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Design and Development of Signal Processing and AI-Based Classification Framework for BCI Applications
No full textBrain Computer Interface (BCI) systems represent a revolutionary advancement in neural
output manipulation, empowering individuals to interact with their environment solely
through brain signals, bypassing conventional neuromuscular pathways. The systems are
designed primarily to control assistive devices such as speech synthesisers and robotic
wheelchairs, facilitating seamless interaction with the external surroundings. Preprocessing
and feature extraction modules form the core of BCI systems, which play pivotal roles in
enhancing system performance.
To this end, this work endeavours to pioneer novel methodologies to extract and classify EEG
signals, aiming to enhance the efficacy of BCI systems. Beginning with the preprocessing
phase, the focus is on optimising key parameters, including time segment, frequency band
determination, and the spatial arrangement of electrodes, to design an efficient model for MI-
EEG signal classification. This method strategically utilises data from a minimal set of EEG
channels, optimising computational efficiency without compromising performance. An
automated approach incorporating the time-reassigned multisynchrosqueezing transform
technique with the deep learning framework, E-CNNet, has been developed to extract and
classify distinctive MI-EEG features. The time-reassigned multisynchrosqueezing transform
technique extracts time and frequency information from EEG signals, transforming MI-EEG
signals into time-frequency representations with enhanced resolution. The developed E-
CNNet model captures meaningful patterns from the time-frequency representations for
precise feature extraction, subsequently classified by an ensemble of classifiers. This
approach achieved high classification accuracy and enhanced the overall reliability of the BCI
systems. The reduction in the methodological complexity improves the viability of the
proposed methodology for practical-world BCI applications.
Next, an innovative method tailored for feature extraction and the classification of MI-EEG
signals for BCI systems is introduced. The conjunction of the scaling-basis chirplet transform
along with the hybrid parallel-series attention-driven deep learning architecture presents
substantial improvement in MI-EEG signal analysis. The scaling-basis chirplet transform
effectively maps non-stationary MI-EEG signals to high-resolution and energy-concentrated
time-frequency representation, thereby enhancing the efficacy of the model by capturing
intricate dynamics of neural activities. The hybrid parallel-series attention-driven model is
designed for extraction of multi-scale information imperative to classify MI-EEG signals
efficiently. This framework enhances the extraction of discriminative features and facilitates
more accurate classification of neural patterns associated with MI tasks. This method attains
superior classification accuracy, underscoring its viability for real-time BCI applications,
making it a viable solution for practical, real-time BCI applications.
With further advancement, the graph theory has been leveraged to construct brain functional
networks, facilitating the identification of MI-EEG signals by modeling the complex neural
interactions and connectivity patterns among distinct brain regions. To mitigate noise and
minimize redundant information present in the EEG channels, a novel method, Stockwell
Transform- based phase lag index-Wilcoxon signed test (S-PLI-WT) is proposed. The PLIiv
computed via S-transform enhances time-frequency resolution, capturing inter-channel MI-
EEG information by computing EEG phase across frequencies and time points. This method
uses phase lag combined with a weighing mechanism which eliminates volume conduction
and noise, identifying significant connections without thresholds via the Wilcoxon signed-
rank test method. The global and local network topology attributes are extracted from selected
EEG channels. The study further develops the RFE-ELI5 algorithm to select significant
features. It employs the Explain Like I’m 5 (ELI-5) framework of Explainable Artificial
Intelligence (XAI) to interpret the outcomes of the proposed framework and offers insights
into the features that play a significant role in the decision-making process of the proposed
framework. Furthermore, the study analyses quantum machine learning to classify MI-EEG
signals (left-hand, right-hand, feet and tongue MI classes). Furthermore, the study delves into
the deployment of quantum machine learning algorithms to classify MI-EEG signals, thereby
leveraging the inherent advantages of quantum computing in processing complex EEG
datasets. The proposed methodology outperformed existing state-of-the-art models developed
for classifying motor imagery data, thereby affirming its efficacy in advancing MI-EEG signal
analysis for BCI systems.
