Search

Herein, a series of ZnO-doped lignin-based carbons (LC/ZnO) were successfully prepared from different types of lignin and used for methyl orange (MO) photocatalytic degradation. The apparent morphology, internal structure, and photoelectric properties of prepared LC/ZnO composites and their effects on subsequent MO photocatalytic degradation were investigated by various characterization techniques. The results showed that the LC/ZnO composites that were prepared in this work mainly consisted of highly dispersed ZnO nanoparticles and lignin-based carbon nano-sheets, which were beneficial for subsequent photogenerated electrons and holes formation, dispersion, and migration. The MO could be significantly degraded with various ZnO-doped lignin-based carbons, especially over the LCSL/ZnO, and the maximum degradation rate was 96.9% within 30 min under the simulated 300w sunlight exposure. The experiments of free radical elimination showed that the photocatalytic degradation of MO over LC/ZnO were a result of the co-action of multiple free radicals, and h+ might play the predominant roles in MO degradation. In addition, the pH of the solution had little effect on MO degradation, and the MO could be effectively degraded even in an alkaline solution of pH = 12.0. The cycling experiments showed that the prepared LC/ZnO had a good stability for MO photodegradation, especially for LCSL/ZnO, even after 5 times recycling, and the degradation rate of MO only dropped from 97.0% to 93.0%. The research not only provided a fundamental theory for the efficient photocatalytic degradation of MO by LC/ZnO composites, but also offered a new insight into lignin valorization. [ABSTRACT FROM AUTHOR]

Aerodynamic performance of wind turbine governs the overall energy efficiency, which has been an ever-lasting research focus in the field of wind power technology. Due to the coupling effect among the highly complex environmental and structural uncertainties, the practical aerodynamic performance may not be reliably predicted. To aggravate, this performance declines with time in service. It is of great significance to efficiently and reliably assess the impact of uncertain factors and reduce these influences on wind turbine aerodynamic performance. This paper establishes an uncertainty analysis and robustness optimization model of wind turbine aerodynamic performance considering wind speed and pitch angle error uncertainties. An approach combined the no-instrusive probabilistic collocation method is used, and the blade element momentum theory is applied to quantify influences of variable uncertainties on NREL 5 MW wind turbine aerodynamic performance. The optimization target is to reduce the sensitivity of wind turbine aerodynamic performance to uncertainties, as well as maintain capture power. The results show that the wind turbine aerodynamic and mechanical performance will be greatly affected with uncertain factors. By optimizing and adjusting wind turbine rotor speed and blade pitch angle, the wind turbine rotor power and thrust load variation can be reduced to 9.14% and 9.36%, respectively, which indeed reduces the uncertainty effects. [ABSTRACT FROM AUTHOR]

Eden, Feige, and Feldman explored the concept of the max-min greedy matching problem, which can be conceived as a game played between an algorithm and an adversary. Both parties are presented with a bipartite graph consisting of items and players. Initially, the algorithm selects a priority order for the items, while the adversary, in turn, chooses a priority order for the players based on the algorithm's choice. These priority orders are then utilized in a greedy process to establish a matching between the items and players. During the process, when it is a player's turn, they select the highest priority item from their remaining available neighbors. The objective of the algorithm is to maximize the size of the resulting matching, while the adversary's goal is to minimize its size. Previous studies have demonstrated that the algorithm can employ a polynomial-time strategy to achieve a competitive ratio greater than 1 2. In this research, we present evidence that, from the adversary's standpoint, approximating the adversarial order minimum matching problem with a ratio better than 6 5 is NP-hard, assuming the small set expansion (SSE) hypothesis. Conversely, we introduce a fractional variant of the problem and analyze the interaction between the algorithm and the adversary when either or both parties are allowed to utilize fractional permutations. Notably, we discover that if the algorithm solely employs integral item permutations, then an optimal response from the adversary can also involve integral player permutations. Additionally, we establish that, in the fractional variant, the algorithm can adopt a round-robin strategy to achieve a competitive ratio of at least 1 − 1 / e for input graphs with a sufficiently large granularity parameter m. Furthermore, we demonstrate that the analysis for the round-robin strategy is tight even for regular graphs. [ABSTRACT FROM AUTHOR]

