Flower-like Ag-decked non-stoichiometric Bi2O3-x/rGO hybrid nanocomposite SERS substrates for an effective detection of Rhodamine 6G dye molecules

In early years, SERS-active substrates were generally noble metals. However, their practical applications were limited due to their poor biocompatibility, low uniformity and high cost. Recently, the utilization of semiconductor SERS-active substrates has greatly expanded the applications of SERS in many fields. However, metal-free SERS-active substrates have a low enhancement factor (EF), which can be overcome by adjusting their oxygen deficiency or through the effective preparation of non-stoichiometric semiconducting oxide materials. This is the key strategy and may work as an efficient and simple way to achieve high sensitivity and obtain an enhancement factor (G-factor) comparable to that of noble metals. Here, we report the preparation of flower-like rGO-Bi2O3/Bi2O2.75 and rGO-Ag-Bi2O3/Bi2O2.75 hybrid thin film nanocomposites using a liquid/liquid interface method (LLI) for the first time. In addition to the synergic effect of different enhancement mechanisms, the 3-D flower-like morphology of the substrate shows more favourable properties to improve the G-factor due to the existence of more hotspots. The rGO-Ag-Bi2O3/Bi2O2.75 hybrid thin-film nanocomposites show an EF of 1.8 × 109 with a detection ability of up to 1 nM towards Rhodamine 6G (R6G), which is highly toxic to humans and the aquatic environment.

An Electro-Oculogram (EOG) Sensor's Ability to Detect Driver Hypovigilance Using Machine Learning

Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver's physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), and surface electromyography (sEMG) signals are used to monitor a driver's physical state during a drive. The purpose of this study was to detect driver hypovigilance (drowsiness, fatigue, as well as visual and cognitive inattention) using signals collected from 10 drivers while they were driving. EOG signals from the driver were preprocessed to remove noise, and 17 features were extracted. ANOVA (analysis of variance) was used to select statistically significant features that were then loaded into a machine learning algorithm. We then reduced the features by using principal component analysis (PCA) and trained three classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and ensemble. A maximum accuracy of 98.7% was obtained for the classification of normal and cognitive classes under the category of two-class detection. Upon considering hypovigilance states as five-class, a maximum accuracy of 90.9% was achieved. In this case, the number of detection classes increased, resulting in a reduction in the accuracy of detecting more driver states. However, with the possibility of incorrect identification and the presence of issues, the ensemble classifier's performance produced an enhanced accuracy when compared to others.

Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models

Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing at an alarming rate every year. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Open Access Series of Imaging Studies (OASIS) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML models. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with these ML algorithms. The proposed work shows better results with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.

A Recommendation System Based on AI for Storing Block Data in the Electronic Health Repository

The proliferation of wearable sensors that record physiological signals has resulted in an exponential growth of data on digital health. To select the appropriate repository for the increasing amount of collected data, intelligent procedures are becoming increasingly necessary. However, allocating storage space is a nuanced process. Generally, patients have some input in choosing which repository to use, although they are not always responsible for this decision. Patients are likely to have idiosyncratic storage preferences based on their unique circumstances. The purpose of the current study is to develop a new predictive model of health data storage to meet the needs of patients while ensuring rapid storage decisions, even when data is streaming from wearable devices. To create the machine learning classifier, we used a training set synthesized from small samples of experts who exhibited correlations between health data and storage features. The results confirm the validity of the machine learning methodology.

Transition to Electric Mobility in India: Barriers Exploration and Pathways to Powertrain Shift through MCDM Approach

The ever-increasing environmental concern necessitates the implementation of electric vehicles (EVs) in the public fleet in a larger proportion. While developed countries have accomplished this objective through unique ways, developing and underdeveloped countries are still lagging. Deficiencies in technological readiness and supportive infrastructure are major concerns for them. To support their future endeavours in the field of electric vehicles, a review article that encompasses the present status, barriers for EVs adoption and operation in developing countries proves highly critical. A critical analysis of EV adoption barrier information is used to develop appropriate technology to tackle these barriers and provide necessary plans for policymakers to effectively implement electric vehicles. Furthermore, this article comprehensively analyses barriers to EV implementation and adopts the multi-criteria decision making (MCDM) model to rank the evaluation criteria based on expert opinions. From the results for developing countries such as India, the model placed battery-operated electric vehicles at 5th position on the hierarchy ranking. This is because the existing infrastructure and technology will not currently support the imminent shift to electric mobility. The identification of a possible alternative propulsion system that bridges the gap between necessity and feasibility is essential. For this purpose, the article also explores the prospects and critically analyses hybrid electric vehicles as an intermediary propulsion system that smoothens the transition to EV. The outcomes of the literature review, expert opinions and author findings suggest that hybrid technology uses powertrain components that are chiefly similar to those found in conventional vehicles, yielding vehicle manufacturers with a smoother changeover to Electric Vehicle technology, which may otherwise lead to an immense failure in technology transition.

The second and third waves in India: when will the pandemic be culminated?

