Showing papers by "Shiv Nadar University published in 2021"

Piezoelectric Materials for Energy Harvesting and Sensing Applications: Roadmap for Future Smart Materials

Abstract: Piezoelectric materials are widely referred to as "smart" materials because they can transduce mechanical pressure acting on them to electrical signals and vice versa. They are extensively utilized in harvesting mechanical energy from vibrations, human motion, mechanical loads, etc., and converting them into electrical energy for low power devices. Piezoelectric transduction offers high scalability, simple device designs, and high-power densities compared to electro-magnetic/static and triboelectric transducers. This review aims to give a holistic overview of recent developments in piezoelectric nanostructured materials, polymers, polymer nanocomposites, and piezoelectric films for implementation in energy harvesting. The progress in fabrication techniques, morphology, piezoelectric properties, energy harvesting performance, and underpinning fundamental mechanisms for each class of materials, including polymer nanocomposites using conducting, non-conducting, and hybrid fillers are discussed. The emergent application horizon of piezoelectric energy harvesters particularly for wireless devices and self-powered sensors is highlighted, and the current challenges and future prospects are critically discussed.

Titania modified gum tragacanth based hydrogel nanocomposite for water remediation

Abstract: In this work, 2-hydroxyethyl methacrylate cross-linked gum tragacanth hydrogel (GumT-cl-HEMA) was systematically synthesized to attain maximum swelling (396.9 %) by simple microwave-assisted green polymerization technique. The prepared GumT-cl-HEMA hydrogel was further modified by using TiO2 nanoparticles to generate TiO2 loaded 2-hydroxyethyl methacrylate cross-linked gum tragacanth (GumT-cl-HEMA/TiO2) hydrogel composite. The GumT-cl-HEMA hydrogel and GumT-cl-HEMA/TiO2 hydrogel composite were characterized through XPS, BET, XRD, FTIR, TGA and SEM. The GumT-cl-HEMA hydrogel and GumT-cl-HEMA/TiO2 hydrogel composite were used to perform adsorption to separate the molecules of malachite green (MG) from water. Batch adsorption parameters were examined to analyze the pH effect, time effect and adsorbent dose for the adsorption of malachite green dye from aqueous solution. The inclusion of TiO2 particles inside the GumT-cl-HEMA hydrogel led to increased adsorption rates and stability. The adsorption was accurately followed the pseudo second order model and Langmuir isotherm model. The reported GumT-cl-HEMA/TiO2 hydrogel composite removal efficiency was 99.3 % adsorbent dose = 90 mg, MG = 50 ppm, pH = 7, volume = 50 mL and time = 80 min). The synthesized samples were regenerated and then reused for five repeated adsorption-desorption cycles. The construction of GumT-cl-HEMA hydrogel and GumT-cl-HEMA/TiO2 hydrogel composite gives a facile scheme for efficient adsorbents.

Recovery processes of sustainable energy using different biomass and wastes

Abstract: The biomass ‘waste-to-energy theory transforms low-value biomass within value-added outputs which include high financial potential has drawn awareness from both academicians and enterprise professionals. The biomass waste administration method involves the production, chamber, acquisition, transferal, and conveyor, processing, and distribution of all classes of trash. However, most of these methods are yet under the growing state and attempting to acquire a business partly due to several hurdles, such as proper infrastructure, feedstock, technological conditions, administration management, and communal recognition. Recently, thermochemical methods towards agricultural biomass to power conversion appear encouraging and achievable. The comparative benefit of thermochemical transformation above others is higher potency and adaptability among subsisting infrastructure tools. These advantages differ by method and final product, presenting compliance in matching market necessities. Advantages include environmental change: upon a life-cycle base, high-level biofuels generated through thermochemical transformation could decrease greenhouse gases by around 50% or higher than traditional gasoline. In this article, a comprehensive report on the advancement of the five most encouraging ways of thermochemical transformation (i.e. incineration, pyrolysis, gasification, plasma, and torrefaction), three ways of biochemical renovation (i.e. composting, bioethanol fermentation and anaerobic digestion) and landfill methane capture have been discussed. The modern evolution of several conversion technologies for biomass regeneration has also been examined and benchmarked towards global development. Additionally, the core scientific hurdles in profiting these green pieces of machinery are emphasized.

