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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting Failure Using Deep Learning SAND Report

Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.

Modelling↗
DOE OSTI2022

Excellent antioxidizing, thermally insulating and flame resistance silica‐polybenzoxazine aerogels for aircraft ablative materials

Abstract High‐performance thermal protective composites with lightweight, micro‐ ablation and high‐efficient thermal insulation are urgently required for thermal protection systems in advanced hypersonic speed vehicles. However, the practical applications of thermal protective composites have long been hampered by the main issues such as low mass residual rate and poor long‐term antioxidation of the matrix in high‐temperature aerobic environments. Here, we report a novel silica‐polybenz oxazine (SiO 2 ‐PBO) aerogels with interpenetrated networks, possessing the ability to antioxidation, thermal insulation, and flame‐retardant properties. The resulting SiO 2 ‐PBO aerogels exhibit low density (0.25 g/cm 3 ), low thermal conductivity (0.035 W/(m·K)), and superior peak heat release rate value (15.3 W/g). Moreover, the mass residual rate is up to 70.46 wt% in the N 2 atmosphere and remains 57.83 wt% despite existing in the air atmosphere and experiencing the highest temperature of 800°C. Briefly, SiO 2 ‐PBO aerogels as‐prepared could be a potential matrix for a new gene ration of high‐performance thermal protective composites in the future.

Xiao, Yunyun↗
DOE OSTI2026

Towards Generalizable and Efficient Circuit Topology Design: A Graph-Transformer-based Surrogate Model with Curriculum Learning

Unlike circuit parameter and sizing optimizations, the automated design of analog circuit topologies poses significant challenges for learning-based approaches. One challenge arises from the combinatorial growth of the topology space with circuit size, which limits the topology optimization efficiency. Moreover, traditional circuit evaluation methods are time-consuming, while the presence of data discontinuity in the topology space makes the accurate prediction of circuit performance exceptionally difficult for unseen topologies. To tackle these challenges, we design a novel Graph-Transformer-based Network (GTN) as the surrogate model for circuit evaluation, offering a substantial acceleration in the speed of circuit topology optimization without sacrificing performance. Our GTN model architecture is designed to embed voltage changes in circuit loops and current flows in connected devices, enabling accurate performance predictions for circuits with unseen topologies. To address the cold start problem when scaling GTN to large-scale circuits, we further introduce a curriculum learning strategy that progressively trains GTN from small-scale to large-scale circuits. This approach enables the model to first learn fundamental physical principles from simpler topologies and gradually adapt to complex configurations, effectively bridging the circuit complexity gap and improving prediction accuracy. Taking the power converter circuit design as an experimental task, our GTN model significantly outperforms an analytical approach and baseline methods directly utilizing graph neural networks. Furthermore, GTN achieves less than 5% relative error and 196× speed-up compared with high-fidelity simulation. Notably, our GTN surrogate model empowers an automatic circuit design framework to discover circuits of comparable quality to those identified through high-fidelity simulation while reducing the time required by up to 98.2%. With curriculum learning, the enhanced GTN achieves a 51% improvement for performance prediction of large-scale circuits compared to the GTN model without this strategy. These advancements establish GTN as a scalable framework for automated analog circuit design across varying circuit complexity levels.

Lu, Haoshu [New Jersey Institute of Technology (NJ↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗
DOE OSTI2017

Biomass-derived carbon/silicon three-dimensional hierarchical nanostructure as anode material for lithium ion batteries

