Tezpur University

In this paper, a hybrid, comprising of solar-PV and wind energy sources, grid-connected system with nine-switch converter (NSC) instead of a back-to-back (BtB) converter (comprising 12 switches) is proposed and a control scheme is also proposed for NSC. NSC is the potential substitute for the current BtB converter, which uses 12 switches. The NSC uses nine switches, that is, three less active switches than BtB converter therefore the system efficiency is increased by lowering component costs, counts of components, and switching losses. Vector control concept is considered to design the proposed control scheme for NSC to provide both supply power generation to the utility grid at unity power factor and variable speed constant frequency operation under fluctuating wind velocity and solar irradiance. In addition, 120° discontinuous modulation concept is integrated with the control scheme of NSC for reducing the switching losses of the system. Moreover, maximum power point trackers are used for capturing maximum power from wind and solar-PV, and blade pitch angle algorithm is also considered to restrict the generation from wind to its rated power level while speed is more than its rated level. The proposed system along with its control scheme is implemented in OPAL-RT lab to investigate its performance thoroughly under real-life scenarios (i) constant solar irradiance and variable wind velocity and (ii) variable solar irradiance and wind velocity. Results show the effectiveness of the proposed scheme on the system and it has been observed that the proposed scheme provides good dynamic responses in terms of less oscillation during transient, almost negligible (i.e. 0.0001 approx.) steady-state error etc. to the variation of input sources.

At present, selective and accurate determination of hydrogen peroxide (H2O2) and glucose has become essential for routine diagnosis. The present work demonstrates the hydrothermal synthesis of NiO nanosheet (NS)–MoS2-based composite system for non-enzymatic electrochemical detection of H2O2 and glucose. To understand the structure, morphology and elemental constituents of the prepared composite system, various characterization techniques were employed, namely XRD, FTIR, FESEM, TEM and EDX. Redox activity and charge transfer process of the NiO–MoS2-based sensor electrode towards H2O2 and glucose were realized via using electrochemical techniques: cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and chronoamperometry. To be mentioned, limit of detection (LOD) and sensitivity for detection of H2O2 are calculated to be 3 µM and 3925 µA mM−1 cm−2, respectively, under the linear range of 5–455 µM in 0.1 M PBS solution. Similarly, the LOD and sensitivity for sensing glucose is estimated to be 3.53 µM and 1880 µA mM−1 cm−2, respectively, under the linear range of 5–370 µM in 0.1 M NaOH solution. The cost-effective fabricated sensor exhibited good stability with a high selectivity towards the specific analytes only.

Autofrettage is a material forming procedure commonly practised to induce beneficial compressive residual stresses in the proximity of the inner wall of a thick-walled cylinder or hollow disc that are subjected to high pressure in service. This work presents theoretical predictions of residual stresses induced in a hollow circular disc subjected to autofrettage by a radial temperature difference across its wall thickness, called thermal autofrettage. A plane stress condition is assumed for the analysis. The plastic flow of the material in the disc under radial temperature difference is modelled using von Mises yield criterion incorporating Ludwik’s material strain hardening law. The solutions for the thermo-elasto-plastic stresses upon loading the disc by a plastically deforming radial temperature difference and the thermal residual stresses after cooling the disc to room temperature are obtained. The process is numerically exemplified for a typical disc. The beneficial effect of autofrettage-induced residual stresses on the in-service pressure carrying capacity of the disc is investigated. The results obtained in this work are corroborated with the solutions of a corresponding analytical model based on Tresca yield criterion.

