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Emotional intelligence, job satisfaction and psychological well-being among nurses in a tertiary care hospital
Background: Emotional intelligence helps in preservation of mental health because of their effective emotional regulation skills. Objectives: We aimed to evaluate the impact of emotional intelligence on nurses job satisfaction and psychological well-being. Methods: This cross-sectional study was conducted in a tertiary hospital and included 120 nurses. Wong and Law Emotional Intelligence Scale, Psychological General Well-being scale and Job Satisfaction Survey questionnaires were used. Results: The study showed a low positive correlation between emotional intelligence and psychological wellbeing (r=0.313) and a low correlation between emotional intelligence and job satisfaction (r= 0.122). The emotional intelligence was significantly correlated to their psychological well-being (9.8%). Conclusion: Nurses with higher emotional intelligence experience greater psychological well-being. We did not find a link between emotional intelligence and job satisfaction. Implementing interventions to enhance emotional intelligence in nurses is crucial for improving psychological well-being and reducing burnout risk. The Author(s). 2024. -
NSS-ML: a Novel spectrum sensing framework using machine learning for cognitive radio IoT networks
A key component of cognitive radio systems is spectrum sensing, which reduces coexistence problems and maximises spectrum efficiency. However, the introduction of multiple situations with distinct characteristics brought about by 5G communication presents problems for spectrum sensing to support a wide range of applications with high performance and flexible implementation. Inspired by these difficulties, a new method with a multi-layer extreme learning machine optimised for bats is presented in this study. This technique makes use of a variety of input vectors, such as channel ID, energy, distance, and received signal intensity, to enhance user categorization and sensing capabilities. Moreover, we compare the proposed method with the state-of-the-art spectrum sensing approaches in order to evaluate its effectiveness in 5G situations, especially in healthcare applications. Evaluation metrics including channel detection probability, sensitivity, and selectivity are carefully examined. The findings unequivocally prove the suggested spectrum sensing approachs superiority over current methods and highlight its potential for smooth incorporation into a variety of 5G applications. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Impact of Learnability Quotient on Employability of Students: Mediating Role of Spiritual Intelligence
This study investigates the impact of Learnability Quotient (LQ) on Employability, with a particular focus on the mediating role of Spiritual Intelligence (SI). Conducted in southern India with a cross-sectional design, the research utilizes primary data collected from educated adults through surveys. The study aims to elucidate cause-and-effect relationships between LQ and Employability and to test hypotheses regarding these variables. The findings reveal that Learnability Quotient and Employability significantly influence each other, with both being affected by age. Education also plays a crucial role in determining employability, while Spiritual Intelligence and Learnability Quotient are less influenced by educational level. The type of institution does not significantly affect these factors, although the location of the institution does impact Spiritual Intelligence and Employability. Correlation analysis shows that higher Spiritual Intelligence correlates moderately with both Learnability Quotient and Employability, while Learnability Quotient has a strong positive association with Employability. Mediation analysis uncovers a complex dynamic where, despite the positive direct effect of Learnability Quotient on Employability, its impact is diminished when mediated through Spiritual Intelligence, as indicated by a negative Variance Accounted For (VAF). Learnability Quotient is crucial for enhancing employability, while Spiritual Intelligence has a nuanced, potentially counterproductive mediation role. Further research is necessary to refine strategies for improving employability through these variables. 2024, Iquz Galaxy Publisher. All rights reserved. -
Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection
Modern technology like Artificial Intelligence (AI) must be used in the agricultural sectorif sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases & pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model