An unprecedented upsurge of COVID-19-positive cases and deaths is currently being witnessed across India. According to WHO, India reported an average of 3.9 lakhs of new cases during the first week of May 2021 which equals 47% of new cases reported globally and 276 daily cases per million population. In this letter, the concept of SIR and fractal interpolation models is applied to predict the number of positive cases in India by approximating the epidemic curve, where the epidemic curve denotes the two-dimensional graphical representation of COVID-19-positive cases in which the abscissa denotes the time, while the ordinate provides the number of positive cases. In order to estimate the epidemic curve, the fractal interpolation method is implemented on the prescribed data set. In particular, the vertical scaling factors of the fractal function are selected from the SIR model. The proposed fractal and SIR model can also be explored for the assessment and modeling of other epidemics to predict the transmission rate. This letter investigates the duration of the second and third waves in India, since the positive cases and death cases of COVID-19 in India have been highly increasing for the past few weeks, and India is in a midst of a catastrophizing second wave. The nation is recording more than 120 million cases of COVID-19, but pandemics are still concentrated in most states. In order to predict the forthcoming trend of the outbreaks, this study implements the SIR and fractal models on daily positive cases of COVID-19 in India and its provinces, namely Delhi, Karnataka, Tamil Nadu, Kerala and Maharashtra.

Green synthesis of ZnO nanoparticles for antimicrobial and vegetative growth applications: A novel approach for advancing efficient high quality health care to human wellbeing

The present work aims to synthesize zinc oxide (ZnO) nanoparticles via green approaches using leaf extract of Parthenium hysterophorus. UV-vis and FT-IR tests confirmed the existence of biomolecules, active materials, and metal oxides. The X-ray diffraction structural study exposes the ZnO nanoparticles formation with hexagonal phase structures. SEM and TEM analysis reveal surface morphologies of ZnO nanoparticles and most of them are spherical with a size range of 10 nm. ZnO nanoparticles were revealed strong antimicrobial activity against both bacterial and fungal strains. The germination of seeds and vegetative growth of Sesamum indicum has been greatly improved.

Low-cost electrochemical detection of l-tyrosine using an rGO-Cu modified pencil graphite electrode and its surface orientation on a Ag electrode using an ex situ spectroelectrochemical method

A low cost reduced graphene oxide–copper hybrid nano thin-film modified Pencil Graphite Electrode has been employed to detect the l-tyrosine enantiomer. The free-standing rGO–Cu hybrid nano-thin film was prepared by a simple one-step liquid–liquid interface method. Electrochemical Cyclic Voltammetry, Differential Pulse Voltammetry, pH-dependent and scan rate dependent studies on bare PGE, Cu, rGO, and rGO–Cu for l-tyrosine have been explained in detail. The rGO–Cu modified PGE based biosensor exhibits good detection of l-tyrosine. The linear range detection limit was estimated to be 1 × 10⁻⁷ M. The calculated sensitivity is 0.4 μA ppm⁻¹ mm². This electroactive biosensor is easily fabricated and controlled and is cost-effective. The surface orientation of l-tyrosine on the Ag electrode at a particular potential and its comparison with vibrational DFT calculations have been studied for the first time.

A low cost reduced graphene oxide–copper hybrid nano thin-film modified pencil graphite electrode has been employed to detect the l-tyrosine enantiomer.

Improved surface-enhanced Raman and catalytic activities of reduced graphene oxide-osmium hybrid nano thin films

Reduced graphene oxide-osmium (rGO-Os) hybrid nano dendtrites have been prepared by simple liquid/liquid interface method for the first time. The method involves the introduction of phase-transfered metal organic precursor in toluene phase and GO dispersion in the aqueous phase along with hydrazine hydrate as the reducing agent. Dendritic networks of Os nanoparticles and their aggregates decorating rGO layers are obtained. The substrate shows improved catalytic and surface-enhanced activities comparable with previous reports. The catalytic activity was tested for the reduction of p-nitroaniline into p-phenyldiamine with an excess amount of NaBH₄. The catalytic activity factors of these hybrid films are 2.3 s^-1 g^-1 (Os film) and 4.4 s^-1 g^-1 (rGO-Os hybrid film), which are comparable with other noble metal nanoparticles such as Au, Ag, but lower than Pd-based catalysts. Surface-enhanced Raman spectroscopy (SERS) measurements have been done on rhodamine 6G (R6G) and methylene blue dyes. The enhancement factor for the R6G adsorbed on rGO-Os thin film is 1.0 × 10⁵ and for Os thin film is 7 × 10³. There is a 14-fold enhancement observed for Os hybrids with rGO. The enhanced catalytic and SERS activities of rGO-Os hybrid thin film prepared by simple liquid/liquid interface method open up new challenges in electrocatalytic application and SERS-based detection of biomolecules.