Algae are characterised by their rapid growth, microscopic size and eukaryotic nature, and they possess the capability for photosynthesis and simultaneous fixation of atmospheric CO2. The cultivation of algae in wastewater not only addresses environmental concerns but also positions them as promising biofuel feedstock, offering solutions for fuel security and CO2 emissions reduction. In response to the current challenges of the global energy crisis and escalating greenhouse gas emissions, microalgae-based bioenergy emerges as a viable substitute for traditional fossil fuels. The review provides an extensive overview of contemporary techniques for converting algal biomass into diverse biofuels, including biodiesel, biohydrogen, biogas and bioethanol. Moreover, it highlights the latest sustainable technology in the field of biofuel and bioenergy production. The present research status has been assessed through bibliometric analysis employing specific keywords related to diverse biofuels and algae. The visualisation of similarities has facilitated a comprehensive analysis, providing discernment into the interrelationships among chosen publications. The obtained findings furnish significant insights into the optimisation strategies for biofuel production from algae, underscoring their essential role in influencing a more sustainable energy terrain. Algae exhibit significant potential as a sustainable biofuel feedstock, with diverse biogas, biohydrogen, bioethanol and biodiesel production applications. Their notable attributes, including high conversion rates and versatility, position them as a promising and environmentally friendly resource for meeting global energy demands. The findings contribute valuable insights into optimising algae-based biofuel production, emphasising their crucial role in transitioning to a more sustainable energy future.

Conventional wastewater treatment processes, being significant energy consumers, are not sufficiently adapted to the ongoing energy challenges. In this context, the use of electroactive microorganisms in bioelectrochemical systems has grown, primarily focusing on microbial fuel cell technology. Microbial fuel cells, exploiting the electrogenic properties of specific bacteria, provide a sustainable solution by concurrently treating wastewater and generating electric power in treatment plants. This technology transforms wastewater treatment plants into potential net energy producers through the oxidation of organic matter and bioelectricity generation by microorganisms. This chapter thoroughly describes microbial fuel cells, encompassing its various types and essential operational factors influencing its efficiency in wastewater treatment and bioelectricity generation. Recent advancements and breakthroughs in addressing diverse wastewater types are explored alongside analysing associated challenges and their prospects. This chapter objectively assesses the challenges of energy deficiency and high expenditure in microbial fuel cells for wastewater treatment. It explores microbial fuel cell applications and proposes integration with other treatment processes to enhance the practicality and effectiveness of contaminant removal on a larger scale. The aim is to provide valuable insights for overcoming limitations and promoting microbial fuel cell integration in comprehensive wastewater treatment strategies.

Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 $\pm$ .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.

Polymorphism and its screening to select the best‐performing form is in high demand. In low molecular weight organogels (LMWG), gelators are designed as they contain flexible groups, functionalities capable of varied H‐bonding, and increased the potential to show polymorphism. We synthesized a bis‐urea based LMWG G1 and isolated three distinct polymorphic phases (Form I, II, and III). G1 polymorphs showed noticeable differences in solubility; precisely, Form I is highly soluble compared to the other two. Gel screening was carried out for all three polymorphs using different stimuli like heat‐cool, sonication, shaking, and grinding. Among the polymorphs, Form I was found to have better gelling ability which was reflected by the solvent scope, thermal stability (gel‐sol transition temperature Tgel), minimum gelator concentration (M.G.C.), stimuli‐responsiveness, morphology, and rheological properties. The differences in their gelation performance among the three polymorphs are associated with their solubility parameter. Stimuli like sonication, shaking, and grinding triggered Form I to form a gel. Form II and III responded to heat‐cool stimuli only due to poor solubility. Therefore, it is noted crucial to add polymorph screening as an integral part of the gel synthesis to avoid problems associated with reproducibility in the gel prophecy of LMWG systems.

Speech, the principal mode of human interaction, involves the articulation of language through vocal sounds generated by the vocal apparatus. It encompasses various forms such as vocalized speech, whispering, silent speech, and subvocal speech. Silent speech refers to the absence of audible sound despite movement of speech articulators due to minimized airflow. This study seeks to convert silently mouthed words into audible speech aimed at providing communication assistance to individuals with speech impairments. The research utilizes electromyographic (EMG) signals captured from facial muscles during speech production, in conjunction with corresponding audio recordings of the same. Both the EMG signals and the audio recordings are utilized for feature extraction are the extracted features are then collectively employed to train three distinct neural network models viz. Convolutional neural network (CNN) model, Gated Recurrent Unit (GRU) and Convolutional neural network Long Short Term Memory (CNN-LSTM). Model predicts the audio features based on EMG features input and they are subsequently passed through vocoder to reconstruct the original audio speech. The models are tested on real time data and the corresponding metrics and plots are evaluated. The performance metrics establishes the superiority of the CNN-LSTM model over the other models with mean squared error (MSE) as low as 0.036. Such an approach holds promise for improving communication aids and speech rehabilitation technologies.

