture model for analyzing fracturing fluid recovery and distribution in fractured ultra-low permeability gas reservoirs

More accurate characterization and prediction of the in-situ distribution of fracturing fluid in fractured reservoirs are needed for enhancing well productivity. In this study, an implicit-sequentially coupled flow/geomechanics simulator incorporating an efficient discrete fracture model is developed to model fluid distribution and recovery performance of ultra-low permeability gas reservoirs. The finite-volume and finite-element methods are used for space discretization of the flow and geomechanics equations, respectively, while the backward Euler method is employed for time discretization. The flow and geomechanics equations are solved sequentially based on fixed-stress splitting. An efficient discrete-fracture model is used to explicitly model the fractured system. Flexible unstructured gridding is employed to model arbitrarily-oriented fractures. The interrelations among pore volume, permeability and geomechanical conditions are considered dynamically using two-way coupled flow and geomechanics computations. The geometry of fracture (networks) due to hydraulic fracturing has significant impacts on the fracturing fluid recovery efficiency and ensuing fluid distribution. Under the same injection volume, the fracturing fluid recovery is higher when the fracture geometry is planar. Fluid recovery is relatively lower whenever natural fractures are activated during fracturing treatments; flowback time is also shortened when complex fracture network with enlarged fracture interface is present. Fracturing fluid in hydraulic fractures may leak off into the natural fractures and subsequently imbibes into the surrounding matrix due to capillarity effects. The fracturing fluid recovery and in-situ fluid distribution are sensitive to the shut-in duration and fracture closure behavior. This study analyzes the coupled flow-geomechanical responses of fractured gas reservoirs during the post-fracturing periods. Understanding the fate of the fracturing fluid can provide insights on, to some extent, the stimulated fracture volume, size of the water invasion zone, and efficiency of the fracturing design. The simulation predictions can also provide more accurate initial reservoir conditions (e.g. distributions of different phases and pressure) for long-term well performance estimation.

42 ENGINEERING↗
DOE OSTI2026

Reduction of baseplate distortion during directed energy deposition using compliant features

Distortion in additive manufacturing (AM) remains a barrier to its adoption in precision industries. Baseplate warpage is one such issue which compromises the feasibility of post-process precision machining. The restriction to thermal contraction of the deposited part, imposed by the baseplate, generates bending moments, that in turn causes warpage. A novel distortion mitigation strategy using baseplates with integrated compliant features, which enables thermal contraction of the build is presented. Six unique design concepts are evaluated, including a solid reference, using two deposition geometries. A laser, hot-wire, directed energy deposition (DED) process is used to deposit a symmetric cylindrical part (C-part) and a T-shaped asymmetric part (T-part). Flatness deviation of baseplates is measured using structured light 3D scanning. Measurements reveal a reduction in net flatness deviation of 58.8% for the C-part and 40.9% for the T-part, compared to the solid reference. While most designs yielded reductions exceeding 30% and 20% for the C- and T-parts, respectively, one configuration resulted in increased deviation. Finite element (FE) simulations are performed to elucidate the underlying mechanisms affecting distortion of compliant baseplates during DED. Despite variations between predictions and measurements, agreement in the general trend is observed. It also revealed that initial flatness errors in baseplates significantly affect its deviation during deposition. Predictions indicate that compliant features significantly affect the thermal distribution as well as the evolution of flatness deviation in the baseplate during deposition. Notably, one design exhibited a reduction in distortion during cooling, following its initial increase during deposition. FE predictions show a maximum reduction of 60.9% and 38.8% in net flatness deviation for the C- and T-parts, respectively. The performance of compliant baseplates is found to be governed by both its the thermal and mechanical characteristics, which are crucial factors to be considered during design.