This extensive exploration delves into the forefront of BCI systems, revolutionising the
manipulation of neural outputs and significantly enhancing the user interaction with the
environment. By emphasising innovative preprocessing, feature extraction and classification
methodologies, we have markedly enhanced the capabilities of BCI systems, facilitating more
accurate interpretation of neural signals and enhancing overall system functionality. Our
proposed channel selection method, leveraging brain functional connectivity, ensures robust
neural signal processing, preserving vital neurological activity for comprehensive analysis.
Furthermore, the techniques developed to extract and classify features from MI-EEG signals
showcase remarkable accuracy and efficiency, leveraging minimal EEG channels while
capturing complex temporal and spectral dynamics. These advancements underscore the
potential for real-time BCI applications, paving the way for more feasible and effective BCI
system designs with profound implications for neuroscientific research and clinical practice
Efficient Task Scheduling and Resource Allocation Using Osmotic Computing
No full textPh. D. ThesisThe rapid proliferation of Internet of Things (IoT) devices, projected to reach 30.9 billion by 2025, has led to an unprecedented surge in data generation and a heightened demand for real-time, low-latency processing. As IoT applications become increasingly critical in sectors such as healthcare, transportation, and smart cities, traditional cloud-centric computing models face significant challenges in meeting the stringent requirements for timely data processing and service delivery. The inherent latency and bandwidth limitations of centralized cloud architectures create bottlenecks that hinder their effectiveness in handling the vast and dynamic data streams generated by IoT devices. To address these challenges, there is a pressing need for computing paradigms that can adapt to the distributed nature of modern networks and efficiently manage the high volume of data generated. This work explores Osmotic Computing (OC), a novel paradigm designed to bridge the gap between edge and cloud computing by optimizing resource allocation and service migration across heterogeneous environments. OC leverages emerging technologies such as 5G and Mobile Edge Computing (MEC) to enhance the responsiveness and scalability of computing systems, enabling them to meet the demands of real-time applications. This work focuses on developing an efficient framework for task scheduling and resource allocation in 5G networks, Intelligent Transportation Systems (ITS), and IoT environments.
The first framework, OCTRA-5G, is developed to address the limitations of traditional task scheduling and resource allocation in 5G networks. Traditional models struggle to efficiently manage the increased complexity and demand of 5G environments. OCTRA-5G utilizes OC principles to segregate services into microservices and macroservices, optimizing their scheduling and migration. Through simulations on sets of 10, 20, and 30 gNBs, the framework demonstrated substantial improvements in performance using algorithms such as FCFS, SJF, and PS, effectively reducing latency and improving overall efficiency.
The second framework, OsCoMIT, is introduced to tackle the challenges in Intelligent Transportation Systems (ITS). With the rise in intelligent vehicles, there is a critical need for efficient resource allocation at the edge network to handle service requests swiftly and effectively. OsCoMIT employs a Proportional Fairness (PF) algorithm to manage computational and memory resources for these vehicles. This framework improves upon traditional algorithms like FCFS and PS by enhancing resource utilization and system performance. The results of the framework are validated through statistical analysis using ANOVA which shows that OsCOMIT performed better than other algorithms.
The third framework, DQN-Osmosis, addresses the dynamic nature of IoT, edge, and cloud environments by introducing Deep Q-Networks (DQN) for intelligent decision-making. In rapidly changing network conditions, traditional decision-making approaches may not adapt swiftly enough. DQN-Osmosis uses reinforcement learning to optimize service migration and task offloading, significantly improving resource allocation and processing efficiency compared to Random Agent, Q-Learning, and SARSA. The effectiveness of this approach was confirmed through the Wilcoxon signed-rank test.
