Zn-doped nickel iron(oxy)hydroxide nanocubes passivated by polyanions with high catalytic activity and corrosion resistance for seawater oxidation

Electrochemical water splitting to produce hydrogen fuel is a promising renewable energy-conversion technique.Large-scale electrolysis of freshwater may deplete water resources and cause water scarcity worldwide.Thus,seawater electrolysis is a potential solution to the future energy and water crisis.In seawater electrolysis,it is critical to develop cost-effective electrocatalysts to split seawater without chloride corrosion.Herein,we present zinc-doped nickel iron(oxy)hydroxide nanocubes passivated by negatively charged polyanions(NFZ-PBA-S)that exhibits outstanding catalytic activity,stability,and selectivity for seawater oxidation.Zn dopants and polyanion-rich passivated surface layers in NFZ-PBA-S could effectively repel chlorine ions and enhance corrosion resistance,enabling its excellent catalytic activity and stability for seawater oxidation.

Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

Investigation of the sodium storage mechanism of iron fluoride hydrate cathodes using X-ray absorption spectroscopy and mossbauer spectroscopy

Elucidation of a reaction mechanism is the most critical aspect for designing electrodes for highperformance secondary batteries.Herein,we investigate the sodium insertion/extraction into an iron fluoride hydrate(FeF_(3)·0.5H_(2)O)electrode for sodium-ion batteries(SIBs).The electrode material is prepared by employing an ionic liquid 1-butyl-3-methylimidazolium-tetrafluoroborate,which serves as a reaction medium and precursor for F^(-)ions.The crystal structure of FeF_(3)·0.5H_(2)O is observed as pyrochlore type with large open 3-D tunnels and a unit cell volume of 1129A^(3).The morphology of FeF_(3)·0.5H_(2)O is spherical shape with a mesoporous structure.The microstructure analysis reveals primary particle size of around 10 nm.The FeF_(3)·0.5H_(2)O cathode exhibits stable discharge capacities of 158,210,and 284 mA h g^(-1) in three different potential ranges of 1.5-4.5,1.2-4.5,and 1.0-4.5 V,respectively at 0.05 C rate.The specific capacities remained stable in over 50 cycles in all three potential ranges,while the rate capability was best in the potential range of 1.5-4.5 V.The electrochemical sodium storage mechanism is studied using X-ray absorption spectroscopy,indicating higher conversion at a more discharged state.Ex-situ M?ssbauer spectroscopy strengthens the results for reversible reduction/oxidation of Fe.These results will be favorable to establish high-performance cathode materials with selective voltage window for SIBs.

Effect of Co substitution on the structural,electrical,and magnetic properties of Bi_(0.9)La_(0.1)FeO_3 by sol-gel synthesis

Cobalt （Co）-doped Bi0.9La0.lFeO3 multiferroics were synthesized by a sol-gel method based on the auto- combustion technique. As-synthesized powder was examined using various characterization techniques to explore the effect of Co substitution on the properties of Bi0.9La0.1FeO3. X-ray diffraction reveals that Co-doped Bi0.9La0.1FeO3 preserves the perovskite-type rhombohedral structure of BiFeO3, and the composition without Co preserves the original structure of the phase; however, a second-phase Bi2Fe409 has been identified in all other compositions. Surface morphological studies were performed by scanning electron microscopy. Temperature-dependent resistivity of the samples reveals the characteristic insulating behavior of the multiferroic material. The resistivity is found to decrease with the increase both in temperature and Co content. Room temperature frequency-dependent dielectric measurements were also reported. Magnetic measurements show the enhancement in magnetization with the increase in Co content.