Mathews, Ritin [ORNL] (ORCID:0000000301440828)↗
DOE OSTI2023

Integrated Multiscale Model for Design of Robust 3D Solid-state Lithium Batteries

In FY23, we successfully established the multiscale modeling framework for probing the effects of materials microstructure on cell performance of 3D solid-state batteries. The framework covers physicochemical processes co-evolving at the atomistic and microstructure scales. Our simulations revealed the mechanism of initial interfacial degradation, formation of secondary phases, and the structure-property relationship for ion transport and mechanical stability at the interface. In addition, we also established the microstructure-performance relationships by performing sensitivity tests of various microstructure features and extracting their impact on cell performance during charge-discharge cycles. We have successfully applied our multiscale, multiphysics modeling capability to common electrode and electrolyte materials that are of interests to VTO and the experimental teams within the US-Germany collaboration. The insights we obtained from these simulations provide valuable design principles to optimize materials properties for advanced 3D solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Polycarbonate‐Based Solid‐Polymer Electrolytes for Solid‐State Sodium Batteries

Solid-polymer electrolytes comprised of polypropylene carbonate (PPC) and varied sodium bis(fluorosulfonyl)imide (NaFSI) salt concentrations are investigated for implementation as a conductive solid polymer electrolyte into solid-state cathode composites utilizing a sodium-layered oxide active material. The ionic conductivity generally increases with NaFSI salt content, reaching ≈1 mS cm −1 at 80 °C at the highest salt concentration (PPC:NaFSI = 0.5:1). Through an all-in-one slurry casting method, Na 2/3 Ni 1/3 Mn 2/3 O 2 cathode composites are fabricated in which the dispersed PPC electrolyte acts as the primary binder. Enabled by a bilayer polymer electrolyte system, cycling performance with the PPC cathode electrolyte is optimized with respect to salt concentration and anode material. The best cyclability is achieved with a moderate salt concentration electrolyte (PPC:NaFSI = 5:1), showcasing an initial capacity of 83 mA h g −1 with a remarkable 80% capacity retention after 150 cycles at C/5 rate and 60 °C. The superior performance of the lower salt concentration electrolyte is attributed to better electrochemical stability, as confirmed by linear sweep voltammetry and online electrochemical mass spectrometry measurements. In conclusion, these results underscore the potential of carbonate-based polymer electrolytes and the importance of balancing electrolyte conductivity and stability in cell design.

25 ENERGY STORAGE↗
DOE OSTI2025

Impact of Different Thermal Gradients on the Dynamics of Cylindrical Lithium-ion Cells Subject to Accelerated Aging and on Module Performance

This study investigates the impacts of applying different thermal gradient patterns to cylindrical lithium-ion cells in a module on cell dynamics (temperatures, current flows, state of charge), module performance (evolution of resistance, capacity, and energy versus cycle number), and module lifetime. The thermal gradients were generated using cooling plates (CPs) with three different flow-field designs, namely, straight, perpendicular, and U-turn. The study uses computational fluid dynamics (CFD), the pseudo-two-dimensional (P2D) battery model, capacity loss and increased impedance due to the growth of a solid-electrolyte-interphase, and the electric current distribution from module terminals to cells that depends on the series-parallel electrical connections among the cells. The impact of the thermal gradient (resulting from the CP designs) on the variability in resistance, current, state of charge, and voltage among the cells was analyzed and linked to differences in the module's performance. Applying a thermal gradient to parallel-connected strings of series-connected cells led to variation in the current through each parallel string and an imbalance in the voltage of series-connected cells. Module performance is poorer when the thermal gradient causes a voltage imbalance than when it causes a current imbalance. Module performance becomes the worst when both current variation and voltage imbalance happen together. For instance, the module's lifetime (estimated as reaching 80% of its initial capacity) varied by 5% to 17.5%, depending on the magnitude and pattern of the imposed thermal gradient. As the relative orientation between thermal gradients and cells' electrical connectivity influences the module's performance, appropriate consideration should be given to the choice of the CP, especially if large thermal gradients are allowed.