The fourth framework, μ − osmotic, a novel approach designed to address the challenges of dynamic resource allocation in Intelligent Transportation System. As these devices generate vast amounts of data, the need for efficient computational paradigms at the network edge becomes critical. The proposed μ−osmotic framework leverages Osmotic Computing principles and the Advantage Actor-Critic (A2C) algorithm to optimize the distribution of service requests between edge and cloud resources. By dynamically managing resources on the basis of real-time values of the metrics such as CPU usage, memory consumption, and energy efficiency, μ−osmotic enhances service performance, reduces latency, and ensures effective resource utilization. Through comprehensive evaluation and comparison with other algorithms, the framework demonstrates significant improvements
Design and Verification of IDI VIP for Cache Coherency Management
Full text linkThis report focuses on the design and verification of a Verification IP (VIP) for the Interconnect Direct
Interface (IDI) protocol, specifically aimed at managing cache coherence in multi-core systems. Cache
coherence is a critical aspect of modern computer architectures, ensuring that multiple processors
maintain a consistent view of memory. The IDI protocol plays a vital role in facilitating efficient
communication and data consistency across different cache levels.
The primary objective of this research is to develop a robust and comprehensive VIP that accurately
models the IDI protocol's behaviour and verifies its cache coherence mechanisms. The methodology
involves the creation of a Bus Functional Model (BFM) that simulates IDI protocol transactions and
interactions. This BFM is then integrated into a Universal Verification Methodology (UVM)
environment to perform extensive verification and validation.
Key findings from this research demonstrate that the developed VIP effectively identifies and resolves
cache coherence issues, providing a reliable tool for hardware designers to validate their
implementations. The VIP's ability to simulate various cache coherence scenarios and detect protocol
violations significantly enhances the verification process's efficiency and accuracy.
In conclusion, this thesis presents a detailed account of the design and verification process for an IDI
protocol VIP, emphasizing its importance in ensuring cache coherence in multi-core systems. The
developed VIP serves as a valuable resource for future research and development in hardware
verification, contributing to the overall reliability and performance of advanced computing systems
AI-Enabled Oral Cancer Detection
No full textOral cancer is a major health concern, especially in less-developed or resource constrained environments. The main causes of this cancer include tobacco use, betel chewing, poor oral hygiene, and other factors. Early detection of oral cancer symptoms can greatly improve survival rates. However, existing diagnostic methods heavily rely on limited uni-modal data and may miss subtle indicators in resource-constrained settings. Current methods require large labelled datasets, which are difficult to obtain due to security and privacy concerns; hence, there is a need for multimodal approaches that combine different data types (e.g., images with patient clinical data), which helps the model generalize and perform better. This thesis aims to develop and evaluate a transformer-based multimodal pipeline that fuses histopathological images with structured metadata to improve oral cancer detection performance. Histopathological images are pre-processed and fed into pretrained Shifted Window Vision Transformer(Swin)and Data-Efficient Image Transformers (DeiT) pipelines to generate embedding’s, alongside structured metadata (e.g., demographics, risk factors), which are cleaned, encoded, and embedded. A fusion network integrates these embedding’s, and the combined model is trained end-to-end on a curated dataset. We used a publicly available dataset called NDB, which contains 237 histopathological images and related metadata. These images are converted into patches for model training. Both multimodal approaches performed well, achieving accuracies of 92% for DeiT and 93% for Swin Transformer. These results demonstrate that transformer-based fusion of images and metadata can mitigate data scarcity and enhance diagnostic reliability, offering a promising direction for practical oral cancer detection tools. Future work may integrate additional modalities (e.g., acoustic or IoT sensor data) and validate the pipeline on larger, multi-center cohorts
Still Minds, Timely Minds - Personality, Procrastination, and the Promise of Meditation
No full textThis study investigates the interplay between personality, resilience, and academic
procrastination. Drawing on ayurveda-based triguna and big five personality frameworks the
