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https://repositorio.uft.cl/handle/20.500.12254/2436

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A systematic review of recent developments in IoT-based demand side management for PV power generation
(De Gruyter, 2024-06-21) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Demand-side management (DSM) with Internet of Things (IoT) integration has become a vital path for optimizing photovoltaic (PV) power generating systems. This systematic review synthesizes and evaluates the latest advancements in IoT-based DSM strategies applied to PV power generation. The review encompasses a comprehensive analysis of recent literature, focusing on the key elements of IoT implementation, data analytics, communication protocols, and control strategies in relation to solar energy DSM. The combined results show how IoT-driven solutions are changing and how they might improve PV power systems’ sustainability, dependability, and efficiency. The review also identifies gaps in current research and proposes potential avenues for future investigations, thereby contributing to the ongoing discourse on leveraging smart DSM in the solar energy domain using IoT technology.
An IoT-based intelligent smart energy monitoring system for solar PV power generation
(De Gruyter Mouton, 2023-09-15) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.
Closed-shell d10–d10 mechanochromic [AuPh(CNPh)]n complex: quantum chemistry electronic and optical properties
(Royal Society of Chemistry, 2024-01-31) Mendizabal, Fernando; Cerón, María Luisa; Lara, Dina; Miranda-Rojas, Sebastián
The electronic structure, spectroscopic properties, and solid state chemistry of monomer and dimers of [AuPh(CNPh)] complex were studied at post-Hartree-Fock (MP2, SCS-MP2, and CC2) and density functional theory levels. The absorption spectra of these complexes were calculated using single excitation time-dependent (TD) methods at DFT, CC2, and SCS-CC2 levels. The influences of the bulk are accounted for at the PBE-D3 level, incorporating dispersion effects. The calculated values agree with the experimental range, where absorption and emission energies reproduce experimental trends with large Stokes shifts. The aurophilic interaction is identified as a key factor influencing the spectroscopic and structural properties of these complexes. The intermetallic interactions were found as the main factor responsible for MMCT electronic transitions in the models studied.
Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT
(Elsevier, 2024-12-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.
Design and development of grid connected home automation system for prosumers
(Institute of Electrical and Electronics Engineers (IEEE), 2023-03-23) Patro, Swati P.; Sahoo, Sarat Kumar; Thanarak, Paprita; Yanine, Fernando
Home automation system extend convenience and sophisticated to operate electronic product with in the household. Home automation can be a solution to automating functions like monitoring, controlling etcetera in the home. This paper describes the drives, restraints, opportunities and challenges pertaining to the home automation system market. The grid solar power system is connected to the utility grid where solar power is generated. The power generated if in excess send back to utility grid. Hence the consumer gets compensation as the extra power sent back to the utility grid. Whenever the sunlight, electricity is generated by solar cell, the inverter connected to grid convert the dc power into ac power. The electricity produced is routed to the grid from, where it used to run the various appliances such as air conditioners, washing machine, refrigerator, geyser, induction cooker, pump etc. The excess power at every instant is transmitted to the ground without grid connection and if solar power system is synchronized with the utility grid then return to the grid. It will also determine the feasibility of reducing green gas emission by the use of grid tied PV power system.
Design of an EEG analytical methodology for the analysis and interpretation of cerebral connectivity signals
(Elsevier, 2022-02-20) Córdova, Felisa M.; Cifuentes, Hugo F.; Díaz, Hernán A.; Yanine, Fernando; Pereira, Robertino
The objective of this study is to design an Electroencephalographic (EEG) analytic methodology that allows to develop a variety of analysis and interpretations of brain signals. The initial phase considers the acquisition and filtering of EEG signals, the division into bands in data ranges, and the storage of EEG signals in a cloud data base. Then, an analytical phase considering descriptive, predictive and prescriptive analysis is accomplished. A sequence of analytic intermediate processing steps is done in order to render a graphic visualization of significant correlations between pairs of EEG channels. Pearson correlation is utilized to detect synchronic connectivity through the brain areas. Time series in nearly instantaneous time lapses are treated by using Hilbert Huang Transform. An experimental design by submitting a set of students to an abbreviated version Raven visual test is made providing results in correlation maps of cerebral connectivity
Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy
(Springer Nature, 2024-11-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Consumers in both residential and commercial settings are increasingly interested in reducing their energy consumption, influenced by feed-in tariffs for renewable resources and the recent surge in electricity rates. This study introduces a central control system and a smart power plug utilizing the XBee communication protocol to effectively manage energy usage. Smart energy management systems are employed to measure and optimize power consumption at the consumer premises level. The primary objective of this paper is the design and development of wireless smart plugs capable of assessing various power characteristics and collecting real-time data on individual consumer appliances' power usage. The SEMS setup establishes a Consumer Area Network through an XBee transmitter and receiver node, enabling real-time data collection at the central node for scheduling and prioritizing appliances. Utilizing the SEMS setup, consumer appliance datasets are generated and additional datasets aid in load disaggregation. The system configuration enables wireless data transfer from smart outlets to a central controller. Control instructions derived from data analysis are then used by the system to turn connected devices to the smart plug on or off. Test results indicate that the proposed smart plug accurately assesses power consumption up to eighteen meters away without compromising data integrity. The central controller, guided by a planned user program code, effectively manages multiple plugs based on the test findings. The Smart Energy Management algorithm suggests that employing smart plugs as load controllers results in a significant decrease in energy consumption (0.811 kW min or 0.0134 kWh) when accompanied by the appropriate scheduling algorithm. This technology holds potential in a comprehensive smart energy management system. The data's insights highlight the superiority of the proposed approach compared to current standard practices.