Battery thermal management↗
DOE OSTI2024

Effect of crystallite size on lithium storage performance of high entropy oxide (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 nanoparticles

High-entropy oxides (HEOs), known for their high theoretical capacity and structural stability, are considered promising anode materials for next-generation lithium-ion batteries (LIBs). In this research, we synthesized a novel spinel-type HEO, (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 , using a solution combustion method. By adjusting the quantity of the combustion agent, we produced samples with varying crystallite sizes. The crystallite size of the HEOs initially enlarges with an increased combustion agent, then diminishes. The enhancement of crystallite size correlates with improved electrochemical performance for lithium storage. Notably, the (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 nanoparticles, with the largest crystallite size of 36.3 nm, demonstrated a reversible capacity of 343 mA h g -1 after 100 cycles at 100 mA g -1 , a capacity retention to 319 mA h g -1 after 1000 cycles at 1 A g -1 , and a commendable rate capability of 260 mA h g -1 at 2 A g -1 . Furthermore, this study underscores the pivotal role of crystallite size in LIB performance and presents a viable strategy to enhance the lithium storage capabilities of HEOs and other metal oxides.

25 ENERGY STORAGE↗
DOE OSTI2025

Establishing a process-structure-property-performance framework for SLS additive manufacturing through integrated multiscale modeling

This study presents a comprehensive suite of high-fidelity computational models that integrate multiscale and multiphysics simulations to capture the full Selective Laser Sintering (SLS) additive manufacturing process—from initial melting and solidification to mechanical response under external loads. Process simulations are linked with mechanical analysis through Representative Volume Elements (RVEs), establishing a process-structure–property-performance framework. The interaction between laser light and polyamide 12 (PA12) powder is modeled, accounting for laser characteristics and the optical, thermal, and geometrical properties of the powder. The heat source is incorporated into a heat transfer model, coupled with crystallization kinetics and densification models to predict material density and crystallinity. The porosity distribution from the densification model and crystallinity interpolated from experimental data are used to construct the RVEs. A multi-mechanism constitutive model is then calibrated using mechanical tests to predict the stress–strain response. Simulation results show good agreement with experimental data in terms of porosity, crystallinity, and mechanical performance when sufficient laser power (62 W or higher) is used. This research supports the inverse design of 3D-printed structures by introducing a high-fidelity framework that combines multiscale and multiphysics modeling with experimental calibration for predictive and performance-driven additive manufacturing.

SLS↗
DOE OSTI2020

Unveiling the critical role of interfacial ionic conductivity in all-solid-state lithium batteries

Advancement of all-solid-state lithium-ion (Li + ) batteries (ASSLIBs) has been hindered by the large interfacial resistance mainly originating from interfacial reactions between oxide cathodes and solid-state sulfide electrolytes (SEs). To suppress the interfacial reactions, an interfacial coating layer between cathodes and SEs is indispensable. However, the kinetics of interfacial Li + transport across the coating layer has not been well understood yet. Herein, we tune the interfacial ionic conductivity of the coating layer LiNb 0.5 Ta 0.5 O 3 (LNTO) by manipulating post-annealing temperature. It is found that the interfacial ionic conductivity determines interfacial Li + transport kinetics and enhancing the interfacial ionic conductivity can significantly boost the electrochemical performance of SE-based ASSLIBs. A representative cathode LiNi 0.5 Mn 0.3 Co 0.2 O 2 coated by LNTO with the highest interfacial ionic conductivity exhibits a high initial capacity of 152 mAh.g -1 at 0.1 C and 107.5 mAh.g -1 at 1C. This work highlights the importance of increasing interfacial ionic conductivity for high-performance SE-based ASSLIBs.

