Vascular Surgery Practice Guidelines during COVID-19 Pandemic in a Setting of High Work Volume Against Limited Resources: Perspective of a Developing Country.

BACKGROUND: The situation of coronavirus disease 2019 (COVID-19) pandemic in the Indian subcontinent is worsening. In Bangladesh, rate of new infection has been on the rise despite limited testing facility. Constraint of resources in the health care sector makes the fight against COVID-19 more challenging for a developing country like Bangladesh. Vascular surgeons find themselves in a precarious situation while delivering professional services during this crisis. With the limited number of dedicated vascular surgeons in Bangladesh, it is important to safeguard these professionals without compromising emergency vascular care services in the long term. To this end, we at the National Institute of Cardiovascular Diseases and Hospital, Dhaka, have developed a working guideline for our vascular surgeons to follow during the COVID-19 pandemic. The guideline takes into account high vascular work volume against limited resources in the country. METHODS: A total of 307 emergency vascular patients were dealt with in the first 4 COVID-19 months (March through June 2020) according to the working guideline, and the results were compared with the 4 pre-COVID-19 months. Vascular trauma, dialysis access complications, and chronic limb-threatening ischemia formed the main bulk of the patient population. Vascular health care workers were regularly screened for COVID-19 infection. RESULTS: There was a 38% decrease in the number of patients in the COVID-19 period. Treatment outcome in COVID-19 months were comparable with that in the pre-COVID-19 months except that limb loss in the chronic limb-threatening ischemia patients was higher. COVID-19 infection among the vascular health care professionals was low. CONCLUSIONS: Vascular surgery practice guidelines customized for the high work volume and limited resources of the National Institute of Cardiovascular Diseases and Hospital, Dhaka were effective in delivering emergency care during COVID-19 pandemic, ensuring safety of the caregivers. Despite the fact that similar guidelines exist in different parts of the world, we believe that the present one is still relevant on the premises of a deepening COVID-19 crisis in a developing country like Bangladesh.

Lithium Germanate (Li2 GeO3 ): A High-Performance Anode Material for Lithium-Ion Batteries.

A simple, cost-effective, and easily scalable molten salt method for the preparation of Li2 GeO3 as a new type of high-performance anode for lithium-ion batteries is reported. The Li2 GeO3 exhibits a unique porous architecture consisting of micrometer-sized clusters (secondary particles) composed of numerous nanoparticles (primary particles) and can be used directly without further carbon coating which is a common exercise for most electrode materials. The new anode displays superior cycling stability with a retained charge capacity of 725 mAh g-1 after 300 cycles at 50 mA g-1 . The electrode also offers excellent rate capability with a capacity recovery of 810 mAh g-1 (94 % retention) after 35 cycles of ascending steps of current in the range of 25-800 mA g-1 and finally back to 25 mA g-1 . This work emphasizes the importance of exploring new electrode materials without carbon coating as carbon-coated materials demonstrate several drawbacks in full devices. Therefore, this study provides a method and a new type of anode with high reversibility and long cycle stability.

Hidden risk of terrestrial food chain contamination from organochlorine insecticides in a vegetable cultivation area of Northwest Bangladesh.

Organochlorine insecticide (OCI) exposures in terrestrial food chains from historical or current applications were studied in a vegetable production area in northwest Bangladesh. A total of 57 subsoil, 57 topsoil, and 57 vegetable samples, as well as 30 cow's milk samples, were collected from 57 farms. Multiple OCI residues were detected using GC-MS/MS with modified QuEChERS in 20 % of subsoils, 21 % of topsoils, 23 % of vegetables, and 7 % of cow's milk samples. Diversified OCI residues were detected in subsoils (17 residues with a concentration of 179.15 ± 148.61 µg kg-1) rather than in topsoils (3 DDT residues with a concentration of 25.76 ± 20.19 µg kg-1). Isomeric ratios indicate intensive historical applications of OCIs. According to Dutch and Chinese standards, the lower concentrations of individual OCI residues in the soil indicate negligible to slight soil pollution, assuming local farmers follow local pesticide use regulations. However, a maximum of 78.24 µg kg-1 ΣAldrines and 35.57 µg kg-1 ΣHCHs were detected (1-4 residues) in 60 % of brinjal, 28 % of cucumber, 29 % of sponge gourd, and 20 % of lady's finger samples, which could be a result of either historical or current OCI applications, or both. A strong positive correlation between aldrines in subsoils and cucurbit vegetables indicates greater bioaccumulation. Cow milk samples contained up to 6.96 µg kg-1 ΣDDTs, which resulted either from rationing contaminated vegetables or grazing on contaminated land. Individual OCI in both vegetables and cow's milk was below the respective maximum residue limits of US and FAO/WHO CODEX and poses little or no risk to human health. However, combined exposure to multiple pesticides could increase human health risks. A cumulative health risk assessment of multiple pesticide residues is suggested to assess the suitability of those soils for cultivation and grazing, as well as the safety of vegetables and cow's milk for human consumption.