research is structured into three studies. Study 1 employs a correlational design with 300 students
to examine the relationships between Triguna personality types (Sattva, Rajas, Tamas),
resilience, and academic procrastination among college students aged 17 to 24 years (M = 20.69,
SD = 2.2). Sattva (the mindset of balance, purpose, and mindful action) came out as the strongest
predictor (B = -1.211, p < .001) of procrastination. Study 2 examines concurrent validity between
big five personality types and Triguna theory in academic procrastination. Conscientiousness
negatively predicted procrastination (B = - 4.432, p < .001) and sattva was positively correlated
with Conscientiousness (r = .275, p < .01) Study 3 attempts to enhance ‘focus’ an element of
sattva via meditation. Headspace was used with 38 students aged 18 to 22 years (M = 19.32, SD
= 1.2) over four weeks. Scores in high procrastination group dropped within the course of
meditation t(18) = 7.53, p < .001, d = 1.73. Low procrastinators showed a moderate increase,
t(18) = -2.69, p = .015, d = -0.62. Mind wandering showed significant time effect, F(1.58, 3.08)
= 11.27, p < .001, η² = .238, with a notable reduction in high procrastinators (p = .013, d = 0.76),
and a large between-group difference at (t(36) = 4.25, p < .001, d = 1.45). Impulsivity was
measured with a go-no-go task. Omission errors decreased significantly over time (p = .041, η² =
0.107). Go accuracy improved significantly across sessions (p = .041, η² = 0.107), though
reaction time showed no significant change
Role of -2549 Insertion/Deletion Polymorphism in the Promoter Region of VEGF Gene and Its Association with Chronic Obstructive Pulmonary Disease (COPD) Susceptibility and Clinical Parameters.
No full textChronic Obstructive Pulmonary Disease (COPD) is a progressive lung disease that
is characterized by inflammation, airway remodelling, and long-term airflow limitation.
Vascular Endothelial Growth Factor (VEGF) is a key regulator of angiogenesis and has an
antiapoptotic effect on endothelial cells. The −2549 insertion/deletion (I/D) polymorphism
found in the promoter region of VEGF gene is known to affect gene expression
Objectives: The aim of this study was to determine the role of -2549 insertion/deletion
polymorphism in the promoter region of VEGF in modulating susceptibility to COPD and its
associated clinical parameters.
Methods: A case-control study was conducted with 100 COPD patients and 100 age- and sex
matched healthy controls. Genomic DNA was extracted from peripheral blood, and VEGF
−2549 I/D genotyping was performed using PCR. Chi-square test assessed allelic and
genotypic distributions, with ORs and 95% CIs estimating disease risk. Logistic regression
analyzed age, gender, and smoking status, and Bonferroni correction was applied for multiple
comparisons.
Results: In COPD patients DD genotype of the VEGF −2549 I/D polymorphism was more
frequent as compared to controls. The DD genotype was linked to worse lung function indices
(lower FEV₁), higher CAT scores, and advanced GOLD stages; however, after adjustment the
association was not statistically significant. Upon subgroup analysis, it has been found that
men and smoker were having greater genotype effects.
Conclusions: From this study VEGF −2549 I/D polymorphism seems to affect a person's
vulnerability to COPD and could contribute to different clinical symptoms observed among
individuals. The results from study support the idea of using VEGF genetic variations as
predictive biomarkers for the commence and progression of COPD, especially among the
genetically diverse groups
Role of AI in VLSI Device Modelling
No full textThe rapid advancement of Artificial Intelligence (AI) has significantly impacted various
scientific and engineering disciplines, including Very Large-Scale Integration (VLSI) device
modeling. Traditional modeling approaches in VLSI rely on physics-based equations and
empirical data, often requiring extensive computational resources and time. AI-driven
methodologies, particularly machine learning and deep learning techniques, offer a more
efficient and accurate alternative for device modeling and performance prediction.
This thesis explores the role of AI in VLSI device modeling, emphasizing the use of neural
networks, regression models, and reinforcement learning to predict critical device parameters
such as current-voltage characteristics, leakage currents, and threshold voltages. By leveraging
AI-based models, the research demonstrates improved accuracy and reduced computational
complexity compared to conventional techniques. The study also evaluates various AI
algorithms, including artificial neural networks (ANNs), convolutional neural networks (CNNs),
and recurrent neural networks (RNNs), in predicting semiconductor device behavior.
