Multiple Energy Research Articles

Research on Energy Management in Hydrogen–Electric Coupled Microgrids Based on Deep Reinforcement Learning

Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion, utilization, and storage technologies with distributed wind and photovoltaic power generation techniques, it is possible to achieve complementary utilization and synergistic operation of multiple energy sources in the form of microgrids. However, the diverse operational mechanisms, varying capacities, and distinct forms of distributed energy sources within hydrogen-coupled microgrids complicate their operational conditions, making fine-tuned scheduling management and economic operation challenging. In response, this paper proposes an energy management method for hydrogen-coupled microgrids based on the deep deterministic policy gradient (DDPG). This method leverages predictive information on photovoltaic power generation, load power, and other factors to simulate energy management strategies for hydrogen-coupled microgrids using deep neural networks and obtains the optimal strategy through reinforcement learning, ultimately achieving optimized operation of hydrogen-coupled microgrids under complex conditions and uncertainties. The paper includes analysis using typical case studies and compares the optimization effects of the deep deterministic policy gradient and deep Q networks, validating the effectiveness and robustness of the proposed method.

Optimal dispatch of multiple interconnected-integrated energy systems considering multi-energy interaction and aggregated demand response for multiple stakeholders

Optimal dispatch of multiple interconnected-integrated energy systems considering multi-energy interaction and aggregated demand response for multiple stakeholders

Resilient bus-bar protection scheme for DC microgrids connected to AC electric grids

ABSTRACT In recent years, there has been a growing interest in incorporating microgrids into existing electrical power networks to reduce reliance on conventional grids. This is further due to the numerous benefits of microgrids, most notably the ability to operate in either autonomous or grid-connected mode, making microgrids a highly versatile structure to incorporate intermittent production and energy storage. However, despite the many benefits, it is difficult to present resilience protection for DC bus bar to ensure the efficient operation of a DC microgrid, as there is a non-zero crossing phenomenon of DC fault current, while zero crossing phenomenon of an AC fault. Additionally, the DC bus bar has both an AC side with rectified DC and a DC line current, creating protection concerns for microgrids. To address these issues, this article proposes a novel fast fault detection scheme for DC microgrids with multiple renewable energy sources, energy storage, and loads. The scheme incorporates current and voltage sensing through current and voltage transformers, along with advanced digital relays, to detect and isolate faults on the DC microgrid. By analyzing rectified AC and DC line data, the scheme employs a differential current-based protection strategy to safeguard the DC bus bar. Validated through comprehensive fault simulations in PSS/SINCAL, the method proves effective in maintaining DC bus voltage stability and enhancing microgrid flexibility.

Evaluation of techno-economic design andimplementation of solar-wind hybrid microgridsystem for a small community

Pakistan is faces significant challenges in meeting its energy demand and consumption needs for consumers. This country's energy production from primary sources such as petroleum and natural gasses is incompetent in fuel-use and hence unable to meet feasibility cost. With an increasing population, Pakistan's energy consumption per capita has been steadily rising. This behaviour is leading to critical energy issues, especially in remote rural areas. This trend in rising energy costs and demand factors are similar to those in the energy markets in the South and South-East Asia. The primary energy sources in Asia continent, including fossil fuels, are insufficient supply to meet this growing demand in production and thus resulting in frequent electricity blackouts. Consequently, renewable energy sources such as solar photovoltaic (PV) and wind power have substantially started to produce energy and to provide a huge portion of Pakistan's daily energy needs apart in conventional energy currently. However, these sources are not yet as reliable, conventional energy bases have a challenge for sustainable energy production. As a result, renewable energy factors nonetheless initial started have effectively stabilized energy consumption, particularly for green electricity with net-zero carbon emissions. The aim of this study is to evaluate the feasibility and cost-effectiveness of integrating a microgrid hybrid system with combined (solar PV/wind power) renewable energy as well as conventional fossil fuel generators. This evaluation focuses on predicting energy production and its costs using Hybrid Optimization of Multiple Energy Resources (HOMER) software, and to enhance the electricity standards at NUST (National University for Sciences and Technology), Pakistan. The proposed methodology of microgrid hybrid system, when evaluated using HOMER software, shows a significant improvement in energy stability and cost efficiency. Moreover, this proposed system can reduce reliance on fossil fuels by a substantial percentage, enhances the predictability of energy production, and optimizes its energy consumption. These can achieve better performance metrics in terms of reliability, cost, and environmental impact; feasible solution for Pakistan and the developing countries. This proposed methodology offers a novel approach by integrating renewable energy sources with conventional generators to create a balanced and efficiency factor by microgrid system. This hybrid system goals as an investigation is to optimize this energy production, reduce carbon emissions, and provide a more stable and cost-effective energy supply.