Clusters of α-LiFeO2 nanoparticles incorporated into multi-walled carbon nanotubes: a lithium-ion battery cathode with enhanced lithium storage properties.

We report the preparation of a novel nanocomposite architecture of α-LiFeO2-MWCNT based on clusters of α-LiFeO2 nanoparticles incorporated into multiwalled carbon nanotubes (MWCNTs). The composite represents a promising cathode material for lithium-ion batteries. The preparation of the nanocomposite is achieved by combining a molten salt precipitation process and a radio frequency oxygen plasma for the first time. We demonstrate that clusters of α-LiFeO2 nanoparticles incorporated into MWCNTs are capable of delivering a stable and high reversible capacity of 147 mA h g(-1) at 1 C after 100 cycles with the first cycle Coulombic efficiency of ~95%. The rate capability of the composite is significantly improved and its reversible capacity is measured to be 101 mA h g(-1) at a high current rate of 10 C. Both rate capability and cycling stability are not simply a result of introduction of functionalized MWCNTs but most likely originate from the unique composite structure of clusters of α-LiFeO2 nanoparticles integrated into a network of MWCNTs. The excellent electrochemical performance of this new nanocomposite opens up new opportunities in the development of high-performance electrode materials for energy storage application using the radio frequency oxygen plasma technique.

A Study on High-Rate Performance of Graphite Nanostructures Produced by Ball Milling as Anode for Lithium-Ion Batteries.

Graphite, with appealing features such as good stability, high electrical conductivity, and natural abundance, is still the main commercial anode material for lithium-ion batteries. The charge-discharge rate capability of graphite anodes is not significant for the development of mobile devices and electric vehicles. Therefore, the feasibility investigation of the rate capability enhancement of graphite by manipulating the structure is worthwhile and of interest. In this study, an effective ball-milling process has been set up by which graphite nanostructures with a high surface area are produced. An in-depth investigation into the effect of ball milling on graphite structure as well as electrochemical performance, particularly rate capability, is conducted. Here, we report that graphite nanoflakes with 350 m2 g-1 surface area deliver retained capacity of ~75 mAh g-1 at 10 C (1 C = 372 mA g-1). Finally, the Li+ surface-storage mechanism is recognised by associating the structural characteristics with electrochemical properties.

Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach.

Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world.

Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries.

Recognizing an urgent need to understand the dynamics of the pandemic's severity, this longitudinal study is conducted to explore the evolution of complex relationships between the COVID-19 pandemic, lockdown measures, and social distancing patterns in a diverse set of 86 countries. Collecting data from multiple sources, a structural equation modeling (SEM) technique is applied to understand the interdependencies between independent variables, mediators, and dependent variables. Results show that lockdown and confinement measures are very effective to reduce human mobility at retail and recreation facilities, transit stations, and workplaces and encourage people to stay home and thereby control COVID-19 transmission at critical times. The study also found that national contexts rooted in socioeconomic and institutional factors influence social distancing patterns and severity of the pandemic, particularly with regard to the vulnerability of people, treatment costs, level of globalization, employment distribution, and degree of independence in society. Additionally, this study portrayed a mutual relationship between the COVID-19 pandemic and human mobility. A higher number of COVID-19 confirmed cases and deaths reduces human mobility and the countries with reduced personal mobility have experienced a deepening of the severity of the pandemic. However, the effect of mobility on pandemic severity is stronger than the effect of pandemic situations on mobility. Overall, the study displays considerable temporal changes in the relationships between independent variables, mediators, and dependent variables considering pandemic situations and lockdown regimes, which provides a critical knowledge base for future handling of pandemics. It has also accommodated some policy guidelines for the authority to control the transmission of COVID-19.

