5,017 research outputs found
Nexus between agriculture and photovoltaics (agrivoltaics, agriphotovoltaics) for sustainable development goal: A review
This is the final version. Available on open access from Elsevier via the DOI in this recordThe coexistence of agricultural land and solar photovoltaics (PV) can be named Agriphotovoltaics (APV). APV concept was developed two decades ago however its actual implementation is happening nowadays. APV directly solves SDGs 7, and 11 by generating benevolent renewable energy without damaging the land and keep producing food for people. In this work, a comprehensive review of the APV system is documented. Currently available software tools, field experiment results, and PV for APV are described in this work which identified that for forecasting APV, a more robust tool is required. Vertically placed Bifacial PV, transparent, and semitransparent tilted PVs can be suitable for shade-intolerant crops whereas opaque PVs are appropriate for shade-tolerant crops. The knowledge gap between various stakeholders such as solar PV researchers, agricultural researchers, and land users needs to be more rigorous. Economic and policymakers should share dialogue to improve the growth of APV which not only solves SDG 7, and 11 but also meets the target for SDG 5, 8, 9,12, and 15
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Plasma engineering of advanced functional materials for photocatalytic wastewater treatment
Semiconductor metal oxide photocatalyst with favourable light absorption and charge transport characteristics have been widely used as a photocatalyst in various applications, to name a few, energy harvesting and storage, environmental remediation and air pollution. Energy harvesting which comprises the full utilisation of the wide solar light (wavelength) spectrum has become a central point of research in the field of materials science and engineering. Hence, the development of sustainable materials from environmentally sustainable techniques which can absorb majority of the solar light spectrum has become a huge challenge. For efficient utilisation of solar energy in catalytic applications, it is important to create photocatalyst that can absorb the full solar spectrum involving ultraviolet (UV), visible (VIS) and near infrared (NIR) wavelengths. More than three decades, TiO2 and its composites have been widely researched academically and used industrially as a low-cost material for photocatalytic applications. However, the large bandgap of TiO2 limits its photocatalytic activity to the UV region which is just 3-5% of sunlight on Earth’s atmosphere. TiO2 also suffers from rapid recombination of photogenerated carriers (i.e., holes and electrons) thereby affecting its photocatalytic efficiency. Over the years, there has been active research in altering the chemistries of TiO2 to overcome these aforementioned shortcomings. The most recent advantage is the use of two dimensional (2D) materials because of its layered structure One of the unexplored and interesting layered structure is MXene. The aim of this thesis is to modify the chemical structure of Ti2C MXene to produce TiO2 as an efficient photocatalyst for absorbing solar energy especially in the UV and visible regions. As a compound of titanium and carbon, Ti2C MXene could facilitate the creation of TiO2 and carbonaceous materials hereby improving the photocatalytic performance. The abundance of surface terminal groups on Ti2C MXene allow for ease of surface modification and functionalisation. In this thesis, for the first time, the functionalisation of TiO2 from Ti2C MXene using a dry and low powered system, atmospheric pressure plasma jet (APPJ) is reported. This process involved using Ti2C nano colloidal ink with highly reactive oxygen plasma source which can tune the electronic properties (engineering bandgap) of Ti2C MXene in-situ while simultaneously printing on to a substrate. X-ray/Ultraviolet Photoelectron spectroscopy showed an additional density of states (DOS) close to valence band edge and changes to the Ti, O core level spectra due to the oxygen plasma functionalisation. Density functional Theory calculation suggests that the changes in the electron structure might be due to the influence of oxygen vacancies and hence the increase in efficiency of catalytic process. Also, time dependent oxygen plasma functionalisation studies reveal the morphology and size of the in-situ generated TiO2 nanoparticles varied from 5-8 nm with exceptionally high photocatalytic performance.