In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models. 2024 River Publishers. -
Detection of a new sample of Galactic white dwarfs in the direction of the Small Magellanic Cloud
Aims. In this study, we demonstrate the efficacy of the Ultraviolet Imaging Telescope (UVIT) in identifying and characterizing white dwarfs (WDs) within the Milky Way Galaxy. Methods. Leveraging the UVIT point-source catalogue towards the Small Magellanic Cloud and cross-matching it with Gaia DR3 data, we identified 43 single WDs (37 new detections), 13 new WD+main-sequence candidates, and 161 UV bright main-sequence stars by analysing their spectral energy distributions. Using the WD evolutionary models, we determined the masses, effective temperatures, and cooling ages of these identified WDs. Results. The masses of these WDs range from 0.2 to 1.3 M? and the effective temperatures (Teff) lie between 10 000 K to 15 000 K, with cooling ages spanning 0.1-2 Gyr. Notably, we detect WDs that are hotter than reported in the literature, which we attribute to the sensitivity of UVIT. Furthermore, we report the detection of 20 new extremely low-mass candidates from our analysis. Future spectroscopic studies of the extremely low-mass candidates will help us understand the formation scenarios of these exotic objects. Despite limitations in Gaia DR3 distance measurements for optically faint WDs, we provide a crude estimate of the WD space density within 1kpc of 1.3 10-3 pc-3, which is higher than previous estimates in the literature. Conclusions. Our results underscore the instrumental capabilities of UVIT and anticipate forthcoming UV missions such as INSIST for systematic WD discovery. Our method sets a precedent for future analyses in other UVIT fields to find more WDs and perform spectroscopic studies to verify their candidacy. The Authors 2024. -
A ratiometric luminescence thermometer based on lanthanide encapsulated complexes
Lanthanide-containing complexes have been widely developed as ratiometric luminescence thermometers, which are non-invasive, contactless and accurate. The synthesis of these Ln complexes generally requires high temperatures, multiple steps and other harsh conditions. Moreover, bimetallic lanthanide complexes, which have been reported to be better thermometers, are even more challenging to synthesize. This complexity can be simplified by preparing a host-guest complex of lanthanides. In this work, Tb or both Tb and Eu are encapsulated in an MOF host, making them emissive. The ratio of Tb/Eu was also easily tuned by simply changing their ratio in the solution, resulting in a tunable emission. Accordingly, we were able to synthesise both the emissive Tb complex and Tb/Eu complexes at different ratios using a single host. The complexes were found to be suitable as ratiometric luminescent thermometers in the temperature range of 160-380 K, with reasonably good sensitivity and uncertainty. The thermometer's sensitivity and uncertainty were significantly improved using bimetallic Tb and Eu host-guest complexes. Calculations using the host and Eu emission ratio were found to provide better thermometer parameters than the commonly reported Tb and Eu emission ratio. Thus, using a single host, we were able to synthesise different lanthanide complexes that can sense temperature, and we improved the thermometer parameters by incorporating multiple lanthanides in a single host. This research will enable the scientific community to reexamine the applicability of unexplored host-guest lanthanide complexes. 2025 The Royal Society of Chemistry. -
Effect of Coupled Microstructural Characteristics of Catalyst Layer on High Temperature: Proton Exchange Membrane Fuel Cell Performance
The widespread adoption of High Temperature-Proton Exchange Membrane Fuel Cells (HT-PEMFC) in commercial applications is limited by their performance and durability compared to conventional energy sources. A key factor affecting these cells is the sluggish oxygen reduction reaction (ORR) at the cathode catalyst layer (CL). Optimizing the structural parameters of the cathode CL can enhance cell performance and longevity. Current research on these parameters is mostly descriptive, lacking numerical evidence to quantify their impact. This study develops a three-dimensional, non-isothermal HT-PEMFC numerical model to investigate the sensitivities of coupled structural parameters of the cathode CL, including Pt loading, CL thickness, and Pt particle diameter, at three levels. The orthogonal/Taguchi approach quantitatively assesses the impact of these parameters. The study reveals that Pt loading significantly affects cell voltage and cathode overpotential, while Pt diameter influences the homogeneity of overpotential distribution. The dominant impact of a single parameter decreases at higher current densities, necessitating careful analysis of trade-offs between different structural characteristics to maximize performance. These findings offer valuable insights for future experimental studies to enhance cell performance through adjustments to cathode catalyst characteristics. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