25 ENERGY STORAGE↗
DOE OSTI2026

Initial Assessment of 3D-Printed Molybdenum Lattices as a Substrate for Liquid Metal Plasma-Facing Components

Many liquid metal plasma-facing component concepts for fusion reactors require a capillary porous material to wick the liquid metal to the surface of the component. In this work, samples of 3D-printed molybdenum lattices designed to have pore sizes ranging from 100 to 1000 µm were fabricated by two different suppliers. The dimensions of the lattice structures were characterized using an optical microscope, revealing that the lattice walls were almost systematically thicker than they were designed to be and in turn the pores are all smaller than they were designed to be. In many cases the pores were completely clogged. Several spectroscopic techniques were used to characterize the chemical composition of the materials, collectively indicating the presence of a thick layer of oxygen-rich material extending more than 1 µm below the surface. Compression testing revealed significant asymmetry in the mechanical behavior depending on which direction the force was applied relative to the print direction. Lastly, lithium wetting tests of several lattice samples were performed, and a sample with ~250-µm pores was found to soak up lithium quickly after initial wetting of the surface occurred. The implications of these results for future liquid metal plasma-facing component design are discussed.

Parsons, Matthew S. [Princeton Plasma Physics Labo↗
DOE OSTI2022

Tank 49H solids disturbance analysis

Tank 49H serves as the feed tank for the Salt Waste Processing Facility (SWPF). Transfers into Tank 49H may disturb any solids that have settled to the bottom of the tank, resulting in feed that may exceed the insoluble solids content limit of 1,200 mg/L of the SWPF Waste Acceptance Criteria (WAC). During a transfer into Tank 49H, material that free falls into Tank 49H through a downcomer could potentially disturb any solids on the bottom of the tank and scour or suspend solids from a settled solids layer or turbid region. A previous analysis and report evaluated the potential to disturb solids when transferring into Tank 49H through the B4 riser and recommended a minimum tank level of 120 inches to prevent disturbing any solids in the bottom of the tank. The scope of this task is to perform additional fluid flow analysis to determine whether accounting for disturbed particle settling and particle mixing and dispersion during transfer could allow the minimum liquid level to be reduced below 120 inches. The analysis utilized models from the technical literature to calculate the size and shape of the “plunging jet” as a function of input parameters such as initial velocity, initial jet diameter, elevation of the initial jet, liquid level in the tank, and solids depth. The analysis relied on the M-Star® simulations performed for the previous analysis to provide bounding estimates of the amount of solid particles disturbed and used the MStar® software to calculate the dispersion and mixing of the disturbed solids with other liquid in the tank as the solids are transported to the transfer pump. The analysis showed that with a solid particle size of 5 micron or less, a liquid level of 120 inches should be maintained to prevent significant disturbance of the solid layer at the bottom of Tank 49H.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Novel solid-infused durable nonwetting surfaces for sustained condensation heat transfer enhancement

Here, in this article, we report novel nonwetting solid-infused surfaces that are easily fabricated using industrially widely adopted methods such as chemical etching or electrodeposition and exhibit sustained dropwise condensation performance durably than superhydrophobic and lubricant-infused surfaces. Solid-infused surfaces demonstrate fourfold larger condensation heat transfer coefficient and 25% better condensation effectiveness when compared to superhydrophobic surface and conventional filmwise condensation. Although the condensation heat transfer coefficient of lubricant-infused surface initially matches that of solid-infused surface, it is shown through durability studies that the wettability characteristic that underpins the performance of lubricant-infused surfaces degrades quickly with time as the lubricant depletes and lubricant-infused surface transitions to superhydrophobic surface. Solid-infused surface, on the other hand, retains its steadfast superior dropwise condensation effectiveness in the long run, overcoming the challenges of droplet pinning, surface inundation, cloaking, and lubricant depletion that eventually deteriorate condensation on conventional nonwetting superhydrophobic and lubricant-infused surfaces.

