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Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT
ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet; countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques. 2023 IEEE. -
Twitter sentiment analysis on online food services based on elephant herd optimization with hybrid deep learning technique
Twitter is a social media stage, making it a valuable resource for learning about peoples opinions, feelings, and thoughts. For this reason, experts came up with methods to analyse the tone of tweets and determine whether they were favourable or negative. This article aims to assist businesses, and especially app-based meal delivery businesses, in conducting competitive research on social broadcasting and transforming social broadcasting data into data production for decision-makers. In this analysis, we compared Swiggy, Zomato, and UberEats. Customers tweets about all these brands are obtained using R-Studio, and a deep learning-based sentiment examination approach is functional on the retrieved tweets. The pseudo-inverse learning autoencoder is able to provide feature extraction in the form of an analytic solution after pre-processing, without resorting to many iterations. In this research, we suggest framework for combining the Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) models. ConvBiLSTM is used, which is a word embedding model that uses numerical values to represent tweets. The CNN layer takes the feature implanting as input and outputs lower features. In this instance, elephant herd optimization is used to fine-tune the Bi-LSTM weights. Among the three firms, the results indicate that Zomato got the most positive feedback (29%), followed by Swiggy (26%), and UberEats (25%). Zomato also had fewer bad reviews than Swiggy and UberEats, with only 11% of users having a poor experience. In addition, tweets were evaluated for unfavourable views against all three meal delivery services, and suggestions for improvement were offered. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Twitter Sentiment Analysis Based on Neural Network Techniques
Our whole world is changing everyday due to the present pace of innovation. One such innovation was the Internet which has become a vital part of our lives and is being utilized everywhere. With the increasing demand to connected and relevant, we can see a rapid increase in the number of different social networking sites, where people shape and voice their opinions regarding daily issues. Aggregating and analysing these opinions regarding buying products and services, news, and so on are vital for todays businesses. Sentiment analysis otherwise called opinion mining is the task to detect the sentiment behind an opinion. Today, analysing the sentiment of different topics like products, services, movies, daily social issues has become very important for businesses as it helps them understand their users. Twitter is the most popular microblogging platform where users put voice to their opinions. Sentiment analysis of Twitter data is a field that has gained a lot of interest over the past decade. This requires breaking up tweets to detect the sentiment of the user. This paper delves into various classification techniques to analyse Twitter data and get their sentiments. Here, different features like unigrams and bigrams are also extracted to compare the accuracies of the techniques. Additionally, different features are represented in dense and sparse vector representation where sparse vector representation is divided into presence and frequency feature type which are also used to do the same. This paper compares the accuracies of Nae Bayes, decision tree, SVM, multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and their validation accuracies ranging from 67.88 to 84.06 for different classification techniques and neural network techniques. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob
On an average, approximately 7000 tweets are communicated each second and in total it piles up to around 300 billion tweets every year. Society are free to contribute their opinions on public platform and hence it acts as a reliable interface to assess society ongoing viewpoint and attitude over any matter or event. Consumers very often make use of social media to exchange their views about anything. Business may get domain for enhancement and smooth interpretation of the behavior of people regarding various facts through opinion mining. Thus to carry out this mining of opinions on social media interface, textual categorization with language analysis is of great help. With the help of NLP token tool, phrases can be divided into various word series after dropping stop phrases. Larger tweets tokenizing and classifying into distinct labels is a concern. Thus, the main objective of this framework is to process the tweets based on specific keywords given by user, categorize these phrases into negative, positive and neutral ones. TextBlob module assists users and developers to interpret user sentiments about a news. This research tries to give suggestion a textual opinion assessment on social media samples utilizing the NLTK and TextBlob modules. 2023 IEEE. -
