Understanding the oxidation chemistry of Ti_(3)C_(2)T_(x)(MXene)sheets and their catalytic performances

Transition metal carbides and nitrides(MXenes)nanosheets are attractive two-dimensional(2D)materials,but they suffer from oxidation/degradation issues during storage and/or applications due to their sensitivity to water and oxygen.Despite the great research progress,the exact oxidation kinetics of Ti_(3)C_(2)T_(x)(MXene)and their final products after oxidation are not fully understood.Herein,we systematically tracked the oxidation process of few-layer Ti_(3)C_(2)T_(x) nanosheets in an aqueous solution at room temperature over several weeks.We also studied the oxidation effects on the electrocatalytic properties of Ti_(3)C_(2)T_(x) for hydrogen evolution reaction and found that the overpotential to achieve a current density of 10 mA cm^(-2)increases from 0.435 to 0.877 V after three weeks of degradation,followed by improvement to stabilized values of around 0.40 V after eight weeks.These results suggest that severely oxidized MXene could be a promising candidate for designing efficient catalysts.According to our detailed experimental characterization and theoretical calculations,unlike previous studies,black titanium oxide is formed as the final product in addition to white Ti(IV)oxide and disordered carbons after the complete oxidation of Ti_(3)C_(2)T_(x).This work presents significant advancements in better understanding of 2D Ti_(3)C_(2)T_(x)(MXene)oxidation and enhances the prospects of this material for various applications.

Predicting the returns of the US real estate investment trust market:evidence from the group method of data handling neural network

Purpose:The Group Method of Data Handling(GMDH)neural network has demon-strated good performance in data mining,prediction,and optimization.Scholars have used it to forecast stock and real estate investment trust(REIT)returns in some coun-tries and region,but not in the United States(US)REIT market.The primary goal of this study is to predict the US REIT market using GMDH and then compare its accuracy with that derived from the traditional prediction method.Design/methodology/approach:To forecast the return on the US REIT index,this study used the GMDH neural network and the generalized autoregressive conditional heteroscedasticity(GARCH)model.In this test,the training samples,testing samples,and kernel functions of the GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach.Corresponding experiments were performed using the GARCH model,and the accuracies of these two approaches were compared.Findings:Compared with GARCH,GMDH’s accuracy is much higher,indicating that the machine learning approach can provide a highly accurate prediction of REIT prices.The size of the training samples and the kernel functions in the GMDH model affect the accuracy of the prediction results.In particular,the kernel function has a signifi-cant impact on prediction accuracy.The linear and linear covariance kernel functions are simple to train and yield accurate predictions,whereas the quadratic function is difficult to train.Even with small training samples,GMDH can outperform GARCH in prediction accuracy.Research limitations/implications:Although GMDH shows good performance in predicting the US REIT return,it is still a black-box model,and the algorithm is difficult for financial analysts to develop and customize.The data used in this study come from the US REIT market,which is the world’s largest and most liquid market.Social implications:This research shows that the GMDH model outperforms the GARCH model in forecasting REIT returns.Hence,investors can use the machine thus better investment decisions.Future investors and researchers may use GMDH to forecast the performance of REITs in other markets.Originality/value:This is the first study to apply the GMDH neural network to the US REIT market and determine the impact of the two factors on its performance.For example,this research first discusses the impact of kernel functions on the US REIT market using the GMDH neural network.It also includes short-term daily prediction returns that were not previously considered,making it a valuable reference for financial industry analysts.

CdS-modified one-dimensional g-C_3N_4 porous nanotubes for efficient visible-light photocatalytic conversion

A heterojunction photocatalyst based on porous tubular g-C3N4 decorated with CdS nanoparticles was fabricated by a facile hydrothermal co-deposition method.The one-dimensional porous structure of g-C3N4 provides a higher specific surface area,enhanced light absorption,and better separation and transport performance of charge carriers along the longitudinal direction,all of which synergistically contribute to the superior photocatalytic activity observed.The significantly enhanced catalytic efficiency is also a benefit originating from the fast transfer of photogenerated electrons and holes between g-C3N4 and CdS through a built-in electric field,which was confirmed by investigating the morphology,structure,optical properties,electrochemical properties,and photocatalytic activities.Photocatalytic degradation of rhodamine B(RhB)and photocatalytic hydrogen evolution reaction were also carried out to investigate its photocatalytic performance.RhB can be degraded completely within 60 min,and the optimum H2 evolution rate of tubular g-C3N4/CdS composite is as high as 71.6μmol h^–1,which is about 16.3 times higher than that of pure bulk g-C3N4.The as-prepared nanostructure would be suitable for treating environmental pollutants as well as for water splitting.