Diagnosing grid's service quality issues: the virtual microgrid and the digitization and innovations in the power distribution grid
(Elsevier, 2024-08-23) Cordova, Felisa; Yanine, Fernando; Carrasco, Sergio; Rother, Hans; Duarte, Vanessa
Power quality issues from small and medium-sized grid-integrated solar photovoltaic systems (PMGD), whose plant capacity is less than or equal to 9 MW, challenge electric power distribution companies like ENEL Distribución S.A. in Chile. To tackle these challenges, leveraging rapidly configurable, scalable, and easily deployable distributed energy resources (DER) through a virtual power plant (VPP) is crucial. This paper introduces a "virtual microgrid," a specific VPP design and functional specification that swiftly diagnoses and resolves power quality issues. Virtual power plants use renewable energy sources, energy storage, and smart energy management to provide ancillary services to the grid. With the expanding PMGD market in Chile and globally, there is an urgent need for deployable DER to energize feeder sub-sections during reliability events and create grid pathways between DER and loads. Thus, DER are essential for enhancing grid flexibility, reliability, and maintaining the stability and integrity of electric supply quality standards. VPPs can reduce high costs associated with utility grid power capacity deficiencies in areas with unstable power supply, enhancing resilience and flexibility without compromising system stability. This study presents a novel approach—currently at the conceptual development stage—for diagnosing service quality issues related to grid supply through the rapid deployment of a configurable and fully scalable virtual microgrid. This solution is particularly relevant for zones in Santiago, Chile, where ENEL Chile faces challenges due to the increasing presence of PMGD—independent solar farms injecting all their production into the grid for profit, as permitted by current Chilean electric law (N°88/2019 of Ministry of Energy). Such practices negatively impact grid service quality standards, which can be mitigated through the deployment of virtual microgrids. The concept of a "virtual microgrid" emphasizes rapid configurability, scalability, and ease of deployment, tailored to address specific grid quality issues. Unlike previous efforts, this approach focuses on seamlessly integrating DER with existing grid infrastructure to quickly address and mitigate power quality issues caused by PMGD.
Editorial: Recent advances in renewable energy automation and energy forecasting
(Frontiers Media S.A., 2023-05-10) Sahoo, Sarat Kumar; Yanine, Fernando; Kulkarni, Vikram; Kalam, Akhtar
Renewable energy sources like solar, wind, and hydroelectric power are gaining popularity as we work towards a more sustainable future. However, their intermittent and often unpredictable nature, creates challenges for the energy industry in terms of being able to ensure continuous electric power generation over regular periods of time. Thus, accurate forecasting of renewable energy output is crucial for their reliable integration into the power grid. In this regard, automation and machine learning have made significant improvements in energy forecasting by enabling more precise predictions of energy output. Advanced algorithms and high-performance computing systems allow for better grid management and increased power generation systems’ efficiency. Automation is also being used for the operation and maintenance of renewable energy systems. Real-time monitoring and control systems enable a rapid response to changes in weather conditions, optimizing energy production. This editorial summarizes recent advancements in renewable energy automation and energy forecasting, which are critical areas for achieving a sustainable energy future. The Research Topic covers areas like machine learning-based energy forecasting, control and optimization of renewable energy systems, and the integration of renewable energy into microgrids as shown in Figure 1. Continued research and development in renewable energy automation and energy forecasting are essential for the transition towards a sustainable energy future.