HMOSHSSA: a novel framework for solving simultaneous clustering and feature selection problems
In real-life scenarios, information about the number of clusters is unknown. Due to this, clustering algorithms are unable to generate the valuable partitions. Beside this, the appropriate and optimal number of features is also required to produce the good quality clusters. The selection of optimal number of clusters and feature is a challenging task in the clustering. To resolve these problems, an automatic multi-objective-based clustering approach called HMOSHSSA is proposed in this paper. In HMOSHSSA, the spotted hyena and salp swarm algorithms are hybridized to obtain a better trade-off between these algorithms intensification and diversification capabilities. Two novel concepts for encoding and threshold setting are incorporated in the HMOSHSSA. The encoding scheme is used to choose the optimal number of clusters and features during the optimization process. The variance of dataset is used for setting the threshold values for both clusters and features. A novel fitness function is proposed to improve the optimization process. The suggested algorithms performance is evaluated using eight well-known real-world datasets. The statistical significance of HMOSHSSA is measured through t-tests. Results reveal that the proposed approach is able to detect the optimal number of clusters and features from a given dataset without user intervention. This approach is also deployed for solving microarray data analysis and image segmentation problems. HMOSHSSA outperformed the other considered algorithms in terms of performance measures. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Interacting Dark Energy and Its Implications for Unified Dark Sector
Alternative dark energy models were proposed to address the limitation of the standard concordance model. Though different phenomenological considerations of such models are widely studied, scenarios where they interact with each other remain unexplored. In this context, we study interacting dark energy scenarios (IDEs), incorporating alternative dark energy models. The three models that are considered in this study are time-varying ?, Generalized Chaplygin Gas (GCG), and K-essence. Each model includes an interaction rate ? to quantify energy density transfer between dark energy and matter. Among them, GCG coupled with an interaction term shows promising agreement with the observed TT power spectrum, particularly for ?<70, when ? falls within a specific range. The K-essence model (??0.1) is more sensitive to ? due to its non-canonical kinetic term, while GCG (??1.02) and the time-varying ? (??0.01) models are less sensitive, as they involve different parameterizations. We then derive a general condition when the non-canonical scalar field ? (with a kinetic term Xn) interacts with GCG. This has not been investigated in general form before. We find that current observational constraints on IDEs suggest a unified scalar field with a balanced regime, where it mimics quintessence behavior at n<1 and phantom behavior at n>1. We outline a strong need to consider alternative explanations and fewer parameter dependencies while addressing potential interactions in the dark sector. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
OTT Enchantment: Decoding the Secret of Millennials Subscription Intentions
This study explores the factors that affect intention and choices of millennials for subscription of Over-The-Top (OTT) platforms. The study involved a mixed-methods approach, involving exploratory and descriptive design. The outcome of the study showed that there is a profound impact of demographic variables on the subscription intention. Results also indicated that factors like convenient navigation, information seeking, and bingewatching impacted respondents attitudes towards purchasing OTT subscriptions. Moreover, factors like relaxation and voyeurism impacted respondents attitudes towards continuing OTT subscriptions. The research findings will be helpful for OTT companies to implement new distribution strategies with mobile operators to launch novel services like mobile-only packs and sachet pricing and thereby increase their user base. The study contributes significantly in understanding the viewership and subscription patterns of millennials. The study is exclusively an original contribution of the authors. 2024, Econjournals. All rights reserved. -