Multiple indicator cluster survey 2003 in Afghanistan: outdated sampling frame and the effect of sampling weights on estimates of maternal and child health coverage.

Due to an urgent need for information on the coverage of health service for women and children after the fall of Taliban regime in Afghanistan, a multiple indicator cluster survey (MICS) was conducted in 2003 using the outdated 1979 census as the sampling frame. When 2004 pre-census data became available, population-sampling weights were generated based on the survey-sampling scheme. Using these weights, the population estimates for seven maternal and child healthcare-coverage indicators were generated and compared with the unweighted MICS 2003 estimates. The use of sample weights provided unbiased estimates of population parameters. Results of the comparison of weighted and unweighted estimates showed some wide differences for individual provincial estimates and confidence intervals. However, the mean, median and absolute mean of the differences between weighted and unweighted estimates and their confidence intervals were close to zero for all indicators at the national level. Ranking of the five highest and the five lowest provinces on weighted and unweighted estimates also yielded similar results. The general consistency of results suggests that outdated sampling frames can be appropriate for use in similar situations to obtain initial estimates from household surveys to guide policy and programming directions. However, the power to detect change from these estimates is lower than originally planned, requiring a greater tolerance for error when the data are used as a baseline for evaluation. The generalizability of using outdated sampling frames in similar settings is qualified by the specific characteristics of the MICS 2003-low replacement rate of clusters and zero probability of inclusion of clusters created after the 1979 census.

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.

Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer.

Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.

First Detection of Batrachochytrium dendrobatidis in Wild Frogs from Bangladesh.

Global amphibian populations are facing a novel threat, chytridiomycosis, caused by the fungus Batrachochytrium dendrobatidis (Bd), which is responsible for the severe decline of a number of species across several continents. Chytridiomycosis in Asia is a relatively recent discovery yet there have been no reports on Bd-presence in Bangladeshi amphibians. We conducted a preliminary study on 133 wild frogs from seven sites in Bangladesh between April and July 2018. Nested PCR analysis showed 20 samples (15.04%) and 50% of the tested taxa (9 species from 6 genera and 4 families) as Bd-positive. Eight of the nine species are discovered as newly infected hosts. Analysis of Bd-positive samples shows prevalence does not significantly vary among different land cover categories, although the occurrence is higher in forested areas. The prevalence rate is similar in high and low disturbed areas, but the range of occurrence is statistically higher in low disturbance areas. Maximum entropy distribution modeling indicates high probabilities of Bd occurrence in hilly and forested areas in southeast and central-north Bangladesh. The Bd-specific ITS1-5.8S-ITS2 ribosomal gene sequence from the Bd-positive samples tested is completely identical. A neighbor-joining phylogenetic tree reveals that the identified strain shares a common ancestry with strains previously discovered in different Asian regions. Our results provide the first evidence of Bd-presence in Bangladeshi amphibians, inferring that diversity is at risk. The effects of environmental and climatic factors along with quantitative PCR analysis are required to determine the infection intensity and susceptibility of amphibians in the country.

Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review.

The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics.

There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.

Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data.

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel patterns due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 50 States of the US and Washington D.C, the capital city of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of family members and high income are less interested in reopening the economy. The accuracy of the model is reasonable (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi-squared test indicates that this model has high goodness-of-fit. This study provides clear insights for public and corporate policymakers on potential areas to allocate resources, and directional guidance on potential policy options they can undertake to improve socioeconomic conditions, to mitigate the impact of pandemic in the current situation, and in the future as well.

In situ production of a two-dimensional molybdenum disulfide/graphene hybrid nanosheet anode for lithium-ion batteries.