NiFe-based nanostructures on nickel foam as highly efficiently electrocatalysts for oxygen and hydrogen evolution reactions

Water splitting,as an advanced energy conversion technology,consists of two half reactions,including oxygen evolution reaction(OER)and hydrogen evolution reaction(HER).However,the ideal electrocatalysts are noble metal based catalysts.Their high cost and scarcity in earth seriously restrict the large deployments.Ni Fe-based materials have attracted great attention in recent years due to their excellent catalytic properties for OER and HER.Nevertheless,their conductivity and electrochemical stability at high current density are unsatisfactory,resulting in ineffective water splitting due to high impedance and low stability.Recently,a series of catalysts coating Ni Fe-based materials on 3 D nickel foam were found to be extremely stable under the circumstance of high current density.In this review,we summarized the recent advances of NiFe-based materials on nickel foam for OER and HER,respectively,and further provided the perspectives for their future development.

Oxygen Transfer and Hydrodynamics in a Flexible Fibre Biofilm Reactor for Wastewater Treatment

A flexible fibre biofilm reactor was developed for treatment of organic wastewaters.The hydrodynamic characteristics and mass transfer coefficients of oxygen were studied and compared with those of the conventional activated sludge processes.Tracer experiments were performed to obtain the residence time distributions of the reactors.The results indicated that both reactors could be treated as mixed flow reactors.The effects of flow rates of water and air on the overall mass transfer coefficient of oxygen were investigated,and the correlations between the mass transfer coefficient and the ratio of flow rates were obtained.Compared to the conventional activated sludge reactor,the mass transfer coefficients in the flexible fibre reactor were similar to but slightly lower,and less sensitive to the variation in the ratio of flow rates.It indicated that the fibre packing in the reactor hindered the oxygen transfer to some extent.

Platinum stabilized by defective activated carbon with excellent oxygen reduction performance in alkaline media

The exploration of highly active and durable cathodic oxygen reduction reaction(ORR)catalysts with economical production cost is still the bottleneck to realize the large‐scale commercialization of fuel cells and metal‐air batteries.Given that carbon support is crucial to the electrocatalysts,and Pt is the best‐known ORR catalyst so far,in this work,we employed a simple impregnation method for synthesizing a kind of defective activated carbon(D‐AC)supported low Pt content electrocatalysts for the ORR.The reduction conditions of the Pt‐containing precursor were firstly optimized,and the influence of the Pt loading amount on the ORR was investigated as well.The results show that the obtained D‐AC@5.0%Pt sample(contains5wt%Pt)has surpassed the commercial Pt/C with20wt%Pt for the ORR in an alkaline solution.In the meantime,it is more stable than the commercial Pt/C.The outstanding ORR performance of the D‐AC@5.0%Pt confirms that both the unique defects in the D‐AC and the introduced Pt particles are indispensable to the ORR.Particularly,m the ORR activity of the synthesized catalysts is superior to most of the reported counterparts,but with much easier preparation methods and lower production cost,making them more advantageous in practical fuel cell applications.

Sampling Size Required for Determining Soil Carbon and Nitrogen Properties at Early Establishment of Second Rotation Hoop Pine Plantations in Subtropical Australia

Investigations into forest soils face the problem of the high level of spatial variability that is an inherent property of all forest soils.In order to investigate the effect of changes in residue management practices on soil properties in hoop pine(Araucaria cunninghamii Aiton ex A.Cunn.)plantations of subtropical Australia it was important to understand the intensity of sampling effort required to overcome the spatial variability induced by those changes.Harvest residues were formed into windrows to prevent nitrogen(N)losses through volatilisation and erosion that had previously occurred as a result of pile and burn operations.We selected second rotation(2R)hoop pine sites where the windrows(10-15 m apart)had been formed 1,2 and 3 years prior to sampling in order to examine the spatial variability in soil carbon(C) and N and in potential mineralisable N(PMN)in the areas beneath and between(inter-)the windrows.We examined the implications of soil variability on the number of samples required to detect differences in means for specific soil properties, at different ages and at specified levels of accuracy.Sample size needed to accurately reflect differences between means was not affected by the position where the samples were taken relative to the windrows but differed according to the parameter to be sampled.The relative soil sampling size required for detecting differences between means of a soil property in the inter-windrow and beneath-windrow positions was highly dependent on the soil property assessed and the acceptable relative sampling error.An alternative strategy for soil sampling should be considered,if the estimated sample size exceeds 50 replications.The possible solution to this problem is collection of composite soil samples allowing a substantial reduction in the number of samples required for chemical analysis without loss in the precision of the mean estimates for a particular soil property.