Twitter Hate Speech Detection using Stacked Weighted Ensemble (SWE) Model
Online Social Media has expanded the freedom of expression in the internet, which has become a disturbing problem if it has an impact on the situation or the interest of a country. Hate speech refers to the use of hostile, abusive or offensive language, directed at a certain group of people who share common property, whether it is their gender, ethnicity or race (i.e. racism), faith and religion. Therefore, auto detection of hate speeches has an increased importance in Online Social Media for filtering any message that has hatred language before posting it to the network. In this paper, a Stacked Weighted Ensemble (SWE) model is proposed for the detection of hate speeches. The model ensembles five standalone classifiers: Linear Regression, Nae Bayes', Random Forest, Hard Voting and Soft Voting. The experimental results on a Twitter dataset has shown an accuracy of 95.54% in binary classification of tweets into hateful speech and an improved performance is noted compared to the standalone classifiers. 2020 IEEE. -
Twitter data analysis using hadoop ecosystems and apache zeppelin
The day-to-day life of the people doesn't depend only on what they think, but it is affected and influenced by what others think. The advertisements and campaigns of the favourite celebrities and mesmerizing personalities influence the way people think and see the world. People get the news and information at lightning speed than ever before. The growth of textual data on the internet is very fast. People express themselves in various ways on the web every minute. They make use of various platforms to share their views and opinions. A huge amount of data is being generated at every moment on this process. Being one of the most important and well-known social media of the present time, millions of tweets are posted on Twitter every day. These tweets are a source of very important information and it can be made use for business, small industries, creating government policies, and various studies can be performed by using it. This paper focuses on the location from where the tweets are posted and the language in which the tweets are written. These details can be effectively analysed by using Hadoop. Hadoop is a tool that is used to analyze distributed big data, streaming data, timestamp data and text data. With the help of Apache Flume, the tweets can be collected from Twitter and then sink in the HDFS (Hadoop Distributed File System). These raw data then analyzed using Apache Pig and the information available can be made use for social and commercial purposes. The result will be visualized using Apache Zeppelin. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Twins in diversity: Understanding circumstellar disc evolution in the twin clusters of W5 complex
Young star-forming regions in massive environments are ideal test beds to study the influence of surroundings on the evolution of discs around low-mass stars. We explore two distant young clusters, IC 1848-East and West located in the massive W5 complex. These clusters are unique due to their similar (distance, age and extinction) yet distinct (stellar density and far-ultraviolet radiation fields) physical properties. We use deep multiband photometry in optical, near-infrared and mid-infrared wavelengths complete down to the substellar limit in at least five bands. We trace the spectral energy distribution of the sources to identify the young pre-main sequence members in the region and derive their physical parameters. The disc fraction for the East and West clusters down to 0.1?M was found to be 2 per?cent (N = 184, N = 492) and 1 per?cent (N = 173, N = 814), respectively. While no spatial variation in the disc fraction is observed, these values are lower than those in other nearby young clusters. Investigating the cause of this decrease, we find a correlation with the intense feedback from massive stars throughout the cluster area. We also identified the disc sources undergoing accretion and observed the mass accretion rates to exhibit a positive linear relationship with the stellar host mass and an inverse relationship with stellar age. Our findings suggest that the environment significantly influences the dissipation of discs in both clusters. These distant clusters, characterized by their unique attributes, can serve as templates for future studies in outer galaxy regions, offering insights into the influence of feedback mechanisms on star and planetary formation. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Twins and Preterm Birth