Highlights: • The 3D conductive framework is fabricated from biomass-derived carbon. • The enhanced performance is attributed to in-suit coating and 3D skeleton of carbon. • The synthetic strategy is low-cost and environment friendly. - Abstract: Three-dimensional hierarchical silicon/carbon nanostructure (3D AC-Si) is applied as the anode material for lithium-ion batteries (LIBs), where encapsulated Si NPs are uniformly bonded over interconnected carbon framework. The unique structure benefits from the binary functions of the naturally conductive network of biomass carbon and the in-suit carbon coating of dopamine. This 3D AC-Si composite behaves high specific capacity and good cycling stability (3353 mAh g{sup −1} at a current density of 0.2 A g{sup −1}), as well as enhanced rate capability. Our findings exhibit a new strategy for preparing low-cost and environment friendly silicon/carbon composite and has potential to be extended to other anode materials for high-performance LIBs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electronic Bottleneck Suppression in Next‐Generation Networks with Integrated Photonic Digital‐to‐Analog Converters

Digital‐to‐analog converters (DAC) are indispensable functional units in signal processing instrumentation and wide‐band telecommunication links for both civil and military applications. As photonic systems are capable of high data throughput and low latency, an increasingly found system limitation stems from the required domain crossing such as digital to analog and electronic to optical. A photonic DAC implementation, in contrast, enables a seamless signal conversion with respect to both energy efficiency and short signal delay, often requiring bulky discrete optical components and electric–optic transformation, hence introducing inefficiencies. Herein, a novel coherent parallel photonic DAC concept along with a 4‐bit experimental prototype capable of performing this DAC without optic–electric–optic domain crossing is introduced. This new paradigm guarantees a linear intensity weighting among bits when operating at high sampling rates (50 GHz), featuring an exceptional sampling efficiency (> 100 GS ) and small footprint (≈1 mm 2 ) in an 8‐bit implementation. Importantly, this photonic DAC enables seamless interfaces of next‐generation data processing hardware with high relevance in data centers, task‐specific compute accelerators such as neuromorphic engines, and network edge processing applications.

Meng, Jiawei↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗
NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
DOE OSTI2020

Stabilized Electrode/Electrolyte Interphase by a Saturated Ionic Liquid Electrolyte for High-Voltage NMC532/Si-Graphite Cells

Nonaqueous electrolyte has become one of the technical barriers in enabling Li-ion battery comprising of a high voltage cathode and high capacity anode. Here, we demonstrate a saturated piperidinum bis(fluorosulfonyl)imide ionic liquid (IL) with a LiFSI salt not only supports the redox reaction on the cathode at high voltages, but also shows exceptional kinetic stability on the lithiated anode as evidenced by its improved cycling performance in a NMC532/Si-graphite full cells cycled between 4.6 and 3.0 V. On the basis of the spectroscopic/microscopic analysis and molecular dynamics (MD) simulations, the superior performance of the cells is attributed to the formation of solid-electrolyte-interphase on both electrode as well as unique solvation structure where a deadlocked coordination network is established at the saturated state, which prevents transition metal dissolution into the electrolyte via a solvation process.

25 ENERGY STORAGE↗
DOE OSTI2023

Fabrication-conscious neural network based inverse design of single-material variable-index multilayer films

Multilayer films with continuously varying indices for each layer have attracted great deal of attention due to their superior optical, mechanical, and thermal properties. However, difficulties in fabrication have limited their application and study in scientific literature compared to multilayer films with fixed index layers. In this work we propose a neural network based inverse design technique enabled by a differentiable analytical solver for realistic design and fabrication of single material variable-index multilayer films. This approach generates multilayer films with excellent performance under ideal conditions. We furthermore address the issue of how to translate these ideal designs into practical useful devices which will naturally suffer from growth imperfections. By integrating simulated systematic and random errors just as a deposition tool would into the optimization process, we demonstrated that the same neural network that produced the ideal device can be retrained to produce designs compensating for systematic deposition errors. Furthermore, the proposed approach corrects for systematic errors even in the presence of random fabrication imperfections. The results outlined in this paper provide a practical and experimentally viable approach for the design of single material multilayer film stacks for an extremely wide variety of practical applications with high performance.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Solvent-Free Preparation of High Energy, Binder-Free Electrodes Enabled by Dry Compressible Holey Graphene