Image-based precision medicine research is able to help doctors make better decisions on treatments. Among all kinds of medical images, a special form is called Whole Slide Image (WSI), which is used for diagnosing patients with cancer, aiming to enable more accurate survival prediction with its high resolution. However, One unique challenge of the WSI-based prediction models is processing the gigabyte-size or even terabyte-size WSIs, which would make most models computationally infeasible. Although existing models mostly use a pre-selected subset of key patches or patch clusters as input, they might discard some important morphology information, making the prediction inferior. Another challenge is improving the prediction models' explainability, which is crucial to help doctors understand the predictions given by the models and make faithful decisions with high confidence. To address the above two challenges, in this work, we propose a novel explainable survival prediction model based on Vision Transformer. Specifically, we adopt dual-channel convolutional layers to utilize the complete WSIs for more accurate predictions. We also introduce the aleatoric uncertainty into our model to understand its limitation and avoid overconfidence in using the prediction results. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features as supporting evidence for predictions. Evaluations of two large cancer datasets show that our proposed model is able to make survival predictions more effectively and has better explainability for cancer diagnosis. • Without patching WSIs, a novel ViT-based model is proposed for survival predictions. • We first introduce aleatoric uncertainty into the survival loss function. • We explain survival prediction using a post-hoc explainable method. • Our method outperforms baselines in accuracy, explainability, and reliability. [ABSTRACT FROM AUTHOR]

In spectral graph theory, the Cheeger inequality gives upper and lower bounds of edge expansion in normal graphs in terms of the second eigenvalue of the graph's Laplacian operator. Recently this inequality has been extended to undirected hypergraphs and directed normal graphs via a non-linear operator associated with a diffusion process in the underlying graph. In this work, we develop a unifying framework for defining a diffusion operator on a directed hypergraph with stationary vertices, which is general enough for the following two applications. 1. Cheeger's inequality for directed hyperedge expansion. 2. Quadratic optimization with stationary vertices in the context of semi-supervised learning. Despite the crucial role of the diffusion process in spectral analysis, previous works have not formally established the existence of the corresponding diffusion processes. In this work, we give a proof framework that can indeed show that such diffusion processes are well-defined. In the first application, we use the spectral properties of the diffusion operator to achieve the Cheeger's inequality for directed hyperedge expansion. In the second application, the diffusion operator can be interpreted as giving a continuous analog to the subgradient method, which moves the feasible solution in discrete steps towards an optimal solution. [ABSTRACT FROM AUTHOR]

We study the max-min fair allocation problem in which a set of m indivisible items are to be distributed among n agents such that the minimum utility among all agents is maximized. In the restricted setting, the utility of each item j on agent i is either 0 or some non-negative weight wj. For this setting, Asadpour et al. (ACM Trans Algorithms 8(3):24, 2012) showed that a certain configuration-LP can be used to estimate the optimal value to within a factor of 4+δ, for any δ>0, which was recently extended by Annamalai et al. (in: Indyk (ed) Proceedings of the twenty-sixth annual ACMSIAM symposium on discrete algorithms, SODA 2015, San Diego, CA, USA, January 4-6, 2015) to give a polynomial-time 13-approximation algorithm for the problem. For hardness results, Bezáková and Dani (SIGecom Exch 5(3):11-18, 2005) showed that it is NP-hard to approximate the problem within any ratio smaller than 2. In this paper we consider the (1,ϵ)-restricted max-min fair allocation problem in which each item j is either heavy (wj=1) or light (wj=ϵ), for some parameter ϵ∈(0,1). We show that the (1,ϵ)-restricted case is also NP-hard to approximate within any ratio smaller than 2. Using the configuration-LP, we are able to estimate the optimal value of the problem to within a factor of 3+δ, for any δ>0. Extending this idea, we also obtain a quasi-polynomial time (3+4ϵ)-approximation algorithm and a polynomial time 9-approximation algorithm. Moreover, we show that as ϵ tends to 0, the approximation ratio of our polynomial-time algorithm approaches 3+22≈5.83. [ABSTRACT FROM AUTHOR]