About 3.2% of live births comprise twins, and 20% account for preterm labor, with delivery of twins before 37 and 32weeks of gestation approximately 60% and 10.7%. About five times elevated risk of initial neonatal and infant mortality is noted in the case of twin pregnancy in preterm parturition cases. Both spontaneous and indicated preterm labor is observed more in monochorionic twins than in dichorionic twins. Prediction and diagnosis of preterm labor is effectively done using transvaginal ultrasound to compute the length of the cervix before 24weeks without any risk. Vaginal administration of progesterone in women with less than 25 mm cervical length is beneficial to prevent preterm and neonatal obstacles in twin pregnancies. Physical evaluation showed women undergoing cerclage surgery, when cervical dilation is greater than 1cm, have lowered the risk of perinatal death and preterm labor at varied gestational stages. In a few studies, twin delivery is not directly associated with preterm and related complications. However, pregnancies weighing less than 1000g are associated with major disability around year 1 compared to singleton preterm. All these considerations are crucial in order to optimize the antenatal management of this group of pregnancies destinated to show an increasing trend. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Twin deficit hypothesis: Some recent evidence from India /
Global Business And Economics Review, Vol.18, Issue 3/4, pp.487 - 495, ISSN: 1097-4954. -
Twin deficit hypothesis: Some recent evidence from India
The purpose of this study is to examine the relationship between budget deficit and trade deficit commonly known as 'twin deficits hypotheses' in Indian economy. We used time series data for the period of 1970 to 2013. The empirical results of this study follow the autoregressive distributed lag (ARDL) cointegration technique for long run and short run estimates and error correction mechanism (ECM). In this study, we check the hypotheses that trade deficit is the determinant of budget deficit with its current values or the lag values. The results of the ARDL model confirm that there is the positive and significant relationship between trade deficit and budget deficit. So twin deficits hypothesis is valid for India. The ARDL results of the short run confirm the hypothesis that trade deficit can determine the budget deficit in the case of India. The results of the long run estimates are also significant. The error correction specification is used to find evidence of long-run causality running from budget deficit to trade deficit and vice versa. The empirical results suggest that trade deficit can determine the budget deficit in case of India. 2016 Inderscience Enterprises Ltd. -
Twin Benefits: A Study on the Implementation of Digital Twin in Crypto Mining Optimization
The economic downturns at the beginning of the twenty-first century were marked by peoples distrust of financial institutions. Leveraging the demand for a decentralized financial system, Satoshi Nakamoto introduced the concept of Bitcoin, based on blockchain technology. The growth of Bitcoin attracted attention to the Bitcoin mining farms and their substantial energy consumption. According to the Cambridge Bitcoin Electricity Consumption Index, the annualized energy consumption of Bitcoin reached 145.55 TWh (terawatt hours). Bitcoin ranks 27th in global electricity consumption and 67th in global greenhouse gas emission. The production and disposal of mining hardware is also a significant environmental issue. Crypto mining is an area that is constantly striving to achieve energy optimization. Currently, the main focus for energy efficiency optimization is hardware upgradation, resorting to renewable energy, or adopting overclocking. However, these solutions often focus on the individual aspects of the problem. Farms should have a system to leverage real-time data and analytics to make data-driven decisions for optimization. Digital Twin (DT) has the ability to address these limitations and provide a data-driven approach to energy optimization in crypto mining farms. The article discusses the scope of DT implementation in crypto mining, the benefits and impact of DT implementation, the challenges and considerations with DT implementation, various computational models that can be applied for DT implementation, and the policies and regulations to encourage DT adoption in mining farms. The article delves into the potential of computational modeling approaches like Digital Twin to optimize energy consumption and promote sustainability in the blockchain landscape. The article can also initiate further discussions on how financial technology and environmental responsibility can go hand-in-hand. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Twin Benefits: A Study on the Implementation of Digital Twin in Crypto Mining Optimization
