Load Profile Research Articles

Torque Loss, Survival, and Strain Distribution of Implant-Supported Prostheses with Zirconia and Cobalt–Chromium Hybrid Abutments

Background and Objectives: The manufacturing of single crowns using hybrid abutments is an alternative that may be interesting in clinical practice, combining the advantages of the different materials used in a personalized design for each case. The purpose of this in vitro study was to evaluate the torque loss, survival, reliability, failure mode, and strain distribution of implant-supported prostheses with zirconia (Zir) and cobalt–chromium (Co-Cr) hybrid abutments. Materials and Methods: Abutments were milled by CAD/CAM and divided into two groups according to the materials used, Zir and Co-Cr, and cemented on titanium bases screwed to dental implants. Monolithic zirconia crowns were cemented on the abutments. The implant/abutment/crown sets were subjected to thermomechanical cycling (n = 10) (2 Hz, 140 N, 1 × 106 cycles, immersed in water at 5–55 °C) to evaluate the torque loss. The single load to fracture test (SLF) was performed to design the loading profiles (light, moderate, and aggressive) of the step-stress accelerated life testing (SSALT) (n = 21) to evaluate the survival and reliability. The representative fractured specimens were analyzed under optical and scanning electron microscopy. The digital image correlation (DIC) (n = 1) was performed using specimens embedded in polyurethane resin models that received static point loading, and the strain distribution was analyzed. Results: There was no difference in torque loss, survival, or reliability between zirconia and Co-Cr abutments. An analysis of the fractured surfaces showed that the abutments presented the same failure mode, where the fracture probably started in the titanium base/screw. The zirconia abutment model presented only compressive strains around the implant, while the Co-Cr abutment model showed tensile and compressive strains in the middle of the implant; however, all strains were within the clinically acceptable limits. There was a strain concentration in the titanium base close to the implant platform for both groups. Conclusions: Zirconia and Co-Cr hybrid abutments presented similar torque loss, survival, reliability, and failure modes, but the abutment material influenced the strain distribution around the implant. The titanium base screw was the weakest link in the system.

Study of a Power Supply System Operation Based on Methane Generator Supported by an Energy Storage System, to Cover High Fluctuation Electrical Loads: Greenhouse Case Study

The operation of a greenhouse requires the consumption of high amounts of energy. The problem is strengthened by the energy crisis that characterizes our days. This article studies an energy management strategy based on the utilization of methane generators (MG) for energy saving in greenhouses. The management strategy includes covering the energy needs of a greenhouse unit (GHU) by directly injecting the energy produced by the generator units. If the energy produced from MG cannot meet the energy needs of the GHU, an energy storage system (ESS) or a backup (in case ESS is unavailable) is used to support the load, covering the gap between production and consumption. In case of excess energy, priority is given to charging the ESS, and the possible presence of excess energy is led to the network. The main objective of energy management is the optimal design of the MG-ESS system to minimize the possibility of energy excess and deficit. The analysis presented is based on actual GHU energy demand. The special feature of this load is the existence of strong daily fluctuations in energy consumption, especially during the summer months. This study presents a methodology for managing such loads with the main objective of maximizing the operating efficiency of MG. Briefly, this article brings a new look to the existing literature in the following areas: (I) a methodology for the efficient operation of methane generators in combination with ESS to cover both mild and intense power fluctuations has been developed, (II) The mapping of the load profile and its division into zones of different power levels providing the possibility of choosing different sizes of generators that will operate at their optimal power has been achieved and (III) a techno-economically optimal ESS to support the generators in meeting load needs and absorbing excess energy has been designed. The analysis based on the use of three different MGs in the three mapped load zones that will operate at constant power due to their support by an ESS led to the identification of the appropriate combination of MG-ESS for the autonomous operation of the GHU. The preliminary economic evaluation of the optimal MG-ESS combination based on LCOE showed that the proposed system (LCOE = 0.216 – 0.279) is competitive with the existing system (LCOE = 0.392) responsible for covering the load’s energy demand.

Nanostructured Lipid Carrier-loaded In Situ Gel for Ophthalmic Drug Delivery: Preparation and In Vitro Characterization Studies.

Nanostructured lipid carriers (NLCs) are explored as vehicles for ophthalmic drug delivery owing to their better drug loading, good permeation, and satisfactory safety profile. The purpose of the study was to fabricate and characterize an in situ ocular gel of loratadine as a model drug based on NLCs to enhance the drug residence time. NLCs were fabricated using the microemulsion method in which solid lipid as Compritol 888 ATO, lipid as oleic acid, surfactant as Tween 80, and isopropyl alcohol as co-surfactant as alcohol were used. Based on the evaluation of formulation batches of NLCs, the optimized batch was selected and further utilized for the formulation of in situ gel containing Carbopol 934 and HPMC K15M as gelling agents, and characterized Results: The optimized NLCs of loratadine exhibited entrapment efficiency of 83.13 ± 0.13% and an average particle size of 18.98 ± 1.22 nm. Drug content and drug release were found to be 98.67 and 92.48%, respectively. Excellent rheology and mucoadhesion were demonstrated by the loratadine NLC-loaded in situ gel to enhance its attachment to the mucosa. NLC-based in situ ocular gel showed the desired results for topical administration. The prepared gel was observed to be non-irritating to the eye. The optimized NLC-based in situ gel formulation presented better corneal retention and it was found to be stable, offering sustained release of the drug. Thus, the joined system of sol-gel was found promising for ophthalmic drug delivery.