A hydrothermal approach was adopted to synthesize tungsten oxide (WO3) nanocatalysts with tailored morphology, using oxalic acid (H2C2O4) and hydrochloric acid (HCl) as precursors. This precursor-driven method yielded two distinct WO3 catalysts with unique structural and functional properties, viz. rod-shaped WO3-ox and nanoflower-shaped WO3-h. Characterization by FESEM and XRD revealed variations in morphology and crystallite size, contributing to their specialized catalytic applications. UV–Vis spectroscopy confirmed strong UV absorption by WO3-ox at 283.57 nm with an optical band gap of 2.86 eV, making it ideal for photocatalytic activities. Electrochemical analysis demonstrated that WO3-ox effectively drives the hydrogen evolution reaction (HER), while WO3-h is more suitable for the oxygen reduction reaction (ORR), an essential process in microbial fuel cells (MFCs). In practical applications, WO3-ox achieved an 83.9% degradation efficiency of methylene blue (MB) within 3 h, validating its high photocatalytic efficacy for wastewater treatment. Meanwhile, WO3-h, utilized as a cathode catalyst in MFCs, significantly enhanced system performance, elevating chemical oxygen demand (COD) removal efficiency to 78.7% and improving coulombic efficiency by 3%. These findings underscore the potential of precursor-driven hydrothermal synthesis for optimizing WO3 catalysts tailored for energy and environmental applications, specifically in hydrogen production and sustainable wastewater treatment systems. Graphical abstract

Herein we report the first successful synthesis of ethanol-assisted in situ generated reduced graphene oxide as a support for CuO/NiO nanoparticles. Through the strategic incorporation of Cu and Ni precursors into ethanol, followed by thermal treatment, we achieved the fabrication of reduced graphene oxide-supported CuO/NiO nanoparticles. The material underwent thorough characterization using FT-IR, XRD, TEM, XPS, Raman, and UV-DRS analysis. This method promises a breakthrough approach unveiling an unparalleled potential leading to paradigm shifts in graphene oxide synthesis. A theoretical study has also been performed in support of GO formation from ethanol. The synthesized CuO/NiO nanoparticles over reduced graphene oxide were found to be effective for the reduction of 4-nitrophenol within 5 min.

The current study investigated the enhancement of biomass in S. obliquus, using rice bran oil processing (RBOP) wastewater in different RBOP wastewater concentrations, while also aiming to produce biofuel and treat the wastewater simultaneously. The strain was grown in Blue Green-11 (BG11) media as well as RBOP wastewater at different wastewater concentrations with distilled water at 10%, 25%, 50%, 75%, and 100% under controlled experimental settings. The study findings demonstrated a notable enhancement in the characteristics of RBOP wastewater during a 16-day growth period. The 75% RBOP wastewater concentration demonstrated superior efficacy as a growth medium for producing biomass as well as lipids, among other wastewater concentrations. Accordingly, physicochemical parameters and heavy metal percent of the RBOP wastewater were assessed. The collected biomass was employed in the production of biodiesel, and the fatty acid methyl esters (FAME) were measured, along with an assessment of its characteristics. Physicochemical analysis indicated that S. obliquus was able to effectively decrease levels of nitrate, phosphate, sulfur, and chemical oxygen demand (COD) in RBOP wastewater. The heavy metal reduction percentages for iron (Fe), zinc (Zn), nickel (Ni), copper (Cu), and arsenic (As) were 69.43%, 72.94%, 72.99%, 90.49%, and 91.5%, respectively, following treatment with S. obliquus. FTIR indicated the existence of various functional groups (including alcohol, carboxylic acid) on the surface of the microalgal biomass. The FAME profile of S. obliquus exhibited a moderate level of saturated and unsaturated fatty acid content. S. obliquus demonstrated significant phycoremediation capabilities and potential for lipid production. This study has indicated that S. obliquus is a potential candidate for the treatment of wastewater.