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.

Abstract: COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-of-the-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.

Encapsulation of Plant Biocontrol Bacteria with Alginate as a Main Polymer Material.

Abstract: One of the most favored trends in modern agriculture is biological control. However, many reports show that survival of biocontrol bacteria is poor in host plants. Providing biocontrol agents with protection by encapsulation within external coatings has therefore become a popular idea. Various techniques, including extrusion, spray drying, and emulsion, have been introduced for encapsulation of biocontrol bacteria. One commonly used biopolymer for this type of microencapsulation is alginate, a biopolymer extracted from seaweed. Recent progress has resulted in the production of alginate-based microcapsules that meet key bacterial encapsulation requirements, including biocompatibility, biodegradability, and support of long-term survival and function. However, more studies are needed regarding the effect of encapsulation on protective bacteria and their targeted release in organic crop production systems. Most importantly, the efficacy of alginate use for the encapsulation of biocontrol bacteria in pest and disease management requires further verification. Achieving a new formulation based on biodegradable polymers can have significant effects on increasing the quantity and quality of agricultural products.

An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion

Abstract: Road accidents are a leading cause of death and disability among youth. Contemporary research on accident detection systems is focused on either decreasing the reporting time or improving the accuracy of accident detection. Internet-of-Things (IoT) platforms have been utilized considerably in recent times to reduce the time required for rescue after an accident. This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone’s built-in and connected sensors not only to detect but also to report the type of accident. This novel technique improves the rescue efficacy of various emergency services, such as emergency medical services (EMSs), fire stations, towing services, etc., as knowledge about the type of accident is extremely valuable in planning and executing rescue and relief operations. The emergency assistance providers can better equip themselves according to the situation after making an inference about the injuries sustained by the victims and the damage to the vehicle. In this work, three machine learning models based on Naive Bayes (NB), Gaussian mixture model (GMM), and decision tree (DT) techniques are compared to identify the best ADC model. Five physical parameters related to vehicle movement, i.e., speed, absolute linear acceleration (ALA), change-in-altitude, pitch, and roll, have been used to train and test each candidate ADC model to identify the correct class of accident among collision, rollover, falloff, and no accident. NB-based ADC model is found to be highly accurate with 0.95 mean F1-score.

Optimal governance and implementation of vaccination programmes to contain the COVID-19 pandemic

Abstract: Since the recent introduction of several viable vaccines for SARS-CoV-2, vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. We argue that game theory and social network models should be used to guide decisions pertaining to vaccination programmes for the best possible results. In the months following the introduction of vaccines, their availability and the human resources needed to run the vaccination programmes have been scarce in many countries. Vaccine hesitancy is also being encountered from some sections of the general public. We emphasize that decision-making under uncertainty and imperfect information, and with only conditionally optimal outcomes, is a unique forte of established game-theoretic modelling. Therefore, we can use this approach to obtain the best framework for modelling and simulating vaccination prioritization and uptake that will be readily available to inform important policy decisions for the optimal control of the COVID-19 pandemic.

Nature inspired materials: Emerging trends and prospects

Abstract: The term “nature-inspired” is associated with a sequence of efforts to understand, synthesize and imitate any natural object or phenomenon either in a tangible or intangible form, which allows us to obtain improved insights into nature. Such inspirations can come through materials, processes, or designs that we see around us. Materials, as opposed to processes and designs found in nature, are tangible and can readily be used without engineering efforts. One such example is that of an aquaporin that is used to filter water. The scope of this work in nature-inspired materials is to define, clarify, and consolidate our current understanding by reviewing examples from the laboratory to industrial scale to highlight emerging opportunities. A careful analysis of “nature-inspired materials” shows that they possess specific functionality that relies on our ability to harness particular electrical, mechanical, biological, chemical, sustainable, or combined gains. A natural plant leaf compared with a highly engineered nature-inspired artifically fabricated leaf. Taking inspiration from natural materials can help shape the future of materials to address sustainability issues. The unique functions that have arisen in living organisms as a result of evolution can help scientists develop novel materials. Saurav Goel from London South Bank University, UK, and his team review a wide range of examples from the laboratory and industry. Such examples include gaining insights from photosynthesis to develop photocatalysts and artificial photosynthesis, from plant roots in order to filter water, from electric eels to guide the conversion of chemical energy to electrical energy and from biomineralization to create ceramics at lower temperatures. Their review also sheds light on subtle differences between the terms ‘inspiration’, ‘mimetics’ and ‘mimicry’ from a design-to-manufacture perspective.