The second aim of the thesis is to create a heterostructure of Ti2C MXene with low cost and earth abundant graphitic carbon nitride, g-C3N4 (GCN) with visible light properties. For the first time, a lower power APPJ method was reported to produce a ternary in-situ TiO2/Ti2C/GCN heterostructure. In this thesis, GCN nanosheets were used as a semiconducting photocatalyst that could efficiently harvest the energy from visible light. Ti2C MXene nanosheets acted as an excellent electron sink while providing enhanced surface area which could facilitate the interfacial charge carriers. Structural studies show the formation of heterostructure formation between Ti2C MXene and GCN. Influence of morphology and hence changes to the optical properties were discussed. The synthesized ternary in-situ TiO2/Ti2C/GCN nanosheets showed enhancement in photocatalytic performance.
The third aim of my research was to integrate TiO2 onto earth abundant natural cellulose fibres. Utilising the power of low power atmospheric pressure plasma (APPJ) to in-situ anchor TiO2 onto cellulose fibres to prevent the thermal degradation and chemical instability leading to leaching of the oxides from the cellulose fibres. APPJ in the presence of highly oxidised species caused an increase in COO- bonds which provided a strong linkage between TiO2 and cellulose materials. Also, structural studies revealed polymorphic changes in the structure of cellulose materials that improved its crystallinity and surface area for photocatalytic applications. APPJ is also able to create oxygen vacancies in the TiO2 which further reduced the bandgap of as synthesized TiO2/cellulose nanocomposites that enhanced photocatalytic applications. Toxicity studies showed that TiO2 was not cytotoxic.
This plasma modified surfaces (of all the samples) show exceptional degradation of wastewater with ternary in-situ TiO2/Ti2C/GCN showing two times more improvement in methylene blue degradation (84% degradation) as compared to in-situ TiO2/Ti2C MXene (42% degradation). Also, TiO2/cellulose bionanocomposite showed excellent adsorptive-photocatalytic performance in degrading industrial waste dye providing a clear route as nanocomposites from research into industrialisation
Expedient Structures for Safeguarding Aircraft Parking Areas from Climatic Impact: An In-depth Exploration
This research delves into the study and analysis of diverse expedient structures designed to protect aircraft parking areas from the influence of various climatic conditions. The article scrutinizes tent, arched, and rigid constructions, outlining their primary advantages, drawbacks, and application domains. Special emphasis is placed on achieving a balance between costs and requirements, as well as selecting the optimal construction type based on climatic conditions and economic factors. Matters of durability, strength, and stability of the constructions are discussed, accompanied by recommendations for making informed decisions when choosing a structure for safeguarding aircraft parking areas. In conclusion, the authors underscore the significance of conducting additional research and evaluating the long-term effectiveness of each construction type for a more qualitative and well-informed decision-making process. This article provides essential recommendations and conclusions that can prove valuable for engineers, designers, and specialists in the field of aviation infrastructure
Detecting and Mitigating Adversarial Attack
Automating arrhythmia detection from ECG requires a robust and trusted system that retains high accuracy under electrical disturbances. Deep neural networks have become a popular technique for tracing ECG signals, outperforming human experts. Many approaches have reached human-level performance in classifying arrhythmia from ECGs. Even convolutional neural networks are susceptible to adversarial examples as well that can also misclassify ECG signals. Moreover, they do not generalize well on the out-of-distribution dataset. Adversarial attacks are small crafted perturbations injected in the original data which manifest the out-of-distribution shifts in signal to misclassify the correct class. However, these architectures are vulnerable to adversarial attacks as well. The GAN architecture has been employed in recent works to synthesize adversarial ECG signals to increase existing training data. However, they use a disjointed CNN-based classification architecture to detect arrhythmia. Till now, no versatile architecture has been proposed that can detect adversarial examples and classify arrhythmia simultaneously. In this work, we propose two novel conditional generative adversarial networks (GAN), ECG-Adv-GAN and ECG-ATK-GAN, to simultaneously generate ECG signals for different categories and detect cardiac abnormalities. The model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples. Moreover, the ECG-ATK-GAN is robust against adversarial attacked ECG signals and retains high accuracy when exposed to various types of adversarial attacks while classifying arrhythmia. We benchmark our architecture on six different white and black-box attacks and compare them with other recently proposed arrhythmia classification models. When considering the defense strategy, the variation of the adversarial attacks, both targeted and non-targeted, can determine the perturbation by calculating the gradient. Novel defenses are being introduced to improve upon existing techniques to fend off each new attack. This back-and-forth game between attack and defense is persistently recurring, and it became significant to understand the pattern and behavior of the attacker to create a robust defense. One widespread tactic is applying a mathematically based model like Game theory. To analyze this circumstance, we propose a computational framework of game theory to analyze the CNN Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values in CAPTCHA dataset. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies from the Game theoretical perspective
Renewable Energy and Energy Storage Systems
The use of fossil fuels has contributed to climate change and global warming, which has led to a growing need for renewable and ecologically friendly alternatives to these. It is accepted that renewable energy sources are the ideal option to substitute fossil fuels in the near future. Significant progress has been made to produce renewable energy sources with acceptable prices at a commercial scale, such as solar, wind, and biomass energies. This success has been due to technological advances that can use renewable energy sources effectively at lower prices. More work is needed to maximize the capacity of renewable energy sources with a focus on their dispatchability, where the function of storage is considered crucial. Furthermore, hybrid renewable energy systems are needed with good energy management to balance the various renewable energy sources’ production/consumption/storage. This work covers the progress done in the main renewable energy sources at a commercial scale, including solar, wind, biomass, and hybrid renewable energy sources. Moreover, energy management between the various renewable energy sources and storage systems is discussed. Finally, this work discusses the recent progress in green hydrogen production and fuel cells that could pave the way for commercial usage of renewable energy in a wide range of applications
Current approaches for modeling ecosystem services and biodiversity in agroforestry systems: Challenges and ways forward
Limited modeling studies are available for the process-based simulation of ecosystem services (ESS) and biodiversity (BD) in agroforestry systems (AFS). To date, limited field scale AFs models are available to simulate all possible ESS and BD together. We conducted an extensive systematic review of available agroforestry (AF), BD, and soil erosion models for the simulation potential of seven most desirable ESS in AFS. Simple to complex AF models have an inherent limitation of being objective-specific. A few complex and dynamic AF models did not meet the recent interest and demands for the simulation of ESS under AFS. Further, many ESS modules especially soil erosion, GHGs emission, groundwater recharge, onsite water retention, nutrients and pesticide leaching, and BD are often missing in available AF models, while some existing soil erosion models can be used in combination with AF models. Likewise mechanistic and process-based BD diversity models are lacking or found limited simulation potential for ESS under AFS. However, further efforts of model development and improvement (integration and coupling) are needed for the better simulation of complex interactive processes belonging to ESS under AFS. There are different possibilities but a proficient modeling approach for better reliability, flexibility, and durability is to integrate and couple them into a process-based dynamic modular structure. Findings of the study further suggested that crop modeling frameworks (MFW) like SIMPLACE and APSIM could be potential ones for the integration and coupling of different suitable modeling approaches (AF, soil protection, GHGs emission, flood prevention, carbon sequestration, onsite water retention, ground recharge, nutrient leaching, and BD modules) in one platform for dynamic process based ESS estimation on daily basis at the field scale
Optical ground receivers for satellite based quantum communications
Cryptography has always been a key technology in security, privacy and defence.
From ancient Roman times, where messages were sent cyphered with simple encoding techniques, to modern times and the complex security protocols of the Internet.
During the last decades, security of information has been assumed, since classical
computers do not have the power to break the passwords used every day (if they are
generated properly). However, in 1984, a new threat emerged when Peter Shor presented the Shor’s algorithm, an algorithm that could be used in quantum computers
to break many of the secure communication protocols nowadays. Current quantum
computers are still in their early stages, with not enough qubits to perform this
algorithm in reasonable times. However, the threat is present, not future, since the
messages that are being sent by important institutions can be stored, and decoded
in the future once quantum computers are available.