We introduce the Online Food Delivery Problem (OFDP) to model the delivery problem commonly encountered in online food-ordering-and-delivery platforms. In the OFDP the requests (orders) are submitted online, and the depot (restaurant) needs to decide when to send out a server to serve the submitted requests. In addition, the server has to return to the depot (to pickup foods) before serving new requests. The objective is to minimize maximum flow time, i.e., the maximum time between the submission and completion of a request. This problem can also be viewed as a variant of the Online Dial-a-Ride problem, for which however the max flow time objective is inapproximable in general. We study the OFDP on star graphs, and give both algorithmic and hardness results. We analyze a natural greedy strategy and show that it achieves the optimal competitive ratio 3 among all myopic algorithms, which are algorithms that immediately send out the server whenever there are unserved requests. Then we prove that a far-sighted (i.e., non-myopic) algorithm with proper waiting strategy can achieve 8/3-competitive ratio. On the negative side, we give a simple lower bound example that excludes the possibility of any (2 − ϵ) -competitive algorithms. • This paper introduces the Online Food Delivery Problem encountered in online food-ordering-and-delivery platforms. • We show that a natural greedy strategy achieves the optimal competitive ratio 3 among all myopic algorithms. • A far-sighted algorithm with proper waiting strategy can achieve 8/3-competitive ratio. [ABSTRACT FROM AUTHOR]

Nowadays, malicious rumors on social media have caused increasingly negative impacts. Given the rapid dissemination nature of rumors, it is urgent to design automatic methods to detect the veracity of rumors. Previous studies have shown that the multi-task learning paradigm with the stance classification could facilitate the successful detection of rumors, but the shared layers in multi-task learning tend to yield a compromise between the general and the task-specific representation of structural information. To address this issue, we propose a novel Multi-Task Learning framework with Shared Multi-channel Interactions (MTL-SMI), which is composed of two shared channels and two task-specific graph channels. The shared channels extract task-invariant text features and structural features, and the task-specific graph channels, by interacting with the shared channels, extract the task-enhanced structural features. These channels are learned jointly to enhance the ability of representation learning. The experimental results on two real-world datasets demonstrate the superiority of MTL-SMI against strong baselines. [ABSTRACT FROM AUTHOR]

Although lithium–sulfur batteries are promising replacements for conventional lithium-ion batteries, their commercial applications have been limited due to issues such as polysulfide dissolution, volume variation, and low conductivity. To overcome these problems, we designed and hydrothermally synthesized a flower-like three-dimensional CoS/multiwall carbon nanotube (MWCNT) composite as the sulfur host material. The CoS and MWCNTs were tightly joined by chemical bonds to form a stable composite structure. The polar and catalytic CoS can promote the redox process and reduce the dissolution of polysulfides, while the flexible carbon nanotubes can buffer the expansion/shrinkage of the electrode during charging and discharging. CoS/MWCNT-S displays an excellent specific capacity (1120 mAh g−1, 0.1 C) and high capacity retention after 200 cycles (78%, 0.5 C). [ABSTRACT FROM AUTHOR]

We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confidence intervals which give the exact ranges that unlabeled vertices take in any optimal solution. Moreover, we give a much simpler approach for solving the convex program based on the subgradient method. Our experiments on real-world datasets confirm that our confidence interval approach on hypergraphs outperforms existing methods, and our subgradient method gives faster running times when the number of vertices is much larger than the number of edges. Our experiments also support that using directed hypergraphs to capture causal relationships can improve the prediction accuracy. Furthermore, our model can be readily extended to capture multiclass learning. [ABSTRACT FROM AUTHOR]

A unique 3D porous graphene aerogel wrapped LiNi 0.6 Co 0.2 Mn 0.2 O 2 nanoparticle composite (NCM@GA) was prepared via a facile coprecipitation followed with a hydrothermal treatment. The morphological and structural characterizations reveal that the graphene nanosheets adhere tightly to the NCM nanoparticle surface and randomly intertwine to form a three-dimensional porous conductive aerogel network. The galvanostatic charge/discharge tests imply that the NCM@GA composite displays a high initial discharge capacity of 189.9 mAh g−1 (0.1 C), an excellent rate capability of 130.9 mAh g−1 (5 C) and 106.8 mAh g−1 (10 C), and an outstanding cyclic stability (an average decay rate of approximately 0.04% over 200 cycles at 1 C). The outstanding electrochemical performance is due to the synergy between the porous GA framework and the well-dispersed NCM nanoparticles, which facilitates the rapid diffusion of both Li+ and electrons and accommodates the volumetric change during the Li+ insertion/deinsertion process. The novel electrode design strategy described here is promising for constructing other advanced composite cathodes used in power Li-ion batteries. Image 1 • NCM nanoparticles are successfully synthesized by a facile coprecipitation. • Novel 3D porous graphene aerogel wrapped NCM nanoparticle composite is prepared. • NCM@GA displays superior Li-insertion performance to the pristine NCM. • The enhancement is due to the synergy effect of GA and NCM nanoparticles. [ABSTRACT FROM AUTHOR]