Review on the Accelerated and Low-Temperature Polymerization of Benzoxazine Resins: Addition Polymerizable Sustainable Polymers.

Abstract: Due to their outstanding and versatile properties, polybenzoxazines have quickly occupied a great niche of applications. Developing the ability to polymerize benzoxazine resin at lower temperatures than the current capability is essential in taking advantage of these exceptional properties and remains to be most challenging subject in the field. The current review is classified into several parts to achieve this goal. In this review, fundamentals on the synthesis and evolution of structure, which led to classification of PBz in different generations, are discussed. Classifications of PBzs are defined depending on building block as well as how structure is evolved and property obtained. Progress on the utility of biobased feedstocks from various bio-/waste-mass is also discussed and compared, wherever possible. The second part of review discusses the probable polymerization mechanism proposed for the ring-opening reactions. This is complementary to the third section, where the effect of catalysts/initiators has on triggering polymerization at low temperature is discussed extensively. The role of additional functionalities in influencing the temperature of polymerization is also discussed. There has been a shift in paradigm beyond the lowering of ring-opening polymerization (ROP) temperature and other areas of interest, such as adaptation of molecular functionality with simultaneous improvement of properties.

Emerging Perovskite Solar Cell Technology: Remedial Actions for the Foremost Challenges

Abstract: Perovskite solar cells (PSCs) exhibit the steepest growth in power conversion efficiency among the existing photovoltaic technologies. However, a wide range of factors restrict the commercial viability of PSCs as a renewable energy source. This article reviews the latest progress in PSC devices including innovative light-harvesting perovskite materials and advanced interfacial layers, and the foremost challenges. The most promising remedial solutions to challenges are also discussed. For the realization of a high performing and eco-friendly stabilized perovskite photovoltaic device, a combinational method involving multiple restorative solutions is required. A specific emphasis is given to unveiling interesting perovskite properties, and device fabrication approaches to the solutions. These remedies and strategies may help researchers to select appropriate methods and design their experiments to obtain a high photo-conversion efficiency in photovoltaic devices with enhanced stability as compared to conventional photovoltaic devices.

Aminopropyltriethoxysilane as a linker for cellulose-based functional materials: New horizons and future challenges

Abstract: Cellulose and cellulose derivatives represent one of the most abundant, in terms of quantity class of sustainable polymers that nature provides. The use of these polymers in a wide range of applications is well known for being used as such or by processing composite or functionalized materials. When it comes to functionalized cellulose, a small problem arises due to the limitation given by the limited reactivity of the hydroxyl groups present on the polymer chain. The present review aims to show a versatile method of functionalizing cellulose with aminopropyltriethoxysilane, the remaining amino group having a much higher reactivity and being much more versatile. Examples of the use of aminopropyltriethoxysilane as a linker for subsequent binding will be presented, as well as its direct applicability in various reactive separation processes.

Steam gasification with torrefaction as pretreatment to enhance syngas production from mixed food waste

Abstract: In this work, the steam gasification of mixed food waste was carried out with torrefaction as pretreatment. Torrefaction was carried out at various temperatures; 230 °C, 260 °C, and 290 °C and for one-hour residence time. Improvement in physico-chemical properties of food waste by torrefaction was studied using proximate, elemental, and compositional analysis. Additionally, the effect of torrefaction on mass yield, energy yield, mass density and energy density was presented. Gasification was then carried out using torrefied food waste as biomass with steam as a gasification agent at 700 °C. The steam flow rate was kept at 0.625 mL/min with a steam to biomass ratio of 1.25. The resulting syngas was characterized for syngas yield, syngas composition, H2 yield, and high heating value (HHV). It was found that syngas yield and H2 field varied from 0.95 to 3.49 m3/Kg and 0.6 to 2.15 m3/Kg respectively from gasification of food waste torrefied at different temperatures. The highest HHV of syngas was found to be 12.19 MJ/Nm3. Cold gas efficiency and carbon conversion efficiency were also determined to evaluate the performance of proposed pretreatment on steam gasification.