Influence of Planting Methods on Growth, Yield and Storage of Onion (Allium cepa L.) var. Bhima Shakti
Background: Successful crop production in any crop depends on the good agronomic practices. Being a commercial crop, onion needs good attention during the crop growth stages, most of the times, in onion cultivation farmers follow broadcasting method which leads to the wastage of seeds, seedling mortality and poor germination and it will affect the total yield and productivity. Methods: To identify the best planting method an investigation on performance of seed drill versus transplanted onion was conducted at University of Agricultural Sciences, Main Agriculture Research Station, Dharwad. Result: Results indicated that, T3- (Transplanting) performed superior for all the vegetative and yield parameters studied. Plant height 55.57 cm, leaf number (7.86, 8.29 and 8.08 in pooled), Leaf width (1.03 cm, 0.99 cm pooled 1.01 cm). Total yield: (21.97 t/ha, 22.05 t/ha and 22.01 t/ha), marketable yield (20.74 t/ha 20.95 t/ha and 20.85 t/ha), bulb weight (64.00 g, 53.00 g and 53.90 g in pooled, analysis). From this, it can be concluded that transplanting of onion was better compared to other methods. (2024), (Agricultural Research Communication Centre). All rights reserved. -
Drivers of Customer Retention: An Introspection Into Indian Retail Customers
There is a wide variety of choices for the modern retail customer including multiple retail formats. The success of the retail establishments has a great reliance of customer retention, which is an essential attribute to achieve profitability. This study takes in to consideration to extract the factors responsible for customer retention which in turn assists in increasing the customer base. The prime objective of the study is to ascertain the influence of customer satisfaction, switching costs and customer loyalty on customer retention. Whereas, the second one is to explore the effect of demographic factors on customer retention. The sample size of this study was 600 respondents who were chosen for the full-fledged study. The statistical techniques used for final analysis were structural equation modelling and regression. The findings subsequent to the statistical analysis and interpretation concluded that customer loyalty, customer satisfaction and switching cost have the strongest effect on customer retention in retails. Customer satisfaction alone is not every time an indicator of customer loyalty. A loyal customer will spread positive word of mouth to other prospective customers about the retail. Occupation of respondent has a major influence on customer retention dimensions. 2021 Management Development Institute. -
Comparative Analysis of Phytochemicals and Antioxidant Potential of Ethanol Leaf Extracts of Psidium guajava and Syzygium jambos
Background: Plant-based drugs for various human ailments are becoming very important in the current domain of therapeutics. Aim: Psidium guajava and Syzygium jambos are two such plant species known for their medicinal properties in traditional systems of medicine like Ayurveda. Methods: Phytochemical analysis including GCMS, and antioxidant studies (DPPH) was carried out for both plant extracts. Results: Comparative phytochemical analyses of ethanol extracts of both these plants have shown the existence of bioactive components like tannins, polyphenols, alkaloids, flavonoids and terpenoids. These phytochemicals were quantified and the ethanol extracts were subjected to GCMS analysis which showed the presence of cis-?-farnesene, cis-calamenene, copaene, humulene, caryophyllene, phytol, neophytadiene, n-hexadecanoic acid etc, many of which possess diverse properties like antimicrobial, antibiofilm, antioxidant and anti-inflammatory. DPPH and reducing power assays revealed the excellent radical scavenging activity of the extracts. Conclusion: Among the two plants under the current study, S. jambos extract showed better results when compared to P. guajava concerning the antioxidant potential and the quantity of flavonoids, alkaloids, polyphenols and tannins present in the plant samples. 2024, Informatics Publishing Limited. All rights reserved. -
Carbon dots-Zno/TiO2 ternary nanocomposite as a proficient material to enhance the performance of natural DSSC