Acoustic tomography is a widely used non-contact method for visualizing temperature distribution. A temperature distribution reconstruction algorithm based on an improved sparse reconstruction model and multi-scale feature fusion network is proposed. First, the acoustic temperature measurement sparse reconstruction model is improved by combining the error function (ERF) and the iterative reweighting algorithm, and the alternating direction method of multipliers algorithm (ADMM) is used to solve the model to obtain the initial temperature distribution. Then the feature extraction network is constructed to extract multi-scale features of acoustic time of flight (TOF) as prior information. Finally, the feature fusion reconstruction network is constructed to fuse and reconstruct the initial temperature distribution and multi-scale features to obtain a high-precision temperature distribution. Simulation and experimental tests were conducted respectively, and compared with other algorithms. The results show that the average relative error and root mean square error of the simulated temperature distribution reconstruction are 0.073% and 0.1% respectively, the average reconstruction error of the temperature points set in the experimental test is 0.38%, and the reconstruction errors are lower than other algorithms. The proposed method effectively utilizes prior information to correct sparse reconstruction temperature distribution reconstruction results, significantly improving the quality of temperature distribution reconstruction. [Display omitted] • Acoustic tomography technology is used for temperature distribution reconstruction. • The improved sparse reconstruction model can improve the accuracy of initial temperature distribution reconstruction. • The feature extraction network can perform multi-scale feature extraction on the time-of-flight of acoustic waves. • The feature fusion reconstruction network can improve the utilization of prior information. [ABSTRACT FROM AUTHOR]

Acoustic tomography is a widely used non-contact method for visualizing temperature distribution. A temperature distribution reconstruction algorithm based on an improved sparse reconstruction model and multi-scale feature fusion network is proposed. First, the acoustic temperature measurement sparse reconstruction model is improved by combining the error function (ERF) and the iterative reweighting algorithm, and the alternating direction method of multipliers algorithm (ADMM) is used to solve the model to obtain the initial temperature distribution. Then the feature extraction network is constructed to extract multi-scale features of acoustic time of flight (TOF) as prior information. Finally, the feature fusion reconstruction network is constructed to fuse and reconstruct the initial temperature distribution and multi-scale features to obtain a high-precision temperature distribution. Simulation and experimental tests were conducted respectively, and compared with other algorithms. The results show that the average relative error and root mean square error of the simulated temperature distribution reconstruction are 0.073% and 0.1% respectively, the average reconstruction error of the temperature points set in the experimental test is 0.38%, and the reconstruction errors are lower than other algorithms. The proposed method effectively utilizes prior information to correct sparse reconstruction temperature distribution reconstruction results, significantly improving the quality of temperature distribution reconstruction. [Display omitted] • Acoustic tomography technology is used for temperature distribution reconstruction. • The improved sparse reconstruction model can improve the accuracy of initial temperature distribution reconstruction. • The feature extraction network can perform multi-scale feature extraction on the time-of-flight of acoustic waves. • The feature fusion reconstruction network can improve the utilization of prior information. [ABSTRACT FROM AUTHOR]

This study aims to identify the level and trend of alcohol imagery in popular films in China from 2001 to 2020. We divided the running time of the annual 20 top-grossing films in China into 5-min intervals and coded those containing alcohol imagery, the presence of warnings, whether the imagery was related to minors and alcohol brands. Results showed that alcohol imagery occurred in 90.75% (363/400) of the films and 25.26% (2380/9423) of the intervals; these proportions remained stable over time. No film containing alcohol imagery had warnings, alcohol imagery related to minors appeared each year, and 103 alcohol brands were present in 185 intervals across 93 of the 400 films. Chinese films contained more alcohol imagery than international films. National policies are required to restrict alcohol imagery in films and to reduce the availability of such films for viewing by young people. [ABSTRACT FROM AUTHOR]