Hydrogel of gelatin in the presence of graphite for the adsorption of dye: Towards the concept for water purification

Abstract: Gelatin grafted environment-friendly hydrogel composite was utilized to perform the effective adsorption of methyl violet (MV) dye. Due to non-toxic, high surface zone and biodegradability, gelatin has received considerable attention for sustainable applications. Gelatin grafted methyl methacrylate (GE-g-MMA) hydrogels were prepared through the microwave-assisted green method. The GE-g-MMA hydrogel showed the highest swelling of 1039.4 %. Graphite (Gph) was introduced in GE-g-MMA hydrogel matrix to construct gelatin grafted methyl methacrylate/graphite (GE-g-MMA/Gph) hydrogel composite for adsorptive removal of poisonous methyl violet (MV) dye from water. The GE-g-MMA hydrogel and GE-g-MMA/Gph hydrogel composite were described through FTIR, XPS, SEM, Raman, XRD, TGA and BET techniques. Various adsorption factors like adsorbent dose, % removal of dye, contact period and solution pH were examined to develop ideal adsorption conditions. The GE-g-MMA/Gph hydrogel composite exhibited high adsorption efficiency of 99.9 % in 40 min (pH 9, MV: 50 mg L−1, adsorbent dose: 20 mg, MV volume: 20 ml). Experimental analysis results revealed that the synthesized GE-g-MMA/Gph hydrogel composite has a high capacity to remove MV molecules from aqueous solution at 25 °C. The values of the regression coefficient (R2) in case of Frendluich (0.999), Langmuir (0.978), Dubinin-Radushkevitch (0.936) and Temkin (0.931) clearly showed that the Freundlich isotherm fitted best to the adsorption process and pseudo-first-order kinetics demonstrated the better fit of adsorption with highest R2 (0.935). The GE-g-MMA/Gph hydrogel composite is economical, after six regeneration cycles, the adsorption efficiency of 95.8 % has been maintained.

Cellulosic Grewia Optiva fibres: Towards chemistry, surface engineering and sustainable materials

Abstract: Natural fibres are abundantly available in nature and their properties largely depends on their physical features and chemical composition. The Himalayan Grewia Optiva fibre is one such natural fibre that is extracted by processing “shoots of Grewia optiva tree” (Grewia oppositifolia). The composite materials manufactured by utilizing natural fibres possess remarkable mechanical, thermal, and physico-chemical properties attributing to their chemical composition and structural dimensions. However, further “upgrade in the aforementioned properties of natural fibre-based bio-composites can be accomplished if the interfacial attachment between the fibre and matrix is enhanced”, which can be done by surface functionalization of the fibres. In this review article, the impact of various surface functionalization strategies on mechanical, thermal, chemical resistance, water absorption and, moisture absorption properties of Grewia optiva fibre have been discussed to set up surface functionalization strategies as a viable process in consolidating valuable Grewia optiva fibre for industrial applications. The surface-functionalized Grewia optiva fibres reinforced composites, thus, prompts the development of Grewia optiva fibre as a reliable and suitable sustainable material for manufacturing of different industrial components that will help t in developing green materials for the feasible future.

Recent advancements in transparent carbon nanotube films: chemistry and imminent challenges

Abstract: Carbon nanotube (CNT)-doped transparent conductive films (TCFs) is an encouraging option toward generally utilized indium tin oxide-depended TCFs for prospective stretchable optoelectronic materials. Industrial specifications of TCFs involve not just with high electrical performance and transparency but also amidst environmental resistance and mechanical characteristic; those are usually excused within the research background. Though the optoelectronic properties of these sheets require to be developed to match the necessities of various strategies. While, the electrical stability of single-walled CNT TCFs is essentially circumscribed through the inherent resistivity of single SWCNTs and their coupling confrontation in systems. The main encouraging implementations, CNT-doped TCFs, is a substitute system during approaching electronics to succeed established TCFs, that utilize indium tin oxide. Here we review, a thorough summary of CNT-based TCFs including an overview, properties, history, synthesis protocol covering patterning of the films, properties and implementation. There is the attention given on the optoelectronic features of films and doping effect including applications for sophisticated purposes. Concluding notes are given to recommend a prospective investigation into this field towards real-world applicability. This graphical abstract shows the overview of different properties (mechanical, electrical, sensitivity and transportation), synthesis protocols and designing (dry and wet protocol, designing by surface cohesive inkjet-printed and the support of polymers), doping effect (general doping, metal halides, conductive polymers and graphene for transparent electrodes) and implementations (sensing panels, organic light-emitting diodes devices, thin-film transistors and bio-organic interface) of carbon nanotubes transparent conductive films.