The economic downturns at the beginning of the twenty-first century were marked by peoples distrust of financial institutions. Leveraging the demand for a decentralized financial system, Satoshi Nakamoto introduced the concept of Bitcoin, based on blockchain technology. The growth of Bitcoin attracted attention to the Bitcoin mining farms and their substantial energy consumption. According to the Cambridge Bitcoin Electricity Consumption Index, the annualized energy consumption of Bitcoin reached 145.55 TWh (terawatt hours). Bitcoin ranks 27th in global electricity consumption and 67th in global greenhouse gas emission. The production and disposal of mining hardware is also a significant environmental issue. Crypto mining is an area that is constantly striving to achieve energy optimization. Currently, the main focus for energy efficiency optimization is hardware upgradation, resorting to renewable energy, or adopting overclocking. However, these solutions often focus on the individual aspects of the problem. Farms should have a system to leverage real-time data and analytics to make data-driven decisions for optimization. Digital Twin (DT) has the ability to address these limitations and provide a data-driven approach to energy optimization in crypto mining farms. The article discusses the scope of DT implementation in crypto mining, the benefits and impact of DT implementation, the challenges and considerations with DT implementation, various computational models that can be applied for DT implementation, and the policies and regulations to encourage DT adoption in mining farms. The article delves into the potential of computational modeling approaches like Digital Twin to optimize energy consumption and promote sustainability in the blockchain landscape. The article can also initiate further discussions on how financial technology and environmental responsibility can go hand-in-hand. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Tweaking the electrocatalytic ability of Cu-MOF by the inclusion of PTA: a selective electrochemical sensor for resorcinol
Resorcinol (RL) is a phenolic compound that is extensively utilized in the industrial sector, mostly for skin care applications as an antiseptic and disinfectant. However, this chemical has the potential to be very hazardous to people and the environment due to its pernicious nature in the environment owing to its high degree of toxicity and weak degradation capacity. Finding novel analytical techniques to monitor RL is therefore crucial. A facile and superior electrochemical fabrication route was procured to develop the composite of Cu-BTC-MOF/PTA/CFP for the sensitive detection of resorcinol (RL). The modified Cu-BTC-MOF/PTA/CFP (copper benzene-1,3,5-tricarboxylate-poly-3-thiophene acetic acid) electrode displayed improved electron transport features as well as excellent electrocatalytic performance. The developed electrode was characterized using physicochemical and electrochemical techniques. The enhanced electrochemical activity of the Cu-BTC-MOF/PTA/CFP electrode compared to the individual MOF and polymer electrode was examined using electrochemical characterization, which revealed a 10-fold increase in the current response for Cu-BTC-MOF/PTA/CFP (0.004 A) compared to the bare electrode. The cyclic voltammetric analysis of the Cu-BTC-MOF/PTA/CFP electrode in the presence of 120 nM analyte gave an oxidation peak at 0.62 V and a 5.4-fold increase in the current peak compared to the bare CFP electrode suggesting a higher sensitivity in sensing the analyte. The limit of detection for RL under optimal conditions was calculated to be 8 nM with a broad linear range from 0.025 ?M to 350 ?M. In addition, the Cu-MOF/PTAA/CFP electrode was scrutinized for its stability, reproducibility, and selectivity. Real sample analysis was carried out to validate the analytical applications. 2024 RSC. -
Turning mango kernel waste into high-energy porous carbon: a sustainable electrode material for high-performance supercapacitors with exceptional stability
This study explores the sustainable production of high-performance supercapacitor electrodes from waste mango kernels, addressing the growing need for eco-friendly energy storage solutions. Porous carbon materials were synthesized via pyrolysis at varying temperatures (700, 800, 900, and 1000 C), designated as MK7, MK8, MK9, and MK10, respectively. The synthesized carbon was obtained via a simple and eco-friendly carbonization, yielding a highly porous structure with a large specific surface area of 1348.9 m2 g?1, for MK9 material as confirmed by BET analysis. Raman spectroscopy revealed a high degree of graphitization with D and G bands, indicating the presence of both disordered and graphitic carbon domains. SEM imaging showed a well-developed, interconnected porous morphology, while XRD patterns confirmed the amorphous nature with partially crystalline domains. The resulting carbon materials were evaluated for their electrochemical performance in supercapacitor applications. Electrochemical characterization revealed that the MK9 sample, pyrolyzed at 900 C, exhibited the highest specific capacitance of 205.8 F g?1, surpassing the performance of the other samples. To optimize device performance, symmetric supercapacitors were fabricated using a CR2032 coin cell configuration with different electrolytes and concentrations. The KOH electrolyte device demonstrated a maximum power density of 5137.86 W kg?1, an energy density of 12.32 W h kg?1, and a specific capacitance of 112.4 F g?1. Furthermore, this device exhibited excellent cycling stability, maintaining its performance over 100 000 galvanostatic charge-discharge cycles. A practical demonstration showed the ability of the device to power a red LED for approximately 15 minutes. These results highlight the potential of utilizing waste biomass, specifically mango kernels, for sustainable and efficient supercapacitor development. 2025 The Royal Society of Chemistry. -