Araucaria cunninghamii Seedling Response to Different Forms and Rates of ^(15)N-Labelled Fertiliser

Nitrogenous fertilisers are under consideration for promoting the growth of nursery-reared hoop pine （Araucaria cunninghamii Alton ex A. Cunn） seedlings in the establishment phase of second rotation （2R） plantations. Using ^15N- labelled fertilisers, we investigated the effect of different forms （ammonium sulphate, ammonium nitrate, potassium nitrate and urea） and rates of application （0, 150 and 300 mg N kg^-1 dried soil） of fertilisers on the growth, ^15N recovery and carbon isotope composition （δ^13C） of hoop pine seedlings in a 12-month glasshouse trial in southeast Queensland, Australia. The ^15N-labelled fertilisers were applied to nursery-reared hoop pine seedlings, which were then grown in pots, containing ca. 1.2 kg dried soil, under well watered conditions for 12 months. Four seedlings from each treatment were harvested at 4-month intervals, divided into roots, stem and foliage, with a further subdivision for new and old foliage, and then analysed for ^15N, total N, δ^13C and total C. There was no significant response in the seedling growth to the form or rate of application of nitrogen （N） fertiliser within the 12-month period, indicating that the seedlings did not experience N deficiency when grown on second rotation hoop pine soils. While the combined ^15N recovery from soil and plant remained at around 70% throughout the experiment, the proportion of ^15N recovered from the plants increasing steadily over time. Nitrate containing fertilisers at 150 mg N kg^-1 soil gradually increased seedling foliage δ^13C over the 12-month period, indicating an increase in seedling water use efficiency.

A magnetic field strategy to porous Pt-Ni nanoparticles with predominant (111) facets for enhanced electrocatalytic oxygen reduction

For the first time, we developed porous Pt-Ni alloying nanoparticles with predominant(111) facets under intense magnetic fields. Electrochemical analysis revealed that the Pt-Ni alloying nanoparticles obtained at 2 Tesla exhibited a superior catalytic activity and durability for oxygen reduction reaction. This work demonstrated that the imposition of intense magnetic field could be considered as a new approach for developing efficient alloying electrocatalysts with preferential facets.

Adsorption models for heavy metal biosorption

Heavy metal biosorption is an effective process for the removal and recovery of heavy metal ions.Equilibrium isotherms obtained experimentally are usually correlated empirically with commonly used adsorption models, without considering the underlying mechanisms of biosorption.Commonly used models for correlating biosorption isotherm data are briefly reviewed and the use of the adsorption models in correlating the desorption processes is analysed.A set of biosorption/desorption experiments for a marine alga derived biosorbent are carried out to test the use of the adsorption models in the desorption process.Experimental data indicate that the amount of the heavy metal ions desorbed from the biomass could not be calculated with the adsorption models.This suggests that the empirical use of adsorption models in the correlation may not be valid when the reversibility of the biosorption equlibrium in the desorption process needs to be considered.Therefore,mechanism based biosorption models are needed for better correlation of equilibrium isotherm data.

Controllable synthesis of Fe–N4 species for acidic oxygen reduction

Controllable design and synthesis of catalysts with the target active sites are extremely important for their applications such as for the oxygen reduction reaction(ORR)in fuel cells.However,the controllably synthesizing electrocatalysts with a single type of active site still remains a grand challenge.In this study,we developed a facile and scalable method for fabricating highly efficient ORR electrocatalysts with sole atomic Fe-N4 species as the active site.Herein,the use of cost-effective highly porous carbon as the support not only could avoid the aggregation of the atomic Fe species but also a feasible approach to reduce the catalyst cost.The obtained atomic Fe-N4 in activated carbon(aFe@AC)shows excellent ORR activity.Its half-wave potential is 59 mV more negative but 47 mV more positive than that of the commercial Pt/C in acidic and alkaline electrolytes,respectively.The full cell performance test results show that the aFe@AC sample is a promising candidate for direct methanol fuel cells.This study provides a general method to prepare catalysts with a certain type of active site and definite numbers.

RFID Adaption in Healthcare Organizations:An Integrative Framework

Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.

Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magnetic resonance imaging(MRI)is the leading modality used for the diagnosis of AD.Deep learning based approaches have produced impressive results in this domain.The early diagnosis of AD depends on the efficient use of classification approach.To address this issue,this study proposes a system using two convolutional neural networks(CNN)based approaches for an early diagnosis of AD automatically.In the proposed system,we use segmented MRI scans.Input data samples of three classes include 110 normal control(NC),110 mild cognitive impairment(MCI)and 105 AD subjects are used in this paper.The data is acquired from the ADNI database and gray matter(GM)images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models.The proposed approaches segregate among NC,MCI,and AD.While testing both methods applied on the segmented data samples,the highest performance results of the classification in terms of accuracy on NC vs.AD are 95.33%and 89.87%,respectively.The proposed methods distinguish between NC vs.MCI and MCI vs.AD patients with a classification accuracy of 90.74%and 86.69%.The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.

Ag nanoparticles anchored organic/inorganic Z‐scheme 3DOMM‐TiO_(2‒x)‐based heterojunction for efficient photocatalytic and photoelectrochemical water splitting

Narrow spectral response,low charge separation efficiency and slow water oxidation kinetics of TiO_(2)limit its application in photoelectrochemical and photocatalytic water splitting.Herein,a promising organic/inorganic composite catalyst Ag/PANI/3DOMM‐TiO_(2–x)with a three‐dimensional ordered macro‐and meso‐porous(3DO MM)structure,oxygen vacancy and Ti^(3+)defects,heterojunction formation and noble metal Ag was designed based on the Z‐scheme mechanism and successfully prepared.The Ag/PANI/3DOMM‐TiO_(2–x)ternary catalyst exhibited enhanced hydrogen production activity in both photocatalytic and photoelectrochemical water splitting.The photocatalytic hydrogen production rate is 420.90μmol g^(–1)h^(–1),which are 19.80 times and 2.06 times higher than the commercial P25 and 3DOMM‐TiO_(2),respectively.In the photoelectrochemical tests,the Ag/PANI/3DOMM‐TiO_(2–x)photoelectrode shows enhanced separation and transfer of carriers with a high current density of 1.55 mA cm^(–2)at equilibrium potential of 1.23 V under simulated AM 1.5 G illumination,which is approximately 5 times greater than the 3DOMM‐TiO_(2).The present work has demonstrated the promising potential of organic/inorganic Z‐scheme photocatalyst in driving water splitting for hydrogen production.

Smart COVID-3D-SCNN: A Novel Method to Classify X-ray Images of COVID-19

The outbreak of the novel coronavirus has spread worldwide,and millions of people are being infected.Image or detection classification is one of the first application areas of deep learning,which has a significant contribution to medical image analysis.In classification detection,one or more images(detection)are usually used as input,and diagnostic variables(such as whether there is a disease)are used as output.The novel coronavirus has spread across the world,infecting millions of people.Early-stage detection of critical cases of COVID-19 is essential.X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early.For extracting the discriminative features through these modalities,deep convolutional neural networks(CNNs)are used.A siamese convolutional neural network model(COVID-3D-SCNN)is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans.To extract the useful features,we used three consecutive models working in parallel in the proposed approach.We acquired 575 COVID-19,1200 non-COVID,and 1400 pneumonia images,which are publicly available.In our framework,augmentation is used to enlarge the dataset.The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%,specificity 95.55%,and sensitivity 96.62%over(COVID-19 vs.non-COVID19 vs.Pneumonia).

Case Study: Finding Better Solutions for Municipal Solid Waste Management in a Semi Local Authority in Sri Lanka

This case study investigates better and sustainable waste management for a given area in Sri Lanka. A questionnaire and field surveys were performed in a small local authority adjacent to Colombo, the capital city. Composting for organic waste and incineration for non-compostable waste were found to be important treatment methods for solid-waste management. The reduction of solid waste is a critical process for sustainable management. Currently, people in the area do not have much interest in waste recycling to decrease the cost of solid-waste management. It was therefore concluded that raising people’s awareness would play an important role in the reduction of solid waste. A suitable waste-management plan needs to be made for each community and area. The situation and conditions of every area is different, therefore each community needs to make an effort to find its own better and sustainable solid-waste management process.

Digital earth:yesterday,today,and tomorrow

The concept of Digital Earth(DE)was formalized by Al Gore in 1998.At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary.Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society.This creates new opportunities and challenges for the realization of DE.‘What is DE today?’,‘What could DE be in the future?’,and‘What is needed to make DE a reality?’.To answer these questions it is necessary to examine DE considering all the technological,scientific,social,and economic aspects,but also bearing in mind the principles that inspired its formulation.By understanding the lessons learned from the past,it becomes possible to identify the remaining scientific and technological challenges,and the actions needed to achieve the ultimate goal of a‘Digital Earth for all’.This article reviews the evolution of the DE vision and its multiple definitions,illustrates what has been achieved so far,explains the impact of digital transformation,illustrates the new vision,and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.