On the platform but will they buy? Predicting customers' purchase behavior using deep learning

Abstract: A thorough understanding of online customer's purchase behavior will directly boost e-commerce business performance. Existing studies have overtly focused on purchase intention and used sales rank as a natural proxy, which however has limited business application. Additionally, intention to purchase does not necessarily convert to actual retail purchases. We aim to further our understanding of online customer's purchase behavior for an e-commerce platform by predicting the same using deep learning techniques, on a large multidimensional data sample of more than 50,000 unique web sessions. This study used two distinct sets of variables, i.e., platform engagement and customer characteristics, as key predictors of online purchases by retail customers. We further compared the predictive capability of our deep learning method with other widely used machine learning techniques for prediction, including Decision Tree, Random Forest, Support Vector Machines, and Artificial Neural Networks. We found that the deep learning technique outperformed the machine learning techniques when applied to the same dataset. These analyses will help platform designers plan for more platform engagements while simultaneously expanding the academic understanding of purchase prediction for online e-commerce platforms.

On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI

Abstract: In this paper, classification of mental task-root brain–computer interfaces (BCIs) is being investigated. The mental tasks are dominant area of investigations in BCI, which utmost interest as these system can be augmented life of people having severe disabilities. The performance of BCI model primarily depends on the construction of features from brain, electroencephalography (EEG), signal, and the size of feature vector, which are obtained through multiple channels. The availability of training samples to features are minimal for mental task classification. The feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper suggests an approach to augment the performance of a learning algorithm for the mental task classification on the utility of power spectral density (PSD) using feature selection. This paper also deals a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above stated method, the findings demonstrate substantial improvements in the performance of learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman’s statistical test for finding the best combinations and compare various combinations of PSD and feature selection methods.

Spatio-temporal variations in landscape ecological risk related to road network in the Central Himalaya

Abstract: Road networks are permanent manmade features altering the landscape structure and subsequently influence regional landscape ecology. Taking south-western foothills of Central Himalaya, as a case, t...

Bactericidal surfaces: An emerging 21st-century ultra-precision manufacturing and materials puzzle

Abstract: Progress made by materials scientists in recent years has greatly helped the field of ultra-precision manufacturing. Ranging from healthcare to electronics components, phenomena such as twinning, dislocation nucleation, and high-pressure phase transformation have helped to exploit plasticity across a wide range of metallic and semiconductor materials. One current problem at the forefront of the healthcare sector that can benefit from these advances is that of bacterial infections in implanted prosthetic devices. The treatment of implant infections is often complicated by the growth of bacterial biofilms on implant surfaces, which form a barrier that effectively protects the infecting organisms from host immune defenses and exogenous antibiotics. Further surgery is usually required to disrupt the biofilm, or to remove the implant altogether to permit antibiotics to clear the infection, incurring considerable cost and healthcare burdens. In this review, we focus on elucidating aspects of bactericidal surfaces inspired by the biological world to inform the design of implant surface treatments that will suppress bacterial colonization. Alongside manufacturing and materials related challenges, the review identifies the most promising natural bactericidal surfaces and provides representative models of their structure, highlighting the importance of the critical slope presented by these surfaces. The scalable production of these complex hierarchical structures on freeform metallic implant surfaces has remained a scientific challenge to date and, as identified by this review, is one of the many 21st-century puzzles to be addressed by the field of applied physics.

Energy storage performance of 2D MoS2 and carbon nanotube heterojunctions in symmetric and asymmetric configuration.