For complicated dynamic systems, such as free-floating space manipulators (FFSMs), building a sufficiently precise model to predict their long-term behavior is still quite challenging, as nonlinearities that are too complicated to describe always exist. In this paper, a probabilistic ensemble neural network (PENN) is proposed to predict the long-term dynamic behavior. The PENN combines the probabilistic deep network dynamics model with the ensemble method and uses sampling-based uncertainty propagation for long-term prediction to address the aleatoric and epistemic uncertainties in dynamic system estimation. A simulation demonstrates the effectiveness of the PENN. The joint angular position error is approximately 0.05 deg for one-step prediction and 10 deg for 100-step propagation. A comparison with cases in which only either probabilistic NNs or the ensemble method is used shows the urgent need to combine them to deal with the compounding error of long-term prediction. The effects of different hyperparameters are evaluated in terms of the predictive error after 100 steps of bootstrap propagation. The results show that the PENN is robust to the hyperparameters. [ABSTRACT FROM AUTHOR]

The solid electrolyte interphase (SEI) film plays a significant role in the capacity and storage performance of lithium primary batteries. The electrolyte additives are essential in controlling the morphology, composition and structure of the SEI film. Herein, fluoroethylene carbonate (FEC) is chosen as the additive, its effects on the lithium primary battery performance are investigated, and the relevant formation mechanism of SEI film is analyzed. By comparing the electrochemical performance of the Li/AlF3 primary batteries and the microstructure of the Li anode surface under different conditions, the evolution model of the SEI film is established. The FEC additive can decrease the electrolyte decomposition and protect the lithium metal anode effectively. When an optimal 5% FEC is added, the discharge specific capacity of the Li/AlF3 primary battery is 212.8 mAh g−1, and the discharge specific capacities are respectively 205.7 and 122.3 mAh g−1 after storage for 7 days at room temperature and 55 °C. Compared to primary electrolytes, the charge transfer resistance of the Li/AlF3 batteries with FEC additive decreases, indicating that FEC is a promising electrolyte additive to effectively improve the SEI film, increase discharge-specific capacities and promote charge transfer of the lithium primary batteries. [ABSTRACT FROM AUTHOR]

Due to the increasingly urgent demand for effective wastewater denitrification and dephosphorization systems, there is a need to improve the performance of existing biological treatment technologies. As a bacteria‐level communication mechanism, quorum sensing (QS) synchronizes gene expression in a density‐dependent manner and regulates bacterial physiological behavior. On this basis, the QS‐based bacterial communication mechanism and environmental factors affecting QS are discussed. This paper reviews the influence of QS on sludge granulation, biofilm formation, emerging contaminants (ECs) removal, and horizontal gene transfer in sewage treatment system. Furthermore, the QS inhibition strategies are compared. Based on the coexistence and balance of QQ and QS in the long‐term operation system, QQ, as an effective tool to regulate the growth density of microorganisms, provides a promising exogenous regulation strategy for residual sludge reduction and biofilm pollution control. This paper reviews the potential of improving wastewater treatment efficiency based on QS theory and points out the feasibility and prospect of exogenous regulation strategy. Practitioner Points: The mechanism of bacterial communication based on QS and the environmental factors affecting QS were discussed.The application of QS and QQ in improving the sludge performance of biological treatment systems was described.The significance of QS and QQ coexistence in sewage treatment process was described. [ABSTRACT FROM AUTHOR]

• Established a discrete electrochemical–thermal model of lithium-ion battery. • Updated model parameters according to battery internal temperature. • Validated the model under different current and temperature conditions. • Simulated the 'trough' phenomenon at −20 °C successfully. The internal temperature of lithium-ion battery affects the battery parameters during discharge and charge process, and the low-temperature environment has a particularly significant impact on the performance of the lithium-ion battery. In this paper, a simplified discrete electrochemical model adapts to a wide temperature range is developed. Based on Pseudo-Two-Dimension (P2D) model, we simplify the governing equation to describe the solid and solution diffusion by fitting the lithium-ion concentration in the form of parabolas. Analysis of the heat generation and dissipation conditions of lithium-ion batteries is conducted, and a concentrated mass thermal model is established to describe the internal temperature changes of batteries. The parameters involved in the electrochemical model are also updated accordingly. Finally, an electrochemical–thermal coupling model is established. In order to verify the cell potential difference simulation accuracy of the model, the constant current discharge conditions, DST (Dynamic Stress Test) in the ambience above 0 °C and the multi-stage current discharge conditions in the ambience below 0 °C is carried out. The results show that the model with parameters update significantly improves the accuracy of simulation, especially at low temperature (−20 °C, −10 °C, 0 °C). What's more, the model with parameter update can simulate the potential difference's 'trough' phenomenon in the constant current phase of the pulse discharge at -20°C better than the model without parameter update. [ABSTRACT FROM AUTHOR]