Computational investigations employing Density Functional Theory (SMD(benzene)-M06-2X-D3/6-311+G*) predict that stable metal-free mono (Lewis base)-stabilized borylenes could strongly bind with transition metal (Fe and Ni) complexes. The binding results in increase in the Lewis basicity of the metal centers thus facilitating the formation of metal only Lewis pairs (MOLPs) of the form [L(CO)4Fe → GaCl3] and [L(CO)3Ni → GaCl3] (L = electron donating ligands). Further, the binding of different ligands with the metals as well as bonding in the MOLPs have been further investigated with the help of QTAIM and EDA-NOCV analyses.

In the present time, microplastics (MPs) are a cause of growing concern in freshwater environments throughout the globe. Flood accelerates the transport of MPs from river into the marine environment. However, there is a lack of research on the impact of flood on microplastic abundance and distribution in Indian rivers. This study aims to investigate the flood-induced variation of MPs along the stretch of the Jia Bharali River, one of the major tributaries of the river Brahmaputra. The mean concentration of MPs during post-flooding was highest (27.94 ± 9.25 MPs/L in surface water and 29 ± 8.73 MPs/kg in sediments) as compared to pre-flooding period (22.35 ± 5.55 MPs/L in surface water and 19.42 ± 6.08 MPs/kg in sediments). During pre-flood, fibres account for the majority of MP particles (36.13% in surface water and 38.23% in sediments). Similar results were observed for post-flooding surface water samples as fibres were the most dominant type (35.65%), while in the case of sediments, fragments (34.10%) were the major type. Polyethylene was the dominant polymer type of MPs followed by polypropylene. Polymer hazard index (PHI) indicated high risk, while the coefficient of microplastic impact (CMPI) showed an ‘average’ to ‘minimum’ risk level in the studied area. The study identified flooding, runoff from agricultural fields and various anthropogenic activities as the potential source of MPs in the river. The present study unveiled new insights into microplastic contamination of an Indian river, its source analysis, flood-induced distribution and risk assessment which will aid in mitigating and remediating freshwater microplastic pollution in the future.

Oxime ethers are extensively present as key components in numerous active pharmaceutical ingredients and many other synthetically viable organic compounds. Herein, we present a metal, base and additive free mild...

DC-DC converters are an important area of power electronics. They have been used as the power converter interface for power point tracking in photovoltaic systems. The design of the optimized DC-DC converter thus is an important area for the research community. Design optimization of a DC-DC Buck, Boost, Synchronous Buck and Double Buck converters to reduce overall operational losses is the subject of investigation in this study. The ideal design requires selecting the most suitable values for circuit inductance, capacitance, and switching frequency to guarantee functioning in continuous conduction mode (CCM) and continuous voltage mode. The selected design constraints are the ripple content in voltage and current, and bandwidth for operation in CCM. A total of twenty eight (28) recently developed and popular existing metaheuristic optimization algorithms are utilized to select the optimized DC-DC converter’s design. For identifying the best algorithm and to carry out a performance analysis established optimization algorithms like the Grey Wolf Optimizer (GWO), Moth Flame Optimization Algorithm , Particle Swarm optimization, Whale Optimization Algorithm (WOA) and Firefly Algorithm are selected. The simulated results indicate that majority of algorithms are able to select the best design for the converter topologies within the selected constraint criterion’s. The efficacy of an algorithm is determined based on statistical studies, convergence characteristics, computational time and robustness. It is noted that the algorithm that most effectively solves the current optimization problem is the WOA.