A novel sustainable approach for enhancing the efficiency of dye-sensitized solar cells (DSSCs) involves the utilization of a combination of ZnO and carbon dots (CDs) derived from Citrus medica fruit extract, along with microwave-synthesized TiO2 nanoparticles for the preparation of the photoanode. Natural dyes such as Hibiscus rosa-sinensis and Allium Cepa peel are employed as sensitizers to reduce production costs. This co-activation method has demonstrated a significant improvement in the output parameters of the devices. Notably, the photoanode co-activated with ZnO-CD composite (ZnO-CD/TiO2) exhibits the most favorable output parameters when combined with Hibiscus rosa-sinensis dye (open circuit voltage (Voc) = 0.80 V, short circuit current density (Jsc) = 6.62 mA/cm2, fill factor (FF) = 64.20 %, photo conversion efficiency (PCE) = 3.40 %) and Allium Cepa peel dye (Voc = 0.81 V, Jsc = 6.79 mA/cm2, FF = 65.70 %, PCE = 3.61 %). When paired with Allium Cepa dye, the CD modified photoanode (CD/TiO2) offers Voc = 0.73 V, Jsc = 6.64 mA/cm2, FF = 61.27 % and PCE = 2.97 %. Similarly, when combined with Hibiscus rosa-sinensis dye, the output parameters of the CD/TiO2 photoanode are Voc = 0.72 V, Jsc = 6.54 mA/cm2, FF = 64.4 % and PCE = 3.03 %. In comparison to all tested devices, the unmodified photoanode (TiO2) displayed the lowest performance, with parameters such as Voc = 0.59 V, Jsc = 6.45 mA/cm2, FF = 52.5 %, PCE = 2.10 % using Allium Cepa peel dye, and Voc = 0.66 V, Jsc = 6 mA/cm2, FF = 51.60 %, PCE = 2.04 % using Hibiscus rosa-sinensis dye. Furthermore, the co-activation process has been shown to enhance the stability of the devices. While the unmodified photoanodes ceased to operate after eight days, the ZnO-CD composite co-activated photoanodes retained their initial efficiencies up to 61.50 % and 68.53 % with the Allium Cepa peel dye and Hibiscus rosa-sinensis dye, respectively. Therefore, this study underscores the potential of the synthesized composite material in enhancing the performance of natural DSSCs. 2024 Elsevier Ltd -
Unmet Spiritual Needs: A Study among Patients with Chronic Illness
Objectives: The conventional healthcare system operates on the physiological man and overlooks the spiritual man. Many studies reported on the unmet spiritual needs of terminally ill patients. Despite spiritual care being a predictor of a positive mindset (meaning making) among patients with terminal illnesses, assessing spiritual needs and providing adequate spiritual care is still a distant phenomenon in the healthcare setting. Materials and Methods: With the help of a spiritual needs questionnaire, we analysed the unmet spiritual needs and preferences of 30 terminally ill patients. Specific attention was given to psychosocial, emotional, existential, religious and acceptance of death. Results: The results show that 72% of terminally ill patients reported a strong desire to have their spiritual needs met. Psychosocial needs scored the highest, while acceptance of dying scored the least. The need to be connected with the family was one of the strongest wishes expressed in the study. Religious needs ranked as the second category of needs. Findings show that the highest needs are intertwined with the patients culture. Conclusion: In India, religion and family connections are essential; terminally ill patients expressed the desire that meeting these two aspects makes their lives meaningful even at the end stage. The results warrant a spiritual needs assessment as a deathbed test to make the endoflife more meaningful. 2024 Published by Scientific Scholar on behalf of Indian Journal of Palliative Care. -
Social Work Intervention Research in Child Sponsorship Programs: Enhancing Psychological Well-being of Marginalized Adolescents
The Child Sponsorship Program (CSP) is critical to enhancing the objective and subjective well-being of enrollees. Meanwhile, social work interventions emphasize scientific approaches aimed at empowering marginalized populations. This intervention research (IR) was focused on raising the psychological well-being (PWB) of adolescents in a prominent CSP located in Kochi, Kerala. Preliminary findings from a pilot study underscored the need for intervention, and subsequent Delphi survey results guided the formulation of an intervention strategy. Capitalizing on the transformative power of peer groups, IR implemented a social group work intervention to enhance adolescent PWB in CSP. Using a nonequivalent comparison group interrupted time-series design, the PWB of participants in the intervention group (IG, N = 20) and comparison group (CG, N = 20) was measured and compared. Ryffs PWB scale with 42 items served as the assessment instrument. Descriptive