[Display omitted] • Alternating FNA/heat shock treatment was used to keep stable nitrite accumulation. • Nitrite accumulation ratio (NAR) was stable above 90% during the long-term study. • The linear regression method was used to determine nitritation parameters. • NOB were effectively inhibited and AOB abundance was maximized. The feasibility of stable nitritation of sludge alternately treated by free nitrous acid (FNA) and heat shock in a sequencing batch reactor (SBR) was investigated in this study. The linear regression method was used to determine the optimal treatment conditions. Results revealed that an FNA concentration of 2.20 mg HNO 2 -N/L, exposure time of 24 h, and treatment ratio of 20% could inhibit nitrite oxidizing bacteria (NOB) activity to the greatest extent while maintaining the maximum ammonium oxidizing bacteria (AOB) activity; after heat shock at 60 °C for 20 min, NOB were inhibited while AOB still had certain activity. In the long-term continuous-flow experiment, the single FNA or heat shock treatments easily allowed adapt NOB to affect the stability of nitritation. The alternating FNA/heat shock treatment can achieve long-term stability of nitritation. Microbial community analysis revealed that the alternating FNA/heat shock treatment could inhibit NOB while maintaining high AOB abundance. [ABSTRACT FROM AUTHOR]

Lithium-sulfur batteries have emerged as extraordinarily favorable energy storage devices due to their high specific capacity and energy density, safety and low cost. Unfortunately, the wide applications of lithium-sulfur batteries are hampered by several issues, such as the low electronic conductivity and slow redox kinetics, serious volumetric expansion and polysulfide "shuttle effect". To overcome these issues, in our work, we design and synthesize a composite sulfur host material of Co 9 S 8 microspheres and N-doped carbon nanotubes, where the metallic sulfide Co 9 S 8 with a good conductivity enables the immobilization of the polar lithium polysulfides owing to the strong polar chemisorptive capability, and the one dimensional N-doped carbon nanotubes can provide channels for fast electron and lithium-ion transport. As the lithium polysulfides are well confined, and the redox conversions are promoted, the Co 9 S 8 @N-CNTs/S-based lithium-sulfur battery possesses a superior energy storage performance, exhibiting a large specific capacity of 1233 mAh g−1 at 0.1 C and an outstanding cyclic performance, with a low decay of 0.045% per cycle and a Coulombic efficiency of more than 99% after 1000 cycles. Image 1 • Co 9 S 8 @N-CNT composite host was synthesized by a facile solvothermal method. • The composite cathode delivered high specific capacity and cycling performance. • Polysulfide shuttling was suppressed due to the strong adsorption and confinement. [ABSTRACT FROM AUTHOR]

The development of high-performance and new-structure electrode materials is vital for the wide application of rechargeable lithium batteries in electric vehicles. In this work, we design a special composite electrode structure with the macroporous three-dimensional graphene areogel framework supporting mesoporous LiFePO 4 nanoplate. It is realized using a simple sol-gel deposition method. The highly conductivity graphene nanosheets assemble into an interconnected three-dimensional macroporous areogel framework, while LiFePO 4 grows along the graphene nanosheets and generates a mesoporous nanoplate structure. In comparison with LiFePO 4 , this unique sandwich nanostructure offers a greatly increased electronic conductivity thanks to the framework of graphene nanosheets. Also, the bimodal porous structure of the composite remarkably increases the interface between the electrode/electrolyte and facilitates the transport of Li+ throughout the electrode, enabling the superior specific capacity, rate characteristic and cyclic retention. Image 1 • A novel bimodal macro- and meso-porous LFP-GA composite is facilely synthesized. • Mesoporous LFP nanoplate and graphene are layer-by-layer assembled. • The aerogel composite presents remarkably improved Li-insertion performance. • The performance enhancement is ascribed to the unique porous composite structure. [ABSTRACT FROM AUTHOR]