42 ENGINEERING↗
DOE OSTI2021

Progress Towards Accelerating the Unified Model on Hybrid Multi-Core Systems

The cloud microphysics scheme, CASIM, and the radiation scheme, SOCRATES, are two computationally intensive parts within the Met Office's Unified Model (UM). This study enables CASIM and SOCRATES to use accelerated multi-core systems for optimal computational performance of the UM. Using profiling to guide our efforts, we refactored the code for optimal threading and kernel arrangement and implemented OpenACC directives manually or through the CLAW source-to-source translator. Initial porting results achieved 10.02x and 9.25x speedup in CASIM and SOCRATES respectively on 1 GPU compared with 1 CPU core. A granular performance analysis of the strategy and bottlenecks are discussed. These improvements will enable UM to run on heterogeneous computers and a path forward for further improvements is provided.

Zhang, Wei↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Entangling-gate error from coherently displaced motional modes of trapped ions

Entangling gates in trapped-ion quantum computers are most often applied to stationary ions with initial motional distributions that are thermal and close to the ground state, while those demonstrations that involve transport generally use sympathetic cooling to reinitialize the motional state prior to applying a gate. Future systems with more ions, however, will face greater nonthermal excitation due to increased amounts of ion transport and exacerbated by longer operational times and variations over the trap array. In addition, pregate sympathetic cooling may be limited due to time costs and laser access constraints. In this paper, we analyze the impact of such coherent motional excitation on entangling-gate error by performing simulations of Mølmer-Sørenson (MS) gates on a pair of trapped-ion qubits with both thermal and coherent excitation present in a shared motional mode at the start of the gate. Here, we quantify how a small amount of coherent displacement erodes gate performance in the presence of experimental noise, and we demonstrate that adjusting the relative phase between the initial coherent displacement and the displacement induced by the gate or using Walsh modulation can suppress this error. We then use experimental data from transported ions to analyze the impact of coherent displacement on MS-gate error under realistic conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Volume 6: Experiment Facility Spectrum Tailoring (HFIR Futures – Enhanced Capabilities Series)

In-core irradiation experiment research, such as materials and fuels irradiation research and radioisotope production, is one of the primary missions of Oak Ridge National Laboratory’s (ORNL) versatile High Flux Isotope Reactor (HFIR). In support of the HFIR-Sustaining and Enhancing Neutron Science (SENSe) Initiative, a technically diverse group of ORNL irradiation research subject matter experts formed the Spectrum Tailoring Working Group, with the goal of developing a compendium of experiment facility concepts to enhance irradiation experiment conditions via neutron spectrum tailoring capabilities. The purpose of this report is to document the concepts developed in FY22 and the associated scientific justifications, identify potential facility sponsors, and estimate costs and schedules for each concept. This report documents the efforts performed in FY22, which may continue in FY23 or later pending the direction of the HFIR-SENSe Initiative and the interested sponsors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Quantum AI Based Enhanced Detection of Dementia

Quantum computing has the potential to significantly improve the early detection of Alzheimer's Disease and Related Dementias (ADRD). Quantum-enhanced machine learning can be used to perform an early screening of Alzheimer's disease using brain imaging data based on dataset of MRI scans from both healthy individuals and those diagnosed with Alzheimer's. This study aims to demonstrate the potential of quantum transfer learning to enhance the performance of the classical deep learning model for dementia detection. Using the MRI sagittal images available in the OASIS-2 (64 demented and 72 non-demented subjects between 60 and 96 years), we show how quantum techniques can transform a suboptimal classical model into a more effective solution for dementia detection, highlighting their potential impact on advancing healthcare technology. We begin with a simple classical deep learning model with a significantly smaller number of parameters, which gives suboptimal performance on the problem. Then, we apply different configurations of quantum transfer learning based on the pre-trained weak classifier (Figure 1). We fix the weak classifier's initial convolutional layers at their fixed pre-trained parameters and replace the last set of dense layers with a dressed quantum circuit (DQN), which we train to enhance performance. We performed 4-fold cross-validation for both the classical and the hybrid quantum models and trained them using Pennylane's `default.qubit' simulator and IonQ's Aria-1 simulator (noisy simulation). We showed that with significantly fewer parameters, the quantum transfer learning-based hybrid models showed significant performance enhancement over the base weak classical deep learning model for dementia detection. To classify between a demented and non-demented subject, the accuracy of quantum-based AI methods improved by 6 to 14% compared to classical methods. The sensitivity of the models improved by 4 to 17%. This shows that there are fewer chances of misclassifying demented patients. Figure 2 compares the performance of the hybrid quantum models and their base classical model, and Table 1 summarizes the results. We illustrated that with assistance from quantum machine learning, it is possible to enhance detection for dementia based on brain images. This shows the potential for practical utility of quantum computing in ADRD research.