statistics confirmed the normal distribution of baseline data for all participants (N = 40), while repeated measures ANOVA in SPSS 25 validated the alternative hypothesis, indicating significant differences in PWB measures over time within IG and between IG and CG. Additionally, along with statistical evidence of intervention effectiveness, this study used a qualitative design for ongoing evaluation of the intervention process, providing insights for program refinement and demonstrating intervention outcomes. By defining a model for group work intervention among CSP adolescents to improve PWB, this study underscores the important role of social work interventions in empowering marginalized populations. The Author(s) 2024. -
Advanced Electrochemical Detection of 2,4-dichlorophenol in Water with Molecularly Imprinted Chitosan Stabilized Gold Nanoparticles
2,4-Dichlorophenol (2,4-DCP) is a hazardous chemical that can be passed down to offspring. Because 2,4-DCP degrades slowly and can be passed down to future generations, its a pesticide that needs to be continuously monitored and managed. With the use of chitosan-stabilized AuNPs on a glassy carbon electrode and the molecular imprinting technique, an effective electrochemical sensor has been built for the selective determination of 2,4-DCP in different aqueous samples. The analytes electroactive surface area and number of interaction sites are both increased by the AuNPs. The formulated AuNPs were characterized using several material characterization techniques. Molecularly imprinted nanomaterials provided the selectivity against other interfering chlorophenols. With a detection limit of 6.33 nM and a broad linear dynamic range of 21.09 to 310 nM, 2,4-DCP was found using differential pulse voltammetry. Without interference from structural analogs, the sensor was effectively evaluated in a variety of contaminated water samples. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Titania Doped CDs as Effective CT-DNA Binders: A Novel Fluorescent Probe via Green Synthesis
Carbon dots (CDs), which belong to the class of zero-dimensional carbon-based nanomaterials, have garnered significant interest owing to their wide array of applications spanning from the electronics industry to the healthcare sector. This work employs a facile, inexpensive approach to synthesize green luminescent carbon dots (J-10) from a potential medicinal plant named Justicia Wynaadensis by the one-step hydrothermal method. A nanocomposite (JT-10) of the CDs is prepared by adding TiO2 nanoparticles derived from green synthesis of Lavandula leaves. The J-10 and JT-10 are further characterized by X-ray Diffraction spectroscopy (XRD), Transmission Electron Microscopy (TEM), Raman analysis X-ray Photoelectron Spectroscopy (XPS), and Fourier transform infrared techniques (FTIR), UVvis spectroscopy, Photoluminescence (PL), and Fluorescence or PL lifetime analysis. The average size of synthesized CDs is 1.85 nm and exhibits an excitation-dependent fluorescence nature at 320 nm. PL lifetime analysis of J-10 and JT-10 is calculated to be 5.80 and 2.84 ns respectively. Offering these unique optical properties and biocompatibility, the synthesised material is suitable for investigating their binding affinity and interaction mechanisms with DNA. The use of JT-10 in DNA binding studies contributes to the development of sustainable and efficient nanomaterials for applications in biosensors, drug delivery, and gene therapy. 2024 Wiley-VCH GmbH. -
Structural engineering on indole derivative for rechargeable organic lithium-ion battery
In the present work, the indole derivative, namely, 3,3?,3?-methane-triyl-tris-1H-indol(tris-Ind), is synthesized and characterized as an organic electrode material in rechargeable lithium-ion batteries (RLIB). The structural characterization of the synthesized molecule is carried out using physicochemical techniques. The ball milling method is used for the lithiation process to form electroactive lithiated tris-Ind (Li-tris-Ind). The electrochemical activity of Li-tris-Ind is measured in aqueous and non-aqueous electrolytic media, and the results are compared. The aqueous cell system delivers an average cell potential of 0.76V with a discharge capacity of 189 mAhg?1, whereas the non-aqueous cell system delivers an average potential of 1V with 506 mAhg?1. The potentiostatic electrochemical impedance spectroscopic studies reveal the kinetics of finite diffusion. The organic electrode shows good cyclic stability and reproducibility in both systems, making it a significant practical material for RLIB applications. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.