Boosting the piezocatalytic performance of BaTiO3-based materials via phase boundary and defect co-engineering

Recently, piezocatalytic technology has been recognized as an efficient and viable strategy for the remediation of aquatic environments. Herein, a synergistic strategy of phase boundary and defect co-engineering has been proposed to enhance the piezocatalytic performance of BaTiO3-based ceramic materials: a ceramic system material ((1-x)BaTiO3-x(Ca0.9Sr0.1)(Sn0.5Hf0.5)O3, (1-x)BT-xCSSH) was constructed to achieve the coexistence of multiple ferroelectric phases by co-doping ions in the A/B-sites, and defect engineering was realized by a high-temperature annealing. The 0.95BT-0.05CSSH ceramic exhibits the coexistence of rhombic (R), orthorhombic (O), and tetragonal (T) phases, with multiple spontaneous polarization directions and low polarization rotational energy barriers, thereby demonstrating high piezoelectricity (d33 = 450 pC/N) and thus outstanding piezocatalytic performance. The degradation rate/reaction rate constant of the 0.95BT-0.05CSSH ceramic material for rhodamine B (RhB) is 71.35 %/13.5 × 10−3 min−1. After undergoing a high-temperature annealing, the catalytic activity of the 0.95BT-0.05CSSH ceramic is further improved, resulting in a degradation rate/reaction rate constant of 92.84 %/28.5 × 10−3 min−1 for rhodamine B (RhB). The catalytic activity of the 0.95BT-0.05CSSH material is enhanced by the generation of abundant oxygen vacancies during high-temperature annealing, which facilitates the separation and migration of electron-hole pairs. In this study, we propose a novel strategy to upgrade the piezocatalytic performance of BaTiO3-based piezoelectric materials, which is useful for the remediation of aquatic environments.

A systematic method of long-sequence prediction of natural gas supply in IES based on spatio-temporal causal network of multi-energy

Natural gas plays an important role in the energy peak-shaving process of Integrated Energy Systems (IES). Accurate prediction of natural gas supply over longer time scales is one of the most important conditions to ensure the stability and security of IES energy supply. However, as the sequence length of the predicted time series increases, it becomes more difficult to extract valid dependencies from the data. At the same time, there are complex spatio-temporal coupling characteristics of various energy resources in the IES, which makes the construction of long sequence prediction models for natural gas supply very difficult. To solve this problem, we propose a systematic method for long-sequence prediction with natural gas supply in IES based on spatio-temporal causal network of multi-energy. First, the proposed approach uses a causal analysis algorithm to represent the complex coupled spatio-temporal relationships of multiple energy resources in an IES. Then, the natural gas time-series are decomposed through the Variational Modal Decomposition (VMD), to reveal its inherent trend and cyclical characteristics. Finally, the long-sequence prediction model is proposed by fusing the attention mechanism into Graph Convolutional Neural Network (GCN). Attentional mechanisms are introduced to obtain important long-term dependencies in the long sequence prediction process. We use a integrated energy dataset from Spain to test the validity and superiority of the method. The results show that the proposed model in this paper improves the Root Mean Square Error (RMSE) by at least 9.5% and reduces the Mean Absolute Error (MAE) by at least 14.5% compared to several baseline models.

Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system

This paper describes a decentralised smart grid system containing renewable energies, storage systems and distributed generation with human control and intervention. The importance of each element and the interaction between them leads to think about a decision-making strategy. In fact, the integration of a Photovoltaic Panel (PVP) is used due to its availability and its participation in the carbon emissions reduction. Also, a battery is required to fill a power gap or absorb extra generated energy. Moreover, an optimal sizing is needed to get an efficient system with minimum cost. Also, an energy management strategy (EMS) is essential to ensure the power resources scheduling in order to keep a continuous equilibrium supply-demand of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. In the first part, the proposed smart grid optimal sizing is determined under real weather data (solar radiation) of the city of Sousse, Tunisia, using the Hybrid Optimization of Multiple Energy Resources (HOMER) software technique. This approach is chosen thanks to its simplicity, effectiveness, and high precision compared to traditional techniques. In this paper, several configurations (Grid, (Grid-battery), (Grid-PVP), (Grid-PVP-battery)) are studied. The obtained results prove that the (Grid-PVP-battery) system configuration is the most efficient and economical solution. In the second part, a robust energy management strategy (EMS) is proposed for two smart grid configurations (grid-battery, grid-PVP-battery). This strategy is based on Fuzzy Logic Control (FLC) thanks to its non-linear modelling and its ability to make decisions relating to energy management. The primary goal of the suggested (EMS) is to ensure the energy resources scheduling in order to keep a continuous equilibrium among the production and consumption of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. As input data, (FLC) used time-varying price electricity (Price (t)) to solve an instant decision problem by choosing, at each instant, the optimal energy source (which provide electricity at the cheapest price possible). The obtained results, carrying out Matlab simulation, prove the efficacy of the proposed strategy, not only, in the energy resources scheduling to meet the load, but also, for the system cost reduction since the PVP has been used as much as possible since it is inexpensive relative to the costs of battery capacity and the grid.

Imaging Spin-Orbit Excitation of Yttrium through High-Energy Collisions with Rare Gas Atoms.

The energy required for spin-orbit excitation plays a critical role in understanding translational-to-electronic energy conversion, particularly in chemical reactions involving changes in spin states. This is particularly important for transition metal atoms possessing d-orbitals, which result in multiple spin-orbit split energy levels at low energies. The accurate identification and characterization of spin-orbit transitions in such species require advanced experimental techniques and theoretical support. In this study, the spin-orbit excited collisions of Y(2D3/2) with rare gas atoms Ne, Ar, and Kr leading to Y(2D5/2) were observed using laser-ablated crossed-beam and time-sliced ion velocity mapping imaging techniques. Through a comparison of the forward angular distributions of Y(2D3/2) to the backward and sideway scattering distributions of Y(2D5/2) from elastic and inelastic collisions of Y(2D) with rare gas atoms, this study reveals that the spin-orbit electronic excitation occurs with high collision energy and low impact parameters from backward and sideway collisions. The effectiveness of the spin-orbit excitation process is strongly dependent on the collision energy or temperature, suggesting that energy requirements of the process have to be considered in chemical reactions involving changes in spin states.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Assessing the 3E performance of multiple energy supply scenarios based on photovoltaic, wind turbine, battery and hydrogen systems

Supplying buildings with cost-effective, eco-friendly and reliable energy by hybridizing sustainable renewable energy systems (RESs) and energy storage methods is becoming increasingly interesting. This study investigates the energy, environmental and economic (3E) effectiveness of three energy supply scenarios (ESSs) designed to meet the electricity and thermal needs of a typical residential building under the Mediterranean climate of Morocco. The first scenario aims to directly supply the energy produced by two RESs, namely photovoltaic and wind systems, to the building and inject the surplus electricity into the grid. In contrast, in the second scenario, the excess solar and wind energy is stored in batteries, while in the third one a proton exchange membrane electrolyzer is used to produce hydrogen and store it for later use through fuel cells. To examine the performance of these scenarios, numerous software, such as EnergyPlus and PVsyst, as well as numerical models are used. The findings indicate that the second and third scenarios can achieve an energy coverage ratio of up to 100 %, whilst the first scenario reaches lower ratio of 52.56 %. Environmentally, the first scenario exhibits the best outcomes with a carbon payback period of one year and a reduction in carbon emissions of 18.21 tCO2e/year. On the other side, the carbon payback period and carbon emissions reduction are 1.66 years and 15.13 tCO2e/year for the second scenario, and 1.14 years and 14.82 tCO2e/year for the third scenario. Economically, the first scenario reveals the best economic findings, followed by the second one, while the third scenario is not economically feasible. The obtained simple payback period values for the first, second and third scenarios are 5.80, 7.89 and 16.30 years, whereas their levelized cost of energy shares are 0.05, 0.09 and 0.21 $/kWh, sequentially. The outcomes of this research significantly contribute to the field by providing crucial information for building owners and professionals to help them in discerning the most efficient ESSs in the 3E prospects, and hence improving their decision-making and implementation processes.