Furthermore, this work examines the integration of AI with physics-based modeling to enhance
interpretability and reliability. Comparative analysis with industry-standard simulation tools
highlights the effectiveness of AI-driven approaches in optimizing VLSI device design. The
research findings indicate that AI can significantly improve predictive accuracy, accelerate the
design cycle, and enable real-time optimization of VLSI circuits.
The study concludes that AI will play a crucial role in the future of semiconductor modeling,
paving the way for more efficient, intelligent, and scalable VLSI design methodologies. This
work serves as a foundation for further research into AI-driven automation in semiconductor
technology
Computational Study of the Role of Coordinated Ligand Architecture on the Oxidation Reactions Catalysed by Transition Metal-based Complexes
No full textChapter 1 This chapter offers a concise overview of the broad applications of transition metal complexes, with a focus on reaction catalysis by iron (Fe) complexes. In particular, the chapter delves into oxidation reactions mediated by Fe(IV)O species, both heme and non-heme, emphasizing the rapidly growing field of C-H activation. The literature relevant to this process has been systematically reviewed, depicting how biological enzymes inspired biomimetic complexes to carry out these reactions. Various concepts, such as Two State Reactivity, and mechanisms of the proposed transfers have been deliberated. A detailed discussion on the influence of factors such as ligand architecture, on these activation processes is provided. Ligand architecture, including modifications to both the axial and equatorial coordination environments, plays a pivotal role in governing the reactivity of these complexes. Studies have shown that alterations to these ligands, such as heteroatom substitutions, can significantly impact reaction dynamics, influencing parameters like redox potentials and activation barriers. Furthermore, an assessment of gaps in the current literature is presented, with particular emphasis on recent advancements in the field. These insights are crucial for guiding the rational design of nextgeneration catalysts with improved performance and selectivity. The chapter concludes by outlining the objectives of the current research, aiming to address these identified gaps. Chapter 2 This chapter provides an in-depth introduction to the essential principles and methodologies of computational chemistry. It begins by explaining quantum mechanics (QM) as the theoretical basis for many computational methods, particularly highlighting the Schrodinger equation as the core equation governing quantum systems. While the exact solution of this equation is only feasible for single-electron systems, approximations are necessary for multi-electron systems. The Born-Oppenheimer approximation is then discussed followed by Hartree-Fock (HF) theory, which is introduced as an ab initio method for solving the Schrodinger equation, though its limitations in accounting for electron-electron corelations are addressed. Post-HF methods, developed to include electron correlation, are described as important advancements to improve accuracy, despite their higher computational cost. The chapter further elaborates on Density Functional Theory (DFT), which is based on the electron density rather than wave functions, making it a widely used approach due to its balance between computational efficiency and v accuracy. The chapter explores the use of functionals like B3LYP, which has been proven to show results with great accuracy in this field. Other factors such as basis sets and solvent models employed have been discussed. Additionally, the chapter discusses the advanced computational tools used in modern chemistry, such as Gaussian 16 for geometry optimization, frequency analysis, and thermochemistry. Visualization tools like Chemcraft is highlighted for their ability to provide molecular structures, bond lengths, atomic charges, and spin densities. Other tools, such as KiSThelP software, are described as valuable for studying tunneling effects and kinetic analysis. Overall, the chapter not only outlines the theoretical frameworks but also demonstrates the practical tools and techniques of computational chemistry, setting the stage for the more detailed studies presented in later sections. Chapter 3 A comprehensive DFT investigation has been presented in this chapter to address the role of equatorial