Exploring the adsorption of five emerging pollutants on activated carbon: a theoretical approach
(Elsevier, 2024-06-06) González-Rodríguez, Lisdelys; Yáñez, Osvaldo; Mena-Ulecia, Karel; Hidalgo-Rosa, Yoan; García-Carmona, Ximena; Ulloa-Tesser, Claudia
The identification and management of contaminants of emerging concern (CECs) in water systems is crucial for protecting public and environmental health. This paper reports a theoretical approach to studying the adsorption of five CECs: Atrazine (ATZ), Caffeine (CAF), Carbamazepine (CBZ), Sulfamethoxazole (SMX), and Ibuprofen (IBU) - onto Activated Carbon (AC). A set of computational methods, including electrostatic molecular potential maps, conceptual density functional theory, Fukui functions, thermodynamic analysis, and tight-binding molecular dynamics simulations, were employed to analyze the electronic/energetic interactions and mechanisms involved in the adsorption of CECs on AC. The theoretical methodology offered valuable predictions on reactivity sites, stability, and binding mechanisms. Results showed that adsorption primarily occurred through non-covalent interactions like π-π electron donor-acceptor interactions, van der Waals forces, and hydrophobic interactions. Thermodynamic properties suggested the adsorption process was spontaneous and exothermic. However, for the AC/SMX system, the Gibbs free energy reveals that adsorption may be unfavorably compared to the other study systems. Molecular dynamics simulations validated the kinetic stability in the following order CAF (0.13 Å)>CBZ (0.23 Å)>ATZ (0.75 Å)> IBU (1.28 Å)>SMX (1.54 Å). This exploratory theoretical study provides a deep understanding of the interactions between AC and five CECs, aiding in the rational design and optimization of AC-based treatment systems for environmental and industrial applications.
Furfural adsorption on V2O5 surface: A combined experimental-theoretical study
(Elsevier, 2024-06-16) Lizana, Ignacio; Schott , Eduardo; Saavedra-Torres, Mario; Hidalgo-Rosa, Yoan; Pecchi, Gina; Karelovic, Alejandro; Zarate, Ximena;
The adsorption of furfural on the V2O5 surface was investigated using experimental and theoretical methods. In situ Diffuse Reflectance Infrared Fourier-Transform Spectroscopy results show the presence of physi- and chemi-sorption phenomena, where trans-furfural is mostly chemisorbed at the beginning of the adsorption process. These results are in agreement with theoretical DFT results, as the most thermodynamically favored configurations corresponds to the chemisorbed trans-furfural (T1) and cis-furfural (C1) with binding energies of −1.83 and −2.05 eV.
Influence of Iodine substitutions on the photophysical properties of rhenium(I) tricarbonyl complexes with pyridine schiff base (PSB-IHB)
(Wiley, 2025-01-08) Morales-Guevara, Rosaly; Páez-Hernández, Dayán; Ancede-Gallardo, Evys; Carreño, Alexander
This theoretical investigation aims to evaluate the influence of iodine substitutions on the phenolic moiety of the pyridine Schiff bases harboring an intramolecular hydrogen bond (PSB-IHB) ancillary ligand within the monocationic. fac-[Re(CO)3(N,N)(PSB-IHB)]+ architecture on photophysical properties, particularly emission range. Optimized structures of two Re(I) tricarbonyl complexes (C1 and C2) were analyzed, revealing a distorted octahedral coordination geometry. Geometric parameters were compared with experimental data from analogous complexes. NBO analysis confirmed the presence of intramolecular hydrogen bonds (IHBs) in both singlet and triplet states, providing significant stabilization. Theoretical calculations predicted three distinct absorption bands for all complexes in dichloromethane, indicating substantial electronic delocalization between the pyridinic and phenolic rings via the azomethine group. These findings underscore the crucial role of delocalization and donor–acceptor interactions in stabilizing Re(I) tricarbonyl complexes and their impact on photophysical properties. Emissions calculated for the C1 and C2 complexes were observed within the range of 632–643 nm. The presence of IHBs was found to be essential for modulating photophysical properties, with emissions attributed to ligand-to-ligand charge transfer transitions.
Intelligent power management system for optimizing load strategies in renewable generation
(Springer Nature, 2024-08-29) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Effectively utilizing renewable energy sources while avoiding power consumption restrictions is the problem of demand-side energy management. The goal is to develop an intelligent system that can precisely estimate energy availability and plan ahead for the next day in order to overcome this obstacle. The Intelligent Smart Energy Management System (ISEMS) described in this work is designed to control energy usage in a smart grid environment where a significant quantity of renewable energy is being added. The proposed system evaluates various prediction models to achieve accurate energy forecasting with hourly and day-ahead planning. When compared to other prediction models, the Support Vector Machine (SVM) regression model based on Particle Swarm Optimization (PSO) seems to have better performance accuracy. Then, using the anticipated data, the experimental setup for ISEMS is shown, and its performance is evaluated in various configurations while considering features that are prioritized and user comfort. Furthermore, Internet of Things (IoT) integration is put into practice for monitoring at the user end.