A deep generative model for selecting representative periods in renewable energy-integrated power systems

The extensive integration of renewable energies into power systems has led to a challenging and computationally demanding scenario for the system planning. This is due to the increased number of time series involved and the greater complexity of time series data integrated into power systems. In this paper, a new deep learning-based time aggregation method is proposed for selecting representative periods for renewable energy-integrated power system datasets where multiple variable energy resources are present. Relying on the capabilities of deep generative models, especially Generative Adversarial Network (GAN), the proposed method selects a representative period, including one or more representative days, obtained from multiple time series with spatio-temporal correlations among them. The proposed approach contains the Long Short-Term Memory (LSTM) Network in both generator and discriminator parts. Also, it includes a loss term, specifically designed for clustering tasks, in the minimax objective of the vanilla GAN loss function to enhance the clustering performance in the latent space. Furthermore, the learning rate of the proposed model, as the most important parameter of the learning algorithm, is adaptively fine-tuned during the training process, based on the training error, to enhance its learning performance. Two real-world test cases with various datasets and time series are used for data-based and model-based evaluations. Results obtained on these two real-world test cases confirm the superiority of the proposed model with respect to the state-of-the-art conventional and deep learning-based models, obtaining the performance improvement in the range of [38.55 %, 46.14 %] in terms of Mean Absolute Percentage Error (MAPE), in the range of [45.75 %, 70.16 %] in terms of Root Mean Squared Error (RMSE), and in the range of [65.81 %, 74.58 %] in terms of Mean Absolute Error (MAE). The proposed model can significantly enhance the tractability and scalability of the planning problem of renewable energy-integrated power systems, facilitating renewable energy integration into power systems which is a key issue for net zero transitioning of communities.

The application and challenge of energy router in energy internet

AbstractThe energy internet has emerged as a promising area of research in power systems with distributed generation. Similar to an internet router to connect and switch networks, the energy router within the energy internet plays a crucial role to integrate and distribute the energy flow. This paper provides an overview of the application and challenges associated with energy routers in the energy internet. Firstly, the ability of energy routers to bring together multiple energy sources and information is highlighted. Secondly, the discrepancies in power structure when energy routers are utilised in different voltage‐level power grids are discussed. Besides, the roles, structures, and design requirements of energy routers and how they interconnect with the power system, as well as some practical examples of energy router are delved. Finally, the main challenges faced by energy routers are summarised.

Low-potential anodic electrochemiluminescence of terbium metal-organic frameworks for selective microRNA-155 detection

High excitation potential is recognized as a harmful factor for the biological activity of biomacromolecules, such as proteins and nucleic acids, in electrochemiluminescence (ECL) biosensing. Developing low-potential ECL luminophores is vital for improving ECL accuracy in actual sample sensing. In this work, based on porous metal-organic framework (MOF) structure with multiple active sites and energy transfer between the excited ligands and Ln nodes, we designed a series of Ln-MOFs and observed ECL emission at low potential, providing a novel method to realize low-potential ECL. The MOF nanoemitters were prepared using 1,3,5-tri (4-carboxyphenyl)benzene ligand and several lanthanide ions as nodes through mild hydrothermal reaction. Interestingly, strong ECL emission at +0.75 V of peak potential was observed in the ECL-potential curve of Tb-based MOF using 2,2′,2″-nitrilotriethanol as coreactant, which was beneficial for reducing background interference in biosensing, and this ECL emission was attributed to the energy transfer between Tb and excited ligand. This low-potential ECL was then applied to construct an ECL biosensor with newly developed Cas12a-based method for selective detection of microRNA-155 without the help of strand displacement or reverse transcription. For this ECL system, the limit of detection was 0.78 nM, and the overall detection time was 2.5 h. The Ln-MOF nanoemitter provides a robust ECL platform to selectively detect various targets by integrating new bio-related techniques.