sulfur ligation in C-H activation. A nonheme iron-oxo compound with four nitrogen atoms constituting the equatorially connected macrocyclic framework (represented as N4) [Fe(IV)O(THC)(CH3CN)]2+(THC = 1,4,8,11-tetrahydro1,4,8,11-tetraazacyclotetradecane), has been considered as the base compound. Other complexes have been anticipated by the sequential replacement of this nitrogen by sulfur i.e., N4, N3S1, N2S2, N1S3, and S4. Generally, the anti-conformers (with respect to equatorial N-H and Fe=O) turned out to be the most stable. It was found that with the enrichment of the equatorial sulfur atom, reactivity increases successively, i.e., we get the trend N4 < N3S1 < N2S2 < N1S3 < S4. Our investigations have also verified the available experimental results, where it has been reported that N2S2 is more reactive than N4 in their mixed conformation. In search of insight into this typical pattern of reactivity, the interplay of several factors has been recognised, such as the distortion energy - distortion energy decreases for the transition states with the addition of sulfur; the spin density - the spin density on the oxygen atom increases, implying that the radical character of the abstractor increases on sulfur ligation; the energy of the electron acceptor orbital - the energy of the LUMO (σ*z2) decreases continuously with the sulfur substitution; and the triplet-quintet oxidant energy gap—the energy gap decreases consistently with S-enrichment in the equatorial position. The computational predictions reported here, if further validated by experiments, will definitely encourage the synthesis of sulfur-ligated bioinspired complexes instead of the ones constituting nitrogen exclusively. vi Chapter 4 In this chapter, we present a meticulous computational study to foresee the effect of an oxygenrich macrocycle on the reactivity of C-H activation. For this study, a widely studied nonheme Fe(IV)O molecule with a TMC (1,4,8,11-tetramethyl 1,4,8,11-tetraazacyclotetradecane) macrocycle that is equatorially attached to four nitrogen atoms (designated as N4) and acetonitrile as an axial ligand has been taken into account. For the goal of hetero-substitution, the step-by-step replacement of the N4 framework by O atoms, i.e., N4, N3O1, N2O2, N1O3, and O4 systems, has been considered, and dihydroanthracene (DHA) has been used as the substrate. In order to neutralise the system and prevent the self-interaction error in DFT, counterions called triflates have also been included in the calculations. Studying the energetics of these CH bond activation reactions and the potential energy surfaces mapped therefore reveals that the initial hydrogen abstraction, which is the rate-determining step, follows the two-state reactivity (TSR) patterns, which means that the originally excited quintet state falls lower in the transition state and product. The reaction follows the hydrogen atom transfer (HAT) mechanism, as indicated by the spin density studies. The results revealed a fascinating reactivity order, in which the reactivity increases with the enrichment of the oxygen atom in the equatorial position, namely, the order follows N4 < N3O1 < N2O2 < N1O3 < O4. The impact of oxygen substitution on quantum mechanical tunneling and H/D kinetic isotope effect studies have also been investigated. When analysing the causes of this reactivity pattern, a number of variables have been identified, including the reactant like transition structure, spin density distribution, the distortion energy, and the energies of the electron acceptor orbital, i.e., the energy of LUMO (σ*z2), which validate the obtained outcome. Our results also show very good agreement with earlier combined experimental and theoretical studies considering TMC and TMCO-type complexes. The DFT predictions reported here will undoubtedly encourage experimental research in this biomimetic field, as they provide an alternative with higher reactivity in which heteroatoms can be substituted for traditional nitrogen atoms. Chapter 5 In the first part of this chapter, a DFT investigation has been presented to demonstrate the relevance of the macrocyclic ligand ring size of the high-valent non heme Fe(IV)O complexcatalyzed C-H activation process. Tetramethylcyclam (TMC) with varying ring size measures in terms of n = 12, 13, 14, 15, and 16 in [Fe(IV)O(n-TMC)(CH3CN)]2+ has been considered as the oxidant and dihydroanthracene as the general substrate. Computations were also carried out vii to determine the