Microorganisms, known for their enzymatic diversity, prove indispensable in addressing global environmental challenges. Amid escalating concerns over plastic degradation, microorganisms stand out as promising agents for breaking down plastic polymers, offering a viable solution to this ecological threat. In the realm of waste management, particularly the intricate disposal of petroleum hydrocarbons and pesticides, microbial bioremediation emerges as a strategic avenue. This involves optimizing microbial activity to enhance waste degradation processes and mitigate the environmental impact of these pollutants. Microbial-enhanced oil recovery (MEOR) exemplifies the pragmatic application of microorganisms, providing sustainable approaches for oil extraction from depleted reservoirs. The exploration extends to bioleaching processes for extracting metals, emphasizing microorganisms’ potential for eco-friendly metal recovery. In electronic waste management, microorganisms contribute to dismantling and recycling, reducing the environmental footprint. The study concludes with biosensor applications, emphasizing microorganisms’ crucial role in real-time pollutant detection, and offering insights for optimizing microbial activities in bioremediation processes.

The advent of biotechnology in the last century has heralded a significant breakthrough with the development of recombinant biopharmaceuticals and industrial enzymes. These pioneering products are synthesized within eukaryotic or prokaryotic heterologous hosts, underscoring the adaptability of genetic engineering in their production. As our understanding of omics data expands, particularly regarding diverse heterologous hosts, and with the emergence of advanced genetic engineering tools, we gain the capability to artificially modify these hosts to enhance the production of recombinant proteins. This technological advancement is poised to propel the global market for recombinant products to unprecedented heights in the foreseeable future, underscoring the profound impact of these innovations across various industries. To fully leverage the potential of heterologous hosts in large-scale biosynthesis, it becomes imperative to discern their strengths and weaknesses. Scientists have identified bottlenecks in different hosts, and addressing these challenges is vital in meeting the escalating demand for recombinant products.

The development of biopesticides and biofertilizers has marked a paradigm shift in modern agriculture, emphasizing sustainable and eco-friendly practices. This evolution stems from advances in microbial ecology, which explores the intricate relationships between microorganisms and their environments. Concurrently, the emergence of environmental microbiology has played a pivotal role in understanding the diverse roles of microbes in addressing environmental challenges. Biopesticides, derived from naturally occurring microorganisms, offer an alternative to chemical pesticides, minimizing ecological impact and promoting agricultural sustainability. Similarly, biofertilizers, comprising beneficial microbes, enhance nutrient availability and soil health, fostering a more balanced ecosystem. Moreover, environmental microbiology extends these principles to address broader environmental issues, showcasing significant applications of microbes in remediation processes. By harnessing beneficial microorganisms, agricultural stakeholders can improve soil quality, minimize ecological impacts, and enhance overall agricultural productivity in an environmentally sustainable manner. This chapter explores the development of biopesticides and biofertilizers and highlights the emerging field of environmental microbiology, focusing on microbial bioremediation and the intricate relationships between microorganisms and their environments.

The uncontrolled emergence of multidrug-resistant mycobacterial strains presents as the primary determinant of the present crisis in antimycobacterial therapeutics and underscores tuberculosis (TB) as a daunting global health concern. There is an urgent requirement for drug development for the treatment of TB. Numerous novel molecules are presently undergoing clinical investigation as part of TB drug development. However, the complex cell wall and the lifecycle of M. tuberculosis within the host pose a significant challenge to the development of new drugs and, therefore, led to a shift in research focus towards alternative antibacterial compounds, notably nanotechnology. A novel approach to TB therapy utilizing silver nanoparticles (AgNPs) holds the potential to address the medical limitations imposed by drug resistance commonly associated with currently available antibiotics. Their broad-spectrum antimicrobial activity presents the utilization of AgNPs as a promising avenue for the development of therapeutics targeting mycobacterial-induced diseases, which can effectively target Mycobacterium tuberculosis, including drug-resistant strains. AgNPs can enhance the effectiveness of traditional antibiotics, potentially leading to better treatment outcomes and a shorter duration of therapy. However, the successful implementation of this complementary strategy is contingent upon addressing several pivotal therapeutic challenges, including suboptimal delivery, variability in intra-macrophagic antimycobacterial effect, and potential toxicity. Future perspectives may involve developing targeted delivery systems that maximize therapeutic effects and minimize side effects, as well as exploring combinations with existing TB medications to enhance treatment outcomes. We have attempted to provide a comprehensive overview of the antimycobacterial activity of AgNPs, and critically analyze the advantages and limitations of employing silver nanoparticles in the treatment of TB.