Role of multiple energy sources under carbon neturality goals, income and energy consumption in transition economies: A joint case study between China and Uzbekistan

This study critically reviews policies addressing the role of multiple energy sources in achieving carbon neutrality goals, with a focus on income and energy consumption in transition economies, specifically China and Uzbekistan. As both nations undergo significant economic transitions, their energy consumption patterns are evolving in response to modernization and policy changes. Utilizing data from 2012 to 2022, this study examines the consumption of various energy sources, including coal, wood, electricity, and liquefied natural gas (LNG), within the context of these countries' carbon neutrality objectives. Our findings reveal that income levels significantly influence energy consumption behaviors, with low- and high-income households in both China and Uzbekistan exhibiting distinct preferences for energy sources. The study integrates social survey data, economic variables, and policy analysis to explore how energy consumption is shaped by income disparities, fluctuating prices, and environmental goals. By drawing on historical contexts and previous scholarly works, this research provides a comprehensive understanding of the complex interplay between income, energy use, and policy in transition economies. The findings offer valuable insights for policymakers aiming to develop sustainable energy strategies that align with carbon neutrality goals while addressing the unique challenges of income disparity and energy consumption in China and Uzbekistan.

Quantitative assessment methods and qualitative evaluation system for the complementary characteristics of multi-energy

Assessing and analyzing the complementary characteristics of renewable energy (RE) is crucial for designing, operating, and optimizing multi-energy complementary systems (MECSs). However, unified and precise quantitative descriptions of the complementary and stability characteristics among various energy outputs in MECSs have lacked attention and research. Here, this study innovatively proposed a mathematical model for the multi-energy complementarity index (MECI), which considers the complementarity rates of multiple energy outputs during zero and non-zero output periods, and a mathematical model for the multi-energy volatility index (MEVI), which accounts for fluctuation thresholds and the overall volatility of output processes. An evaluation system for multi-energy complementarity characteristics qualitative analysis has been established. The natural output processes of RE at three MECSs in China were applied in the case calculations and verification. Results show that the hydropower rated discharge (Qrating) has a significant negative correlation with MECI, with the MECI decreasing by an average of 0.0046 for every 5 m³/s increase in Qrating. The relationship between the Qrating and MEVI shows an overall negative correlation with local fluctuations. Notably, The MECI of the BeiPan River MECSs exhibits significant seasonal characteristics, with the MEVI in summer (0.378) and autumn (0.395) higher than those in spring (0.132) and winter (0.160), closely related to the natural seasonal variations of the three energy sources: water, wind, and solar. We believe that the study can assist in evaluating and making decisions on the multi-energy complementarity characteristics of RE bases in the future, making a significant contribution to achieving dual carbon goals.

Extremely acidic proteomes and metabolic flexibility in bacteria and highly diversified archaea thriving in geothermal chaotropic brines.

Few described archaeal, and fewer bacterial, lineages thrive under salt-saturating conditions, such as solar saltern crystallizers (salinity above 30% w/v). They accumulate molar K+ cytoplasmic concentrations to maintain osmotic balance ('salt-in' strategy) and have proteins adaptively enriched in negatively charged acidic amino acids. Here we analysed metagenomes and metagenome-assembled genomes from geothermally influenced hypersaline ecosystems with increasing chaotropicity in the Danakil Depression. Normalized abundances of universal single-copy genes confirmed that haloarchaea and Nanohaloarchaeota encompass 99% of microbial communities in the near-life-limiting conditions of the Western-Canyon Lakes. Danakil metagenome- and metagenome-assembled-genome-inferred proteomes, compared with those of freshwater, seawater and solar saltern ponds up to saturation (6-14-32% salinity), showed that Western-Canyon Lake archaea encode the most acidic proteomes ever observed (median protein isoelectric points ≤4.4). We identified previously undescribed haloarchaeal families as well as an Aenigmatarchaeota family and a bacterial phylum independently adapted to extreme halophily. Despite phylum-level diversity decreasing with increasing salinity-chaotropicity, and unlike in solar salterns, adapted archaea exceedingly diversified in Danakil ecosystems, challenging the notion of decreasing diversity under extreme conditions. Metabolic flexibility to utilize multiple energy and carbon resources generated by local hydrothermalism along feast-and-famine strategies seemingly shapes microbial diversity in these ecosystems near life limits.