effect of the axial ligand-acetonitrile on the C-H activation reactivity. It was discovered that the complexes without axial ligands turned out to be more reactive compared to their axially coordinated counterpart. The most intriguing finding, however, was that reactivity increased steadily with ring size increments, giving us the trend 12<13<14<15<16. Behind this typical pattern of reactivity, several factors played a role, including the energy of electron acceptor orbital which sequentially decreases, distortion energy to achieve the transition state which also decreases as we move on from n=12 to 16. The triplet-quintet energy difference of the oxidants also has a part to play, as it decreases with increased ring size, with the quintet becoming more and more dominant. The current studies were also able to corroborate the experimental data that was published regarding Fe(IV)O(13-TMC) (without axial syn form) having a higher C-H activation reactivity than Fe(IV)O(14-TMC) (with axial anti-form). On the whole, this computational presentation gives us a reactivity pattern relying on the ring size commutes and can lead to successful experimental results if pursued based on this reaction. The second part consists of the heme complexes, presenting a detailed comparative analysis of C-H activations catalysed by three different Fe(IV)O porphyrinoid complexes. The study considers the usual heme porphyrin (complex I) as the base compound, porphyrazine (complex II), which is obtained by replacing carbon with nitrogen at the meso position, and phthalocyanine (complex III), which is obtained through the peripheral benzoannulation of porphyrazine. The main focus here is to explore the impact of bridging groups and peripheral functionalisation in heme systems on reactivity. Chloride is used as the axial ligand for all complexes, and DHA is used as the substrate. Factors such as distortion energy and different electron acceptor orbitals significantly affect the overall reactivity. The effect of substitution on quantum mechanical tunneling using H/D kinetic isotope effect studies is also included. The results reveal a fascinating reactivity order: mesonitrogen substitution enhances reactivity, while additional benzo-annulation hinders reactivity, leading to the order complex II > complex I > complex III. In comparison to the usual model compound I, which is Fe(IV)O-porphyrin π cation radical with an –SH axial ligand, complex II was found to be more reactive. These findings support the use of accessible iron frameworks derived from porphyrin in C-H activation processes. viii Chapter 6 This chapter provides conclusion drawn from the research work presented in previous chapters by using DFT to investigate the reactivity and mechanisms of Fe(IV)Oxo complexes, focusing on ligand architecture modifications. Key findings include that hetero-substitution, such as replacing nitrogen with sulfur in macrocyclic ligands, significantly enhances C-H activation reactivity. Additionally, changes in the coordination sphere, including ring size and axial ligand removal, further optimise catalytic efficiency. Studies on heme complexes reveal that structural changes, such as nitrogen substitution and benzo-annulation, influence reactivity patterns. Theoretical results align well with experimental data, confirming the reliability of the reactivity trends. The outlook section proposes expanding research to other metal frameworks, such as manganese and metal-oxygen species, and continuing to explore ligand modifications in hemetype complexes for further catalytic optimization
Preparation and structural characterization of coinage metal loaded ZnO and TiO2 nanostructures for photodegradation of organic pollutants
No full textChapter-1 This chapter briefly introduces semiconductor photocatalysis, focusing on two semiconductor oxides, ZnO and TiO2, as photocatalysts for the decomposition of organic pollutants. It highlights the benefits and drawbacks of these materials and presents various strategies to boost their photocatalytic efficiency. This chapter also discusses the role of various morphologies of nanostructures in the improvement of photodegradation abilities and how discusses the unique characteristics of the different shapes. A thorough review of relevant literature and a summary of characterization techniques used to evaluate the synthesized metal@ZnO or metal@TiO2