Films of Reduced Graphene Oxide with Metal Oxide Nanoparticles Formed at a Liquid/Liquid Interface as Reusable Surface Enhanced Raman Scattering Substrates for Dyes

Free standing, thin films of reduced graphene oxide (rGO) with ZnO, CuO and SnO(2) nanostructures are prepared at a water/toluene interface utilizing simple interfacial reaction and self-assembly. rGO-ZnO, rGO-CuO and rGO-SnO(2) films exhibit unique morphologies such as hexagonal cylinders, elongated splinters, and balls, respectively, wrapped by rGO layers. The hybrid films exhibit surface enhanced Raman scattering (SERS) of rhodamine 6G dye with enhancement factors one order higher than bare metal oxide caused by a synergic effect of charge transfer between the dye, metal oxide and rGO. Doping with Ag+ ions improves SERS enhancement in rGO-Ag–ZnO hybrid films, exploiting the electromagnetic effect of metal surface plasmons. Detection sensitivity up to 10 μM dye with an enhancement factor of 104 is shown. The enhanced photodegradation rate by the hybrid films is utilized for UV induced regeneration of the used SERS substrate and is demonstrated for successive use of different analytes.

Electrochemical Sensing, Photocatalytic and Biological Activities of ZnO Nanoparticles: Synthesis via Green Chemistry Route

In this paper, we have successfully synthesized ZnO nanoparticles (Nps) via solution combustion method using sugarcane juice as the novel fuel. The structure and morphology of the synthesized ZnO Nps have been analyzed using various analytical tools. The synthesized ZnO Nps exhibit excellent photocatalytic activity for the degradation of methylene blue dye, indicating that the ZnO Nps are potential photocatalytic semiconductor materials. The synthesized ZnO Nps also show good electrochemical sensing of dopamine. ZnO Nps exhibit significant bactericidal activity against Klebsiella aerogenes, Pseudomonas aeruginosa, Eschesichia coli and Staphylococcus aureus using agar well diffusion method. Furthermore, the ZnO Nps show good antioxidant activity by potentially scavenging 1-diphenyl-2-picrylhydrazyl (DPPH) radicals. The above studies clearly demonstrate versatile applications of ZnO synthesized by simple eco-friendly route.

Antibacterial and Photocatalytic Activities of ZnO Nanoparticles: Synthesized Using Water Melon Juice as Fuel

In the present work, Zinc Oxide nanoparticles (ZnO Nps) have been prepared by a simple and low temperature solution combustion method using Zinc nitrate as a precursor and solid water melon juice as a novel fuel for the first time. The structure and morphology of the synthesized ZnO NPs have been analyzed using various analytical techniques such as Powder X-ray diffraction, FTIR spectroscopy, Raman spectroscopy, UV-Visible spectroscopy, photoluminescence spectroscopy, scanning electron microscope and transmission electron microscope. ZnO NPs show good photo catalytic activity for the degradation of methylene blue (MB) dye. It also shows significant antibacterial activities against three bacterial strains.

Comparative high pressure Raman studies on perfluorohexane and perfluoroheptane

High pressure Raman spectroscopic studies on perfluorohexane and perfluoroheptane have performed up to 12 GPa. Perfluorohexane under goes two pressure induced transitions: (1) liquid-solid transition at 1.6 GPa and (2) solid-solid transition at 8.2 GPa. On the contrary, perfluoroheptane under goes three phase transitions, they are as follows: (1) liquid-solid transition at 1.3 GPa, (2) intermediate solid I transition at 3 GPa, (3) solid II transition at 7 GPa. The change in slope (dω/dP) shows that the solid I transition at 3.0 GPa could be the conversion of mid-gauche defect into trans conformers for perfluoroheptane. The pressure induced Raman spectra and the behavior of individual band with pressure shows that the solid phase comprises more than one conformer beyond crystallization. The intensity ratio for both the compounds shows that the high pressure phase beyond 8.2 and 7.0 GPa tends to have close packing with distorted all-trans conformers.

Effect of gamma irradiation on antioxidant properties of ber (Zizyphus mauritiana) fruit

Effect of gamma irradiation (0.25 to 1.0kGy) on antioxidant properties of ber fruit was studied. Antioxidant properties of ber fruits were determined by Scavenging DPPH radical activity, reducing power assay, super oxide anion radical activity, TBARS, total phenolic content and total flavonoid content. Gamma irradiation treatment up to 1.0kGy elevated the Scavenging DPPH radical activity (9 %), super oxide anion radical activity (26 %) and total flavonoid content (208 %) compared to fresh ber fruit. On the other hand it brought down the reducing power activity (65 %) and total phenolic content (18 %) as compared to raw fruit. The TBARS activity statistically increased upon irradiation of ber fruit. It indicated that total antioxidant activity decreased as TBARS value increased. Therefore 0.25 to 0.5kGy is better dose to retain the natural antioxidant in fruit.

Investigation of the effect of liquid–liquid phase separation (LLPS) on nucleation and different growth stages of vanillin and bulk growth of defect-free single crystals from aqueous solution – a new approach

In the present work, the advent of secondary liquid phase and its impact on various growth stages of vanillin single crystals in pure aqueous solution were investigated.