IoT enabled intelligent energy management system employing advanced forecasting algorithms and load optimization strategies to enhance renewable energy generation
(Elsevier, 2024-08-08) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Effectively utilizing renewable energy sources while avoiding power consumption restrictions is the problem of demand-side energy management. The goal is to develop an intelligent system that can precisely estimate energy availability and plan ahead for the next day in order to overcome this obstacle. The Intelligent Smart Energy Management System (ISEMS) described in this work is designed to control energy usage in a smart grid environment where a significant quantity of renewable energy is being introduced. The proposed system evaluates various predictive models to achieve accurate energy forecasting with hourly and day-ahead planning. When compared to other predictive models, the Support Vector Machine (SVM) regression model based on Particle Swarm Optimization (PSO) seems to have better performance accuracy. Then, using the anticipated requirements, the experimental setup for ISEMS is shown, and its performance is evaluated in various configurations while considering features that are prioritized and associated with user comfort. Furthermore, Internet of Things (IoT) integration is put into practice for monitoring at the user end.
ML models for severity classification and length-of-stay forecasting in emergency units
(Elsevier, 2023-03-02) Candia-Véjar, Alfredo; Moya-Carvajal, Jonathan; Pérez-Galarce, Francisco; Taramasco, Carla; Astudillo, César A.
Length-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital are crucial problems for public and private health networks. An accurate estimation of these parameters is essential for better planning resources, which are usually scarce. Although it is possible to find several works that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the classification of the patient’s post-care destination and by up to 8% in the prediction of LoS in the hospital. This evidence encourages practitioners to use available text to anticipate the patient’s need for hospitalization more accurately at the earliest stage of the care process.
On a pickup to delivery drone routing problem: models and algorithms
(Elsevier, 2022-09-06) Gómez-Lagos, Javier; Rojas-Espinoza, Benjamín; Candia-Véjar, Alfredo
A new variant of the Pickup and Delivery Routing problem is presented. Given a set of customers, facilities, a depot, and a homogeneous fleet of drones, the Pickup to Delivery Drone Routing Problem (PDDRP) aims to find a drone scheduling such that a drone serves the customer’s order from a set of available facilities. Each drone starts in the depot, flies to pickup the customer’s order in a facility, and continues its flight to deliver the parcel to a customer. Then, the drone begins another service, and once its last service is completed, it returns to the depot. The objective is to minimize the makespan associated with the drone fleet. The layer of facilities forcing drones to visit one of them to pickup the parcel makes the problem different from traditional pickup and delivery routing problems. Three mixed-linear programming models are presented to obtain optimal solutions for the problem. The first model is related to the multiple Traveling Salesman Problem (m-TSP), the second is associated with the Parallel Machine Scheduling Problem (PMS), and the third was developed specifically for the new problem. Given the high computational complexity of the PDDRP, a Greedy Randomized Adaptive Search Procedure (GRASP) was designed to find near-optimal solutions when exact approaches cannot achieve (near) optimal solutions. Computational experiments show that a commercial solver could solve only small problem instances. GRASP can find reasonable solutions in a short time when medium and large instance sizes need to be solved. Finally, is shown that some routing problems for delivery, allowing truck-drone collaboration, could be formulated as an extension of PMS.
Photophysical characterization of tetrahydroxyphenyl porphyrin Zn(II) and V(IV) complexes: experimental and DFT study
(Springer Nature, 2023-06-21) Diaz-Uribe, Carlos; Rangel, Daily; Vallejo, William; Valle, Roger; Hidago-Rosa, Yoan; Zarate, Ximena; Schott, Eduardo
Photodynamic therapy (PDT) is a promising technique for the treatment of various diseases. In this sense, the singlet oxygen quantum yield (Φ∆) is a physical–chemical property that allows to stablish the applicability of a potential photosensitizers (PS) as a drug for PDT. In the herein report, the Φ∆ of three photosensitizers was determined: metal-free tetrahydroxyphenyl porphyrin (THPP), THPP-Zn and the THPP-V metal complexes. Their biological application was also evaluated. Therefore, the in vitro study was carried out to assess their biological activity against Escherichia coli. The metal-porphyrin complexes exhibited highest activities against the bacterial strain Escherichia coli. at the highest concentration (175 μg/mL) and show better activity than the free base ligand (salts and blank solution). Results indicated a relation between Φ∆ and the inhibitory activity against Escherichia coli, thus, whereas higher is the Φ∆, higher is the inhibitory activity. The values of the Φ∆ and the inhibitory activity follows the tendency THPP-Zn > THPP > THPP-V. Furthermore, quantum chemical calculations allowed to gain deep insight into the electronic and optical properties of THPP-Zn macrocycle, which let to verify the most probable energy transfer pathway involved in the singlet oxygen generation.