Turbulent Flow in Forced Convection Heat Transfer-Numerical Validation
Forced convective heat transfer of airflow through circular pipe with constant heat input and different free stream velocities is numerically validated. The significance of the present work is that the suction flow has been employed in the forced convection set up domain kept in the wind tunnel. From first law of thermodynamics and applying the energy balance equation, experimental heat transfer coefficient is determined. Further correlations are used to validate the experimental results. Although correlations provide reasonable estimates from the point of feasibility and accuracy, computational methods are used to estimate the convective heat transfer coefficient. Hence in this paper experimental, theoretical and computational analysis is carried out. The results reveal that the numerical validation is an effective tool from the point of feasibility and accuracy to determine the convective heat transfer coefficient. 2022. MechAero Foundation for Technical Research & Education Excellence. -
Tuning WO3 film properties for electrochromic applications via annealing and oxygen pressure
The objective of this investigation was to examine the intricate relationship between annealing temperature and oxygen partial pressure (PaO2) in regard to morphological, structural, and electrochemical properties of tungsten trioxide (WO3) films that were produced through sputtering. The films were deposited under two different PaO2 values, specifically 0.3 mTorr and 0.5 mTorr, and then underwent annealing at various temperatures: room temperature, 100, 200, 300, and 400 degrees Celsius. X-ray diffraction (XRD) analysis revealed a temperature-dependent transition from an amorphous to a crystalline phase. Morphological analyses conducted with scanning electron microscopy (SEM) indicated a trend towards a smoother surface as both the annealing temperature and PaO2 rose. At 400 C, the films exhibited a granular surface finish. Significantly, the film fabricated at 0.3 mTorr and subjected to room temperature (RT) annealing showed cracks, indicating inherent stress in the film. Electrochemical evaluations revealed that the WO3 film deposited at 0.5 mTorr and annealed later at 200 C demonstrated enhanced redox performance, better diffusion of ions, and remarkable reversibility. Impressive results were demonstrated in optical studies, attaining 83 % optical modulation, colouration efficiency (CE) up to 30.54 cm/C, and swift switching durations of 1.17 s for colouration and 0.82 s for decolouration. Moreover, cycling tests showed negligible degradation after 100 cycles for the films deposited at 0.5 mTorr PaO2 and treated at 200 C, emphasizing their resilience. This study furnishes a comprehensive knowledge of the consequences of annealing temperature and PaO2 collectively on WO3 films, highlighting the novel strategy of enhancing electrochromic efficacy by modifying temperature and meticulously balancing the PaO2, thus contributing to the progress of energy-efficient smart materials. 2025 -
Tuning variegated characteristics of NiO thin films via 50keV nitrogen ion beam irradiation
In this study, a systematic analysis of the changes brought about by low-energy ion beam irradiation in NiO thin films has been carried out. NiO thin films, deposited on glass substrates by RF magnetron sputtering method have been irradiated with 50keV Nitrogen ions (N+) at varied ion fluence values. With N+ irradiation, the intensity of diffraction peak corresponding to (440) decreases up to ion fluence of 1 1016 ion/cm2 due to the irradiation-induced lattice damage. Furthermore, at the highest fluence (5 1016 ions/cm2), the dominancy of (400) is lost and the crystal structure is reoriented to (440) alignment. The low energy ion irradiation has caused a mitigation in thin film transmittance by 25% compared to unirradiated sample. A decrease in the 1LO mode observed from Raman spectroscopy accounts for the formation of Ni vacancy defects at the highest fluence. Ion beam irradiation is seen to tune the material bandgap. The observed reduction in bandgap with an increase in ion fluence can be correlated to the formation of shallow levels near the conduction band of the host material with ion fluence. Bigger grains of pristine NiO thin film are broken into smaller fragments at fluences 5 1015 and 1 1016 ions/cm2. AFM analysis revealed the smoothening of thin film surfaces due to the atomic diffusion arising from ion beam irradiation. The correlated results from structural and morphological analysis support the deposition of subsequent amounts of energy to the lattice and the consequent modifications in the thin film properties. NiO films can thus be tailored with different ion fluences, making them suitable for optical as well as energy storage applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Tuning the output of the higher plants Circadian Clock