Graphene is an atomically thick sheet consisting of a graphitic carbon network with excellent mechanical strength, electrical and thermal conductivity, and chemical stability. Holey graphene, a structural derivative of graphene, has an array of through-the-thickness holes across the lateral surface of the nanosheet. The presence of these holes has minimal detrimental effect on the graphene properties and leads to enhanced performance in applications such as electronics, sensors, and energy storage. For example, these holes allow more facile cross-plane ion and gas transport than intact graphene, making holey graphene an ideal electrode material for electrochemical energy storage. This presentation will focus on the ability of holey graphene to be compression molded into robust articles or architectures under solvent-free conditions without the need for potentially parasitic binders. The unique dry compressibility of holey graphene has enabled facile fabrication of high mass loading electrodes with both high density and high porosity, which have found use in supercapacitors and various high-energy battery systems such as lithium-oxygen, lithium-sulfur, and lithium-selenium batteries.

Yi Lin↗
DOE OSTI2014

Contributing to Net Zero Building: High Energy Efficient EIFS Wall Systems

The team led by Dow Corning collaborated to increase the thermal performance of exterior insulation and finishing systems (EIFS) to reach R-40 performance meeting the needs for high efficiency insulated walls. Additionally, the project helped remove barriers to using EIFS on retrofit commercial buildings desiring high insulated walls. The three wall systems developed within the scope of this project provide the thermal performance of R-24 to R-40 by incorporating vacuum insulation panels (VIPs) into an expanded polystyrene (EPS) encapsulated vacuum insulated sandwich element (VISE). The VISE was incorporated into an EIFS as pre-engineered insulation boards. The VISE is installed using typical EIFS details and network of trained installers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Machine learning framework for assessment of microbial factory performance

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

PSB27: A thylakoid protein enabling Arabidopsis to adapt to changing light intensity

Project Title: Immunophilins in the assembly and maintenance of photosynthetic electron transport chain in Arabidopsis Applicant: The Regents of the University of California PI: Sheng Luan, University of California at Berkeley Photosynthetic light energy conversion entails coordinated function of complex molecular machines that capture and convert light energy into chemical forms through photosynthetic electron transport chain. Each molecular machine, such as photosystem II (PSII), may consist of dozens of protein subunits and small molecule cofactors. Despite advanced understanding of the structure and function of these complexes, little is known about “How individual proteins and cofactors assemble into a functional machine and how do these molecular machines maintain their structure and function under a highly hazardous lumenal environment.” Our studies on immunophilins have unexpectedly contributed to the understanding of this question. Originally defined as cellular receptors for immunosuppressants, immunophilins have been discovered in a wide range of organisms from bacteria, fungi, plants, to animals. Immunophilins function in protein folding processes as chaperones and foldases. Arabidopsis genome encodes ca. 50 immunophilins. The most striking finding is that 16 immunophilin members are targeted to chloroplast thylakoid lumen, by far the largest group in the lumenal proteome. What is the function of immunophilins in the thylakoid lumen? Our studies have demonstrated critical roles for several immunophilins in the biogenesis and maintenance of photosynthetic complexes such as PSII. These studies have made a critical link between immunophilins and the assembly of photosynthetic machines and thus opened up a new area of research in photosynthesis. Our goal is to dissect the roles of immunophilins and their partners in the assembly and maintenance of the photosynthetic electron transport chain. The specific objectives for this funding period will be: 1. To dissect the mechanism of action for CYP38. Our studies suggest an “autoinhibitory” model for CYP38 action. We plan to test this model and determine the mechanistic details for CYP38 function by biochemical and genetic procedures. 2. To determine the mechanism of action of FKBP20-2/FKBP16-3. Studies on FBKP16-3 and FKBP20-2 suggest that they may work together as a redox-dependent duo in PSII assembly. We will test this hypothesis using biochemical and genetic tools. 3. To determine the function of lumenal FKBPs by multi-gene mutagenesis approach. Using new genetic knockout (KO) procedures especially CRISPR/CAS9 system, we plan to generate multi-gene KO models that will likely provide vital information on those FKBPs with functional redundancy. 4. Functional analysis of thylakoid lumen network. We will focus our effort on objectives 1-3. Objective 4 represents a long-term goal to establish a more comprehensive network of proteins in the thylakoid lumen and their function in the assembly and maintenance of photosynthetic complexes. I hypothesize that the 16 immunophilin-type chaperones and foldases in the thylakoid lumen constitute an “assembly line” for the various components in the photosynthetic electron transport chain. Understanding this assembly line will enable further engineering of more efficient photosynthetic machines to capture more light energy and enhance plant productivity. Furthermore, it is envisioned that such molecular “assembly line” may be rebuilt to produce artificial photosynthesis in vitro or in non-photosynthetic organisms. These are highly relevant to the mission of “Photosynthetic Systems” and “Physical Biosciences” programs to “enhance our understanding of energy capture, conversion, and/or storage.” During the funding period, we have performed a number of experiments under the proposed objectives and obtained several new results. We have found in Objective 1 that CYP38 is associated to the thylokoid membrane with a larger complex that might be PSII supercomplex. Under objective 2, we have found that FKBP16-2 interacted with PSB27 that was further pursuited and published a research article in PNAS (attached). Under Objective 3, we have identified several mutants of other FKBPs in the thyalkoid lumen that should be further studied if future funding is available. Under Objective 4, we have started to build a network of lumenal proteins that play a number of roles in photosynthesis. For example, the CYP37 and CYP28 are linked to chloroplast signaling to nucleus, critical for controlling plant response to high light and adaptation to climate change. Unfortunately these studies have been terminated due to funding shortage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Coal-derived conductive pavement for winter de-icing: prototype, modeling, and simulation