Profiling and characterization of plant-based products consumers in Chile
(Asociación Castellano-Manchega de Sociología (ACMS), 2024-12-14) Campos, Zócimo; Yanine, Fernando; Nikulin Chandia, Christopher; Oyarce Ibarra, Claudia
The consumption trend of plant-based products has increased worldwide due to different reasons, and Chile has been a good example of this trend. Although sustainability, health, nutrition and taste may all seem to be main drivers for flexitarians seeking plant-based food and beverages, to date there is no research that allows characterizing and profiling Chilean consumers of plant-based products. Hence, this work seeks to close this gap. To address this problem, we conducted an online survey of 512 people, mainly focused on the Metropolitan Region and Valparaíso. Using Ward’s method, we identified 3 clusters: (1) Animal friendly, (2) Planet friendly and (3) Healthy lifestyle, where, respectively, animal welfare, care for the environment and concern for health are prioritized.
Rare-earth-based metal–organic frameworks with improved visible-light-harvesting properties: a quantum chemistry study
(Springer, 2023-05-25) Hidalgo-Rosa, Yoan; Saavedra-Torres, Mario; Koivisto, Bryan D.; Treto-Suárez, Manuel A.; Páez-Hernández, Dayán; Zarate, Ximena; Schott, Eduardo
This report proves that improving the visible-light-harvesting properties in rare-earth-based metal–organic frameworks (RE-MOFs) (labelled as RE–UiO-66, UiO = University of Oslo MOFs), with the aim of performing as potential visible-light-driven photocatalysts, is achievable. Thus, the design of MOFs with specific applications, especially those involving sunlight and material interactions, represents a growing field, which has been addressed in the herein work using quantum mechanical tools. We achieved to relate the light absorption properties with the structure in systems Y–UiO-66, Sc–UiO-66 and La–UiO-66, by evaluating the inclusion of well-known electron donor substituents in the structure of the 1,4-benzenedicarboxylate (BDC) linker (i.e. BDC-R, R: –CH3, –OH, –SH and –NH2). The electronic structure and optical properties of Y–UiO-66 were rigorously investigated using computational techniques combining molecular and periodic density functional theory (DFT) calculations. As a remarkable result, it was shown that including the groups –SH or –NH2 in the BDC linker, induced a shift in the absorption bands to the visible region (≥ 400 nm). Hence, a group of new RE-MOFs materials with optimal structural and photocatalytic properties is proposed. This could encourage researchers to prepare these new materials to be tested in photocatalysis, such as cleaving the C–H bond, water splitting or photocatalytic degradation of organic contaminants.
Revisiting the potential-energy surface of CnBe3n+2H2n+22+ (n = 2–4) clusters: are planar pentacoordinate carbon structures the global minima?
(Royal Society of Chemistry, 2023-07-07) Inostroza, Diego; Vásquez-Espinal, Alejandro; Leyva-Parra, Luis; García-Argote, Williams; Cerón, María Luisa; Yañez, Osvaldo; Tiznado, William
Using various exploration strategies, in this study, we investigated the potential energy surfaces (PES) of CBe5H5+ and CnBe3n+2H2n+22+ (n = 2-4) clusters. Previous studies proposed that the planar pentacoordinate carbons (ppCs) were the global minima of these clusters. However, our study identified new putative global minima and competitive isomers, refuting some previous assignments. We employed several methods, including evolutive-inspired stochastic approaches guided by "chemical criteria", and ab initio molecular dynamics simulations at elevated temperatures. Our results showed that the size of the scanned population significantly affected the evolutive method and that constrained or guided procedures showed an advantage in identifying better minima for larger systems. This study demonstrated that using multiple complementary strategies can result in a wider variety of minima in a given energy range. Our findings provide valuable insights into exploring the potential energy surfaces of clusters, mainly medium-sized clusters, which could be the connections between small clusters and nanomaterials.

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