The circadian clock is an ascribed regulator found in the cells of creatures, that keeps biological and behavioral processes in stnc with dailt environmental changes throughout the 24-hour ctcles. When the circadian clock in humans malfunctions or is misaligned with environmental signals, the timing of the sleep-wake ctcle is altered and several circadian rhtthm sleep disorders result. Due to the Earth's rotation on its axis, predictable environmental changes are anticipated bt complex processes. The combined term for these ststems is the circadian clock. The circadian rhtthm regulates photostnthesis and photoperiodism, making it the "primart controller of plant life." The circadian clock is made up of post-translational alterations to core oscillators, epigenetic tweaks to DNA and histones, and auto regulatort feedback loops in transcription. In addition, the circadian clock is cell-autonomous and regulates the circadian rhtthms of distinct organs. Biochemical elements such as photostnthetic products, mineral nutrients, calcium ions, and hormones are used bt the core oscillators to communicate with one another. Arabidopsis is utilized to identift clock-related genes that govern plant growth, germination, pollination, flowering, abiotic and biotic stress responses, and more. The biological ctcles of all species, notablt humans, are undoubtedlt impacted bt other elements, including high altitude and changing ecoststems, in addition to the ones alreadt stated. Although it hasn't tet published ant experimental or scientific evidence to support them, the implication that living things have lives does appear inescapable. Hence, the present studt elaborates on the higher plants related to the circadian clock. The Author(s). -
Tuning the electronic dimensionality and bandgap in Cs2AgBiX6 (X = Br, Cl) for photovoltaic applications: a DFT-1/2 study of cation disorder
Nontoxic, stable, and experimentally realized lead-free halide double perovskites, Cs2AgBiX6 (X = Br, Cl), attracted much attention for solar cell applications. However, their reduced electronic dimensionality and indirect (wide) bandgap, limiting solar energy absorption efficiency, are not mostly suitable. To address such issues, we employ the computationally efficient DFT-1/2 + SOC method to study the electronic structure of cation-ordered and cation-disordered materials comparatively. Our study explores the impact of cation disorder in tuning the electronic dimensionality, demonstrating how the disorder effect reduces bandgaps, increases solar energy absorption, enhances band dispersion, and decreases carrier effective masses for better photovoltaic performance. We observe an evolution of the electronic dimensionality in the disordered systems, influencing the carrier effective masses and absorption properties. Fractional (and non-integer) electronic dimensionality appears to be an essential concept in understanding the optoelectronic properties. The direct bandgap, high absorption in the desired energy range, and mostly lower effective masses of the disordered systems make them suitable for solar cell applications. 2024 The Royal Society of Chemistry. -
Tuning the Electrocatalytic Properties of VO2(B): Role of W Doping in Bifunctional Water Splitting
Vanadium-based systems have evoked considerable interest in researchers due to their potential for advanced renewable energy technologies. However, the possibilities of VO2(B) as an electrocatalyst for water splitting are yet to be explored, even though it is an excellent candidate as an electrode material for various batteries. In this work, the bifunctionality of VO2(B) is investigated in overall water electrolysis, focusing on its enhanced catalytic activity when doped with transition metal atoms such as tungsten (W). Hydrothermally synthesized W-doped VO2(B) nanoflakes demonstrate superior hydrogen evolution (HER) and oxygen evolution reactions (OER), confirming this enhanced catalytic activity. For an optimal W doping, the overpotential values decreased from 518 to 335mV and 435 to 348mV for hydrogen evolution and oxygen evolution, respectively. These observations are supported by the lowest Tafel slope values of 115 and 74mVdec?1 for HER and OER, respectively, and the lowest charge transfer resistance of 47 ?. The excellent stability of the nanostructures revealed by the chronopotentiometry demonstrates their reliable performance over extended periods. Together, these findings highlight that W-doped VO2(B) nanostructures are highly effective candidates as electrocatalysts for overall water-splitting applications. 2025 Wiley-VCH GmbH.