Existing pavement de-icing methods result in high installation and maintenance costs, traffic delays, excessive weariness and corrosion, and negative environmental and safety impacts. To overcome these challenges, this paper presents an innovative pathway to designing and constructing smart self-heating pavements using a low-cost coal-char bearing asphalt material. The conductive asphalt incorporates coal char, i.e., a key byproduct of the coal pyrolysis process, into the Stone Mastic Asphalt (SMA) mixture. This asphalt containing coal-derived solid carbon exhibits highly tailorable electrical conductivity, satisfactory mechanical and thermophysical properties, and superior cost-efficiency as compared to other conductive pavement materials. The de-icing performance was also demonstrated by laboratory experiments on a bench-scale prototype. Furthermore, an efficient thermal network model was developed and validated by experiments to investigate the transient thermal behavior and energy performance of the Ohmic heating pavement system. Furthermore, the whole-year energy simulation case studies were conducted on a bridge pavement with an annual energy use of 24.6–1444.8 kWh/m 2 , showcasing its potential in field applications across cool humid, cold humid, and subarctic/arctic climate zones.

42 ENGINEERING↗
DOE OSTI2017

Overall Water Splitting with Room-Temperature Synthesized NiFe Oxyfluoride Nanoporous Films

Freestanding and lightweight thin-films were rationally designed to serve as robust electrodes for renewable energy applications. A facile and scalable nanomanufacturing process was developed to fabricate these transformative thin-film electrodes (iron group mixed oxides) that exhibit a nanoporous structure and controllable composition. More specifically, electrodeposition and anodic treatments were employed to produce freestanding and lightweight metal oxides nanoporous layers (NPL). These NPL can be directly used as flexible and additive-free electrodes for renewable energy generation (water splitting) and storage (supercapacitor) applications without requiring binders and current collector and other additives. Significantly enhanced electrochemical performance was achieved due to the unique merits of the NPL: i) highly porous structure considerably increases the electrode/electrolyte interface, which facilitate electrochemical reactions; ii) NPL substantially increase the number of active sites that are favorable in electrochemical reactions; iii) residual metal network within the NPL forms a conductive framework, drastically improving electrode strength, flexibility and conductivity.

36 MATERIALS SCIENCE↗