A solvent-free, low-cost, high-yield and scalable single-step ball milling process is developed to construct 2D MoS2/graphene hybrid electrodes for lithium-ion batteries. Electron microscopy investigation reveals that the obtained hybrid electrodes consist of numerous nanosheets of MoS2 and graphene which are randomly distributed. The MoS2/graphene hybrid anodes exhibit excellent cycling stability with high reversible capacities (442 mA h g-1 for MoS2/graphene (40 h); 553 mA h g-1 for MoS2/graphene (20 h); 342 mA h g-1 for MoS2/graphene (10 h)) at a high current rate of 250 mA g-1 after 100 cycles, whereas the pristine MoS2 electrode shows huge capacity fading with a retention of 37 mA h g-1 at 250 mA g-1 current after 100 cycles. The incorporation of graphene into MoS2 has an extraordinary effect on its electrochemical performance. This work emphasises the importance of the construction of the 2D MoS2/graphene hybrid structure to prevent capacity fading issues with the MoS2 anode in lithium-ion batteries.

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics.

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

Electrochemical Sodiation/Desodiation into Mn3O4 Nanoparticles.

Mn3O4 is considered to be a promising anode material for sodium-ion batteries (SIBs) because of its low cost, high capacity, and enhanced safety. However, the inferior cyclic stability of the Mn3O4 anode is a major challenge for the development of SIBs. In this study, a one-step solvothermal method was established to produce nanostructured Mn3O4 with an average particle size of 21 nm and a crystal size of 11 nm. The Mn3O4 obtained exhibits a unique architecture, consisting of small clusters composed of numerous tiny nanoparticles. The Mn3O4 material could deliver high capacity (522 mAh g-1 at 100 mA g-1), reasonable cyclic stability (158 mAh g-1 after 200 cycles), and good rate capability (73 mAh g-1 at 1000 mA g-1) even without further carbon coating, which is a common exercise for most anode materials so far. The sodium insertion/extraction was also confirmed by a reversible conversion reaction by adopting an ex situ X-ray diffraction technique. This simple, cost-effective, and environmentally friendly synthesis technique with good electrochemical performance shows that the Mn3O4 nanoparticle anode has the potential for SIB development.

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

Investigation on the Electrochemical Performances of Mn2O3 as a Potential Anode for Na-Ion Batteries.

Currently, the development of the sodium-ion (Na-ion) batteries as an alternative to lithium-ion batteries has been accelerated to meet the energy demands of large-scale power applications. The difficulty of obtaining suitable electrode materials capable of storing large amount of Na-ion arises from the large radius of Na-ion that restricts its reversible capacity. Herein, Mn2O3 powders are synthesised through the thermal conversion of MnCO3 and reported for the first time as an anode for Na-ion batteries. The phase, morphology and charge/discharge characteristics of Mn2O3 obtained are evaluated systematically. The cubic-like Mn2O3 with particle sizes approximately 1.0-1.5 µm coupled with the formation of Mn2O3 sub-units on its surface create a positive effect on the insertion/deinsertion of Na-ion. Mn2O3 delivers a first discharge capacity of 544 mAh g-1 and retains its capacity by 85% after 200 cycles at 100 mA g-1, demonstrating the excellent cyclability of the Mn2O3 electrode. Therefore, this study provides a significant contribution towards exploring the potential of Mn2O3 as a promising anode in the development of Na-ion batteries.

K-ion and Na-ion storage performances of Co3O4-Fe2O3 nanoparticle-decorated super P carbon black prepared by a ball milling process.

The hybridisation of Co3O4 and Fe2O3 nanoparticles dispersed in a super P carbon matrix is proposed as a favourable approach to improve the electrochemical performance (reversible capacity, cycling stability and rate capability) of the metal oxide electrodes in metal-ion batteries. Hybrid Co3O4-Fe2O3/C is prepared by a simple, cheap and easily scalable molten salt method combined with ball-milling and used in sodium-ion and potassium-ion batteries for the first time. The electrode exhibits excellent cycling stability and superior rate capability in sodium-ion cells with a capacity recovery of 440 mA h g-1 (93% retention) after 180 long-term cycles at 50-1000 mA g-1 and back to 50 mA g-1. In contrast, Co3O4-Fe2O3, Co3O4 and Fe2O3 electrodes display unsatisfactory electrochemical performance. The hybrid Co3O4-Fe2O3/C is also reactive with potassium and capable of delivering a reversible capacity of 220 mA h g-1 at 50 mA g-1 which is comparable with the most reported anode materials for potassium-ion batteries. The obtained results broaden the range of transition metal oxide-based hybrids as potential anodes for K-ion and Na-ion batteries, and suggest that further studies of these materials with potassium and sodium are worthwhile.