Bhowmik, Sounak [University of Tennessee, Knoxvill↗
DOE OSTI2021

Lithium cuprate, a multifunctional material for NO selective catalytic reduction by CO with subsequent carbon oxide capture at moderate temperatures

Li 2 CuO 2 was evaluated as a possible catalyst for the NO selective catalytic reduction (NO SCR) by CO. The initial idea was supported by ab initio thermodynamic analysis. This analysis showed that NO SCR was favored by using Li 2 CuO 2 as a catalyst, based on the calculated heat of reaction and Gibbs free energy. The NO SCR reaction was evaluated under dynamic, isothermal and cyclic conditions using different NO : CO ratios (2 : 1, 1 : 1 and 1 : 2). The dynamic and isothermal experiments showed that Li 2 CuO 2 does reduce NO in the presence of CO to reach higher conversions (>90%) between 400 and 450 °C. However, it must be mentioned that the catalytic process was reduced once some of the produced CO 2 was captured on the ceramic. At that point, Li 2 CuO 2 acts as a bifunctional material, catalyzing the NO SCR reaction and capturing some of the produced CO 2 . Moreover, cyclic experiments evidenced high NO reduction. Finally, a different set of experiments showed that this catalytic reduction performed differently if NO and CO were previously fed. Finally, when lithium cuprate is previously carbonated (initial CO feed), the catalytic reaction does not complete. In contrast, when there was a previous nitrate formation (initial NO feed), the reaction reached 100% efficiency.

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

Mechanically robust and superior conductive n-type polymer binders for high-performance micro-silicon anodes in lithium-ion batteries

Compared with nanostructured silicon (Si), the Si microparticle (SiMP) has more commercial prospects due to its low cost. However, SiMPs suffer from unavoidable fracture during electrochemical cycling owing to their significant volume change. Here we develop a series of novel n-type conductive polymer binders (CPBs) for SiMP anodes in lithium-ion batteries owing to their superior properties. Extraordinary electrochemical performance of cells with such unique binders could be achieved, which is because these designed polymers contain electron-withdrawing oxadiazole ring groups and easily ionizable sulfonate polar groups, exhibiting excellent ionic conductivity, outstanding wettability to the electrolyte, and improved electronic conductivity after doping. Moreover, the coexistence of rigid and flexible chains enables them to have exceptional strength and ductility. Besides, a unique electrochromic approach has been utilized to investigate the energy gap of the CPBs in n-doping states. The ionic and electronic conductivities of prepared CPBs in an eigenstate and n-doping state have been systematically studied by the electrochemical method, filling the current research gap in this field. Due to the high conductivity of b-POD, the capacity of SiMPs (4200 mA h g –1 ) can be almost entirely released during the first cycle of discharge, while the SiMP anodes prepared with the PAALi or CMC binder show a much lower initial capacity. Here, the high conductivity of b-POD also endows it with better cycling performance than non-conductive binders especially at high current densities, demonstrating its excellent fast-charging ability. More essentially, this work provides polymer binders without any intricate structural designs to obtain high-performance SiMP batteries, significantly enhancing their practical applications.

25 ENERGY STORAGE↗