Energy Scheduling in the Multiple Energy System with Optimal Operation of the Responsive Loads

Energy Scheduling in the Multiple Energy System with Optimal Operation of the Responsive Loads

Enhanced integrated energy system planning through unified model coupling multiple energy and carbon emission flows

Integrated energy system plays a crucial role in achieving low carbon emissions while supplying multiple loads. As the energy Internet continues to evolve, integrated energy system, acting as a cell-level prosumer, exhibits intricate coupling characteristics between multiple energy flows and carbon flows. This paper presents a flexible planning method based on a unified energy-carbon coupled operation model. It can realize blank-paper planning under multiple energy-carbon flow constraints, that is, jointly optimize equipment configuration and capacity size from scratch. The proposed method combines clustering algorithms and Latin hypercube sampling to generate the operational scenarios for renewable resources and energy loads, enhancing the representativeness and reliability of planning solutions. The results demonstrate that the proposed method can effectively meet the requirements of electricity, cooling and heat loads under different seasonal scenarios. Furthermore, it elucidates the corresponding carbon emission distribution across distinct energy flows of the planned system. Compared with the conventional fixed equipment connected planning method, the more flexible blank-paper planning method can achieve annual economic cost savings of about 7.5 % and reduce annual carbon emissions by 6.7 %.

Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics

Modern agricultural practices encounter challenges related to operational efficiency and environmental effects. This prompts a demand for innovative solutions to foster sustainability in farming while emphasizing the limitations of conventional farming methods. To address these challenges in modern agriculture systems, this research proposes a comprehensive framework for smart farming. The proposed framework comprises of three technology integrations: 1) an efficient integration of renewable energy resources (RERs) with solar panels and battery energy storage systems (BESS), 2) an IoT-based environmental monitoring for precision irrigation, and 3) an android application-controlled precision robotic system for targeted chemical application. The proposed framework investigates a case study on Sharjah, United Arab Emirates (UAE) to explore and analyze optimal scenarios of multiple energy resources. Results demonstrate successful cross-prototype integration through the Blynk IoT platform providing users with a unified interface. Furthermore, the results provide a comprehensive analysis and investigation into the interactions between RERs and the grid across various combinations. The findings indicate the potential of this framework to revolutionize agriculture and thus offer a sustainable, efficient, and technologically advanced approach. It also represents the contribution of a complete solution to modern agricultural challenges presenting tangible results for a promising future in smart and sustainable farming practices.

Engineering Topological States in Nanographenes

Our research focuses on the rational design, deterministic assembly, and detailed investigation of the physical phenomena emerging from quantum confinement effects in graphene nanomaterials. We pursue a highly integrated multidisciplinary program, founded on synthetic bottom-up approaches toward functional materials with precisely defined structure. We control their assembly into hierarchically ordered architectures and evaluate inherent physical properties using modern scanning probe techniques cross multiple length, time, and energy scales. We recently demonstrated the rational design and experimental realization of a graphene nanoribbon (GNR) superlattices that hosts a 1D array of symmetry-protected topological states, thus generating otherwise inaccessible electronic structure.1 This new class of materials can be thought of as an extended form of poly-acetylene wherein highly localized half-filled topological states replace the familiar single occupied p-orbitals along a conjugated π-system. Experimental results and first-principles calculations reveal that the frontier band structure of these GNR superlattices is defined purely by the coupling between adjacent topological interface states and can be tuned all the way from a semiconductor and a metal.2,3 This novel manifestation of 1D topological phases presents an entirely new route to band engineering in 1D materials based on precise control of their electronic topology and is a promising new platform for future studies of 1D quantum spin physics and metallicity in low dimensional carbon nanomaterials. 1. Topological Band Engineering of Graphene Nanoribbons. Rizzo, D. J.; Veber, G.; Cao, T.; Bronner, C.; Chen, T.; Zhao, F.; Rodriguez, H.; Louie, S. G.; Crommie, M. F.; Fischer, F. R. Nature 2018, 560, 204-206. 2. Inducing Metallicity in Graphene Nanoribbons via Zero-Mode Superlattices. Rizzo, D. J.; Veber, G.; Jiang, J.; McCurdy, R.; Cao, T.; Bronner, C.; Chen, T.; Louie, S. G.; Fischer, F. R.; Crommie, M. F. Science 2020, 369, 1597-1603. 3. Robust Metallic Zero-Mode States in Olympicene Graphene Nanoribbons. McCurdy, R. D.; Delgado, A.; Jiang, J.; Zhu, J.; Wen, E. C. H.; Blackwell, R. E.; Veber, G. C.; Wang, S.; Louie, S. G.; Fischer, F. R. J. Am. Chem. Soc. 2023, 145, 15162-15170.