High-Performance Sodium-Ion Batteries with Graphene: An Overview of Recent Developments and Design.

Due to their low production cost, sodium-ion batteries (SIBs) are considered attractive alternatives to lithium-ion batteries (LIBs) for next generation sustainable and large-scale energy storage systems. However, during the charge/discharge cycle, a large volume strain is resulted due to the presence of a large radius of sodium ions and high molar compared to lithium ions, which further leads to poor cyclic stability and lower reversible capacity. In the past, researchers have devoted significant efforts to explore various anode materials to achieve SIBs with high energy density. Hence, as a promising anode material for SIBs, the two-dimensional (2D) materials including graphene and its derivatives and metal oxides have attracted remarkable attention due to their layered structure and superior physical and chemical properties. The inclusion of graphene and metal oxides with other nanomaterials in electrodes have led to the significant enhancements in electrical conductivity, reaction kinetics, capacity, rate performance and accommodating the large volume change respectively. Moreover, these 2D materials facilitated large surface areas and shorter paths for sodium ion adsorption and transportation respectively. In this review article, the fabrication techniques, structural configuration, sodium ion storage mechanism and its electrochemical performances will be introduced. Subsequently, an insight into the recent advancements in SIBs associated with 2D anode materials (graphene, graphene oxide (GO), transition metal oxides etc.) and other graphene-like elementary analogues (germanene, stanine etc.) as anode materials respectively will be discussed. Finally, the key challenges and future perspectives of SIBs towards enhancing the sodium storage performance of graphene-based electrode materials are discussed. In summary, we believe that this review will shed light on the path towards achieving long-cycling life, low operation cost and safe SIBs with high energy density using 2D anode materials and to be suitably commercialized for large-scale energy storage applications in the future.

Evaluation of the effect of precursor ratios on the electrochemical performances of binder-free NiMn-phosphate electrodes for supercapattery.

Binary metal phosphate electrodes have been widely studied for energy storage applications due to the synergistic effects of two different transition elements that able to provide better conductivity and stability. Herein, the battery-type binder-free nickel-manganese phosphate (NiMn-phosphate) electrodes were fabricated with different Ni:Mn precursor ratios via microwave-assisted hydrothermal technique for 5 min at 90 °C. Overall, NiMn3P electrode (Ni:Mn = 1:3) showed an outstanding electrochemical performance, displaying the highest specific (areal) capacity at 3 A/g of 1262.4 C/g (0.44 C/cm2), and the smallest charge transfer resistance of 108.8 Ω. The enhanced performance of NiMn3P electrode can be ascribed to the fully grown amorphous nature and small-sized flake and flower structures of NiMn3P electrode material on the nickel foam (NF) surface. This configuration offered a higher number of active sites and a larger exposed area, facilitating efficient electrochemical reactions with the electrolyte. Consequently, the NiMn3P//AC electrode combination was chosen to further investigate its performance in supercapattery. The NiMn3P//AC supercapattery exhibited remarkable energy density of 105.4 Wh/kg and excellent cyclic stability with 84.7% retention after 3000 cycles. These findings underscored the superior electrochemical performance of the battery-type binder-free NiMn3P electrode, and highlight its potential for enhancing the overall performance of supercapattery.

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

Mycoplasmas and acholeplasmas isolated from ducks and their possible association with pasteurellas.

Two hundred and sixty-three cases of clinically diseased ducks of all ages were examined for the presence of mycoplasmas. Mycoplasmas and acholeplasmas belonging to more than eight serogroups were cultured from 68 of them, and comprised 12 M anatis, one M columbinasale, two M gallinaceum, two M gallinarum, nine M synoviae, three unidentified Mycoplasma species, 37 Acholeplasma laidlawii and one unclassified acholeplasma belonging to each of serogroups 7 and 8. They were identified by biochemical characterisation, disc growth inhibition and agar gel diffusion tests. Fifty-three (78 per cent) of the isolates occurred with species of Pasteurella: 33.8 per cent with Pasteurella anatipestifer, 32.4 per cent with P multocida and 11.8 per cent with both P anatipestifer and P multocida. Nine of the isolates (13.2 per cent) were in pure culture and six (8.8 per cent) with other agents. Of the ducks negative for mycoplasmas 33.3 per cent were infected with P anatipestifer, 25.1 per cent with P multocida and 14.4 per cent with both P anatipestifer and P multocida. There was no correlation between the infections with mycoplasmas and P anatipestifer but there was a weak association between the infections with mycoplasmas, especially M anatis and P multocida.

Isolation and identification of mycoplasmas from pig lungs in Singapore.

A total of 656 lung specimens comprising 196 grossly pneumonic lungs from clinically diseased pigs and 230 grossly pneumonic and normal lungs from abattoirs were cultured for mycoplasmas. Mycoplasmas or acholeplasmas were recovered from 102 lung specimens, of which 28 were serologically identified (by disc growth inhibition and agar gel diffusion tests) as Mycoplasma suipneumonia, 14 as M hyorhinis, 35 as Acholeplasma granularum and 4 as A laidlawii. In addition, 15 isolates were unclassified and six were not typed. Mycoplasmas isolated from the suspect enzootic pneumonic lungs: slaughterhouse pneumonic lungs: normal lungs were in the ratio of 3:2:1. M suipneumonia, A granularum and A laidlawii were isolated from both normal and pneumonic lungs of pigs while M hyorhinis was only isolated from pneumonic ones. A close antigenic relationship between M suipneumonia and A granularum was demonstrated.

Isolation and identification of avian mycoplasmas in Singapore.

Two hundred and forty batches of chickens with chronic respiratory syndrome were tested for mycoplasmas. One hundred and five batches (43.8%) were found to have mycoplasmosis. A total of 110 isolates of mycoplasma was cultured, of which nine isolates were identified as Mycoplasma gallisepticum, 48 avian sero-group D, 45 M. gallinarum, one M. iners and seven unclassified. 2. Identification of the mycoplasmas isolated was carried out by biochemical and serological tests (disc growth inhibition and agar gel diffusion tests). A modified agar gel diffusion test using solubilised antigens with sodium deoxycholate-glucose solution was developed for serotying of mycoplasmas.