Abstract: Excellent cyclic stability and fast charge/discharge capacity demonstrated by supercapacitors foster research interest into new electrode materials with 100% cycle life and high specific capacitance. We report an improvement in the electrochemical performance of MoS2/multiwalled carbon nanotubes (MWCNT) nanohybrid and intensively explored its performance in symmetric and asymmetric supercapacitor (ASC) assembly. The symmetric assembly of MoS2/MWCNT exhibits capacitance of around 274.63 F g-1 at 2 A g-1 with higher specific energy/power outputs (20.70 Wh kg-1/1.49 kW kg-1) as compared to the supercapacitor based on pristine MoS2 (5.82 Wh kg-1/1.07 kW kg-1). On the other hand, a unique all-carbon-based ASC assembled with MoS2/MWCNT and VSe2/MWCNT composite with K2SO4 as electrolyte delivers the highest energy density of 32.18 Wh kg-1 at a power density of 1.121 kW kg-1 with exceptional cycling stability and excellent retention of about 98.43% even after 5000 cycles. These outstanding results demonstrate the excellent electrochemical properties of both symmetric and asymmetric systems with high energy density and performance, which further enable them to be a potential candidate for supercapacitor applications.

A New Simulation Approach of Transient Response to Enhance the Selectivity and Sensitivity in Tunneling Field Effect Transistor-Based Biosensor

Abstract: In this work, a new simulation approach of transient analysis on single cavity dielectric-modulated (DM) ${p}$ -type of tunnel field-effect transistor (TFET) is examined for biosensing applications. The device operation and performance are investigated using the 2D device simulator and results are well-calibrated with experimental data. In this work, we have examined DC transfer characteristics, the transient response of drain current, drain current sensitivity ( ${S}$ ), and selectivity ( $\Delta {S}$ ). Focussing more on the transient results, we have obtained maximum sensitivity of orders greater than 108 for APTES biomolecule with respect to air and a significant selectivity value in orders of 103 for APTES with respect to Biotin biomolecule. The performance of the device in terms of selectivity can be further improved (~104) by optimizing the back-gate bias, and therefore, the impact of back-gate bias has been analysed. The results for charged biomolecules and partially filled cavity are further investigated & highlighted. The DM ${p}$ -TFET biosensor shows a significant improvement in the results with the transient response for biosensing applications with the feasibility of operating at low voltages (gate voltage of −2.0 V, drain voltage of −0.5 V and back gate voltage 0 to 0.5 V).

Recent advances in bio-electrochemical system analysis in biorefineries

Abstract: Concerns around acquiring the appropriate resources toward a growing world population have emphasized the significance of crucial connections between food, energy, and water devices, as described within the food-energy-water nexus theory. Advanced biorefineries provide second-generation biofuels and added-value chemicals through food products have affected these nexus sources. We combine various conversion technologies and expected options to look further for cost-effective technologies that maximize the value of resource use and reuse and minimize the amount of resource needed and environmental impacts. In this review article, our central focus is on structure and application, the outline of food-energy-water (FEW) nexus in biorefineries and bio-electrochemical system (BES) and looking into the energy-efficient and value-added product recovery. In addition, based on BES analysis for energy efficiency and valuable product recoveries such as hydrogen evaluation, acetate, recovery of heavy metals, nutrient’s recovery has been discussed under this article. Additionally, we focused on wastewater processing methods, novel electrode materials used in BES, BESs-based desalination and wastewater treatment, recent BES architecture and designs, genetic engineering for enhanced productivity, and valuable materials production surfactants and hydrogen peroxide. Finally, we concluded the topic by discussing the remediation of soil contamination, photosynthetic & microfluidic BES systems, possibilities of employing CO2, including prospects and challenges.

Epigenetic Regulation of the Wnt/β-Catenin Signaling Pathway in Cancer.

Abstract: Studies over the past four decades have elucidated the role of Wnt/β-catenin mediated regulation in cell proliferation, differentiation and migration. These processes are fundamental to embryonic development, regeneration potential of tissues, as well as cancer initiation and progression. In this review, we focus on the epigenetic players which influence the Wnt/β-catenin pathway via modulation of its components and coordinated regulation of the Wnt target genes. The role played by crosstalk with other signaling pathways mediating tumorigenesis is also elaborated. The Hippo/YAP pathway is particularly emphasized due to its extensive crosstalk via the Wnt destruction complex. Further, we highlight the recent advances in developing potential therapeutic interventions targeting the epigenetic machinery based on the characterization of these regulatory networks for effective treatment of various cancers and also for regenerative therapies.