Quantum key distribution (QKD) is one of the solutions proposed for this threat,
and the only one mathematically proven to be secure with no assumptions on the
eavesdropper power. This optical technology has recently gained interest to be performed with satellite communications, the main reason being the relative ease to
deploy a global network in this way. In satellite QKD, the parameter space and
available technology to optimise are very big, so there is still a lot of work to be
done to understand which is the optimal way to exploit this technology.
This dissertation investigates one of these parameters, the encoding scheme.
Most satellite QKD systems use polarisation schemes nowadays. This thesis presents
for the first time an experimental work of a time-bin encoding scheme for free-space
receivers within a full QKD system in the second chapter. The third and fourth
chapter explore the advantages of having multi-protocol free-space receivers that
can boost the interoperability between systems, polarisation filtering techniques to
reduce background. Finally, the last chapter presents a new technology that can
help increase communications rates
Enhancing the Structural Stability of α-phase Hybrid Perovskite Films through Defect Engineering Approaches under Ambient Conditions
This thesis investigates methods whereby perovskite solar cell power conversion efficiency and material stability
may be improved. Hybrid perovskites have gained increased attention for optoelectronic applications due to
favourable properties such as strong absorption, facile processing, and changeable band-gap. Despite excellent
improvements in power conversion efficiency of devices, perovskite films are unstable, degrading with relative
ease in the presence of moisture, oxygen, light, heat, and electric fields. The focus of this thesis is on ambient
atmosphere stability, concerned with the influence of moisture in particular on perovskite film fabrication,
degradation, and device functionality. In order to shed light on the impact of ambient atmosphere on perovskite
films, experiments are designed to investigate films during fabrication and degradation. The influences firstly of
stoichiometry during ambient fabrication, and then ionic substitution (with caesium and formadinium) upon
moisture-induced degradation are investigated. Finally, films and devices with a novel composition
incorporating Zn are fabricated under ambient conditions to investigate the effect of Zn addition on perovskite
film stability
Planning Urban Mobility within the UN Sustainable Development Goal Framework
Sustainable development is about raising human well-being and protecting the environment. In the context of urban sustainability, cities have prioritized cars leading to negative impacts on society and the environment. Some scholars are concerned that planners do not incorporate equity in the state of the practice. Individuals facing social exclusion are more vulnerable to low air quality, traffic collisions, and noise. Moreover, stronger mitigation measures are needed since the transport sector is the second largest contributor to emissions.
A conceptual investment framework was designed to interconnect the Sustainable Development Goals (SDGs) for planning and designing sustainable urban mobility under five strategic areas: social justice, health, climate change, economic development, and governance. Policies, modeling tools, and methodologies were reviewed to construct this framework under a systems approach.
To analyze the 2050 net-zero policy of changing the vehicle fleet from Internal Combustion Engines (ICE) to Electric Vehicles (EVs), a bottom-up regional average speed emission model relying on a four-step travel demand was developed. Four scenarios were explored: business as usual (BAU), low, moderate, and aggressive to evaluate the potential reduction of carbon dioxide (CO2) emissions, Nitrogen Oxides (NOx), and Particulate Matter PM2.5. The emission model included vehicle speed and weight, fuel type, and vehicle emission standards. In the case study of Costa Rica, it was found that attaining zero CO2 emissions by 2050 requires shifting to EVs at least 25% and 50% by 2030 and 2040, respectively. For ICE vehicles, changing the minimum vehicle emission standards from EURO 1 to EURO IV and EURO VI positively impacted the reduction of NOx and PM2.5 despite the growth of traffic volumes.
This research explored the barriers women face to having equal opportunities and differentiating urban mobility needs and patterns. A vertical equity transport planning approach that is pro-poor, gender-sensitive, and considers intermediary social health determinants is developed. This research is the first approach to incorporate the most at-risk demographics (material deprivation, disabilities, and single mothers' car ownership) and their exposure to NOx and PM2.5 in identifying high-priority areas
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