nanostructures are included. The chapter also emphasizes the importance of combining ZnO and TiO2 as a binary nanohybrid to enhance performance beyond that of the pristine forms. An outline of the photocatalytic performance assessments, including model pollutants, is provided. Finally, the research gaps identified are discussed in relation to the aims of the current study. Chapter-2 This work assesses the role of the noble metal silver in the visible-light sensitization of UV-active ZnO by introducing a localized plasmonic resonance effect and enhancing the spatial charge carrier separation. We herein synthesize ZnO nanorods via a solvothermal approach, yielding nanorods of length and diameter 1.356 ± 0.619 μm and 120.56 ± 25.09 nm, respectively. Silver nanoparticles are reduced on the surface of ZnO nanorods via the photodeposition route. XRD spectra reveal a high crystallinity and wurtzite-type structure for ZnO with new peaks for cubic phase Ag upon Ag-loading. The diffraction peaks shift upon Ag modification, indicating partial incorporation of Ag into the ZnO lattice. Further, upon Ag-decoration, a slight increase in dislocation density and micro strain is obtained, which could suppress the recombination rate of charge carriers. The DRS spectra reveal a band gap contraction of materials from 3.19 to 2.98 eV with increasing Ag density. The PL spectra confirmed that the optimum Ag concentration of 3 wt% is proficient in harvesting visible light towards high photocatalytic degradation efficiencies of tetracycline (92.1%) and amoxicillin (76.4%) in 90 min as compared to 49.4% and 38% over pristine ZnO nanorods. TOC studies reveal only partial mineralization of ~42.7% (tetracycline) and ~31.3% (amoxicillin) due
vii to the formation of reaction intermediates identified by HR-MS chromatograms. In addition, degradation pathways are proposed through the fragments at different m/z ratios. Chapter-3 The removal of pollutants via photocatalysis involves two steps: adsorption and photodegradation. This study aimed to construct a plasmonic metal-loaded semiconductor and examine its synergic adsorption–photocatalysis effect for the removal of sucrose. Plasmonic metals augment photocatalytic activity by improving visible light absorption and charge transfer mechanisms. ZnO and TiO2 nanoparticles were synthesized via wet chemical processes followed by photodeposition of two noble metals, Cu and Ag, at each photocatalyst. The detailed photocatalytic information was investigated to evaluate the performance of various fabricated materials toward sucrose. With qmax = 156.1 mg g-1 (TiO2) > qmax = 126.1 mg g-1 (Cu@TiO2) > qmax = 109.1 mg g-1 (ZnO) >
qmax = 96.8 mg g-1 (Ag@TiO2) > qmax = 95.7 mg g-1 (Cu@ZnO) > qmax = 66.9 mg g-1
(Ag@ZnO), TiO2 displayed superior adsorption capabilities towards sucrose. However, the overall removal efficiencies were Ag@ZnO(100%) > Cu@ZnO(89%) > Ag@TiO2(86%) >
Cu@TiO2(72%) > ZnO(62%) > TiO2(58%). Thus, Ag@ZnO NPs emerged as a promising photocatalyst for the synergic adsorptive-photocatalytic removal of sucrose contamination due to the combined LSPR and interfacial charge transfer effects of Ag NPs. These NPs were then employed for the solar-irradiated removal of sucrose from sucrose-contaminated wastewater, showing a high efficiency of 91% in 60 min. Chapter-4 The current study investigated 3-D ZnO nanoflowers augmented with elongated 1-D TiO2
nanostructures and gold (Au) nanoparticles. ZnO flowers were effectively fabricated via a facile hydrothermal synthesis. Thereafter, the hydrothermal technique was employed to load various concentrations of TiO2 onto the ZnO surface, resulting in the TiO2/ZnO heterostructure. Additionally, Au nanoparticles were photo-deposited superficially at TiO2/ZnO to construct Au@TiO2(x)/ZnO hybrid structures. Samples were characterized using XRD, FE-SEM, EDX, HR-TEM, and DRS analyses. The photocatalytic abilities of ZnO nanoflowers and their consequent enhancements were studied. The study found that the indicated surface-loading materials can improve the photocatalytic capabilities of pristine ZnO nanoflowers. After 50 minutes of exposure to LED light, the synthesized photocatalysts demonstrated the following
viii removal efficiency: TiO2(32%) < ZnO(45%) < TiO2(1.0)/ZnO(54%) <TiO2(10.0)/ZnO(57%) < TiO2(3.0)/ZnO(59%) < TiO2(5.0)/ZnO(65%) < Au@TiO2(5.0)/ZnO(98%). Further, the Au@TiO2(5.0)/ZnO nanostructures could mineralize the toxic PCM (76.7%) ~4.2 times higher than pristine ZnO (18.5%) and ~6.2 times higher than bare TiO2 (12.4%). The Au@TiO2(5.0)/ZnO samples outperformed the others