Chemically derived graphene quantum dots for high-strain sensing

Graphene quantum dots(GQDs)refer to graphene fragments with a lateral dimension typically less than 100 nm,which possess unique electrical and optical properties due to the quantum confinement effect.In this study,we demonstrate that chemically derived graphene quantum dots show great potential for making highly stretchable and cost-effective strain sensors via an electron tunneling mechanism.Stretch-able strain sensors are critical devices for the field of flexible or wearable electronics which are expected to maintain function up to high strain values(>30%).However,strain sensors based on conventional materials(i.e.metal or semiconductors)or metal nanoparticles(e.g.gold or silver nanoparticles)only work within a small range of strain(i.e.the former have a working range<1%and the latter<3%).In this study,by simply dropcasting solution-processed GQDs between the interdigitated electrodes on polydimethylsiloxane,we obtained devices that can function in the range from 0.06%to over 50%ten-sile strain with both the sensitivity and working range conveniently adjustable by the concentration of GQDs applied.This study provides a new concept for practical applications of GQDs,revealing the poten-tial of this material for smart applications such as artificial skin,human-machine interfaces,and health monitoring.

Copper nanowires decorated with TiO_(2−x) from MXene for enhanced electrocatalytic nitrogen oxidation into nitrate under vacuum assistance

The green synthesis of nitrate(NO_(3)^(−))via electrocatalytic nitrogen oxidation reaction(NOR)is a promising strategy for artificial nitrogen fixation,which shows great advantages than traditional nitrate synthesis based on Haber–Bosch and Ostwald processes.But the poor N_(2)absorption,high bond energy of N≡N(941 kJ·mol^(−1)),and competing multi-electron-transfer oxygen evolution reaction(OER)limit the activity and selectivity.Herein,we fabricated MXene-derived irregular TiO_(2)−x nanoparticles anchored Cu nanowires(Cu-NWs)electrode for efficient electrocatalytic nitrogen oxidation,which exhibits a NO_(3)−yield of 62.50μg·h^(−1)·mgcat^(−1)and a Faradaic efficiency(FE)of 22.04%,and a significantly enhanced NO_(3)−yield of 92.63μg·h^(−1)·mgcat^(−1),and a FE of 40.58%under vacuum assistance.The TiO_(2)−x/Cu-NWs electrode also shows excellent reproducibility and stability under optimal experimental conditions.Moreover,a Zn-N_(2)reaction device was assembled with TiO_(2−x)/Cu-NWs as an anode and Zn plate as a cathode,obtaining an extremely high NO_(3)−yield of 156.25μg·h^(−1)·mgcat^(−1).The Zn-nitrate battery shows an open circuit voltage(OCV)of 1.35 V.This work provides novel strategies for enhancing the performance of ambient N_(2)oxidation to obtain higher NO_(3)^(−)yield.

Biochar reduces colloidal phosphorus in leachate by regulating phoD- and phoC-harboring microbial communities during drying/rewetting cycles

Drying and rewetting(DRW)events cause the release of colloidal phosphorus(P_(coll),1-1000 nm)in leachate,and biochar is considered an effective inhibitor;however,the microbial mechanism remains elusive.In this study,three successive DRW cycles were performed on the soil columns to assess the effect of biochar addition on P_(coll) content and its possible associates,including phosphatase-producing microbial populations(phoD- and phoC-harboring microbial communities)and alkaline/acid phosphatase(ALP/ACP)activities.Results showed that the biochar addition significantly decreased the P_(coll) by 15.5-32.1%during three DRW cycles.The structural equation model(SEM)confirmed that biochar addition increased phoD- and phoC-harboring microbial communities and ALP/ACP activities,which reduces the release of P_(coll) into leachate.In addition,the manure biochar was more effective than the straw biochar in promoting competition and cooperation in the co-occurrence network(2-5%nodes increased on average),and the key taxa Proteobacteria and Cyanobacteria were identified as the dominant species of potential ALP/ACP activities and P_(coll) content.Our findings provide a novel understanding of biochar reducing P_(coll) loss from the phosphatase perspective by regulating the phoD-and phoC-harboring communities during DRW events.