Hourly electrical load estimates in a 100% renewable scenario in Italy

The study of the impact of the zero-emissions scenarios of several countries on the electrical demand is relevant to analyse the feasibility of the sustainable energy transition. This paper presents a structured method to assess the effect of the 100% renewable scenario on the hourly electrical load profile of a country taking Italy as reference case study. The hourly discretization is a fundamental approach to evaluate the contribution of the intermittent renewable sources during the day and the proposed methodology can be easily applied to several countries’ scenarios. Numerous decarbonization scenarios consider the adoption of electricity in several sectors. Consequently, the primary energy reduction (40% in the scenario considered) is usually accompanied by a relevant increase of the annual electricity demand (94%-124%). In this paper, each sector’s contribution is estimated separately, and the results show demand peaks above 100 GW and a baseload above 50 GW, which is more than double that of recent years. Electricity consumption is higher in the colder months and hydrogen and synthetic fuel production impacts significantly on the total electricity demand (26%-32%). The results of this work will be used to verify the feasibility of net zero scenarios with hourly discretization in further research analysis.

Design and simulation of hybrid renewable energy system integration of micro grids using Simulink

This research explores the novel design and simulation of a hybrid renewable energy system integrating photovoltaic (PV) panels, wind turbines, and a diesel generator backup to address the energy demands of an independent hostel in Nottingham, United Kingdom. The system prioritizes minimizing long-term costs and emissions while maintaining energy reliability. A detailed load profile was developed to optimize battery performance, reduce reliance on diesel, and extend battery lifespan, thereby lowering operational costs. Voltage source control and load profile modeling were implemented and evaluated using MATLAB R2023b. Simulation outcomes demonstrated a significant reduction in the diesel generator's runtime, leading to lower emissions. Additionally, the integrated energy management system ensures safe battery operations, enhancing system efficiency, sustainability, and overall performance. This research underscores the potential of optimizing renewable energy integration within microgrids using Simulink.

Optimizing Distribution Transformer Ratings in the Presence of Electric Vehicle Charging and Renewable Energy Integration

Aims and Background: A re-evaluation of distribution transformer ratings is necessary to ensure efficient and reliable operation due to the significant impact of the growing number of electric vehicles (EVs) on load dynamics. The objective of this study is to optimize the rating of the distribution transformers to accommodate the year-round demand for electric vehicle charging while minimizing expenses and no-load losses. The performance and lifespan of transformers under dynamic hourly loads are evaluated by analysing real-world EV charging data, load profiles, and transformer parameters. Using state-of-the-art simulation, real-world data analysis, and optimization algorithms, this study optimizes the transformer distribution ratings under the integration of EV charging and renewable energy. Objectives and Methodology: Dynamic hourly load changes can be captured by analysing realworld electric vehicle charging data in conjunction with seasonal load profiles. In order to maximise transformer lifetime and minimise losses, convex optimisation is subjected to Karush-Kuhn- Tucker (KKT) conditions. Transformer performance is also assessed by using energy storage devices and solar energy simulations. To determine the impact of various electric vehicle charging scenarios on thermal stress and transformer ageing, sensitivity analyses are performed. Results and Discussion: Transformers with a 10% higher rating can manage maximum electric vehicle charging loads with a 15% slower loss of life acceleration, according to our models. Transformer performance is further optimised with the integration of solar energy and energy storage systems, which improves load control and reduces operational costs by up to 20%. Conclusion: Using a convex optimisation framework and the Karush-Kuhn-Tucker (KKT) criteria, the study achieves a 95% accuracy rate in predicting hourly load fluctuations.

Volt/VAr Regulation of the West Mediterranean Regional Electrical Grids Using SVC/STATCOM Devices With Neural Network Algorithms

ABSTRACTReactive power compensation is now a challenging issue in maintaining adequate power quality and improving the performance of the transmission system. Many researchers have focused on the behavior of voltage in the unbalanced state, and the majority of the studies have been done with STATCOM. In this study, the increasing power demand in power grids and the challenges associated with maintaining power quality and stability are discussed. The power system in the western Mediterranean region of Turkey is modeled with the DigSILENT Power Factory program using real data. It highlights the importance of reactive power compensation and introduces FACTS devices as a solution to control power factors and reactive power in the grid. In the realistic model, voltage and reactive power regulation is achieved by using SVC and STATCOM devices to ensure voltage stability. A load flow and short circuit analysis is performed on the designed transmission system for a number of critical scenarios. The modeled power system is optimized for the size and location of the FACTS devices by applying genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms to the selected busbars of the FACTS devices, a strategy designed to significantly reduce system losses. The load values used in the heuristics differ from those used in other studies in that they use a realistic load profile that varies randomly and instantaneously over the long term, as opposed to a fixed load profile. All the comparative results show that the STATCOM controller can maintain the most significant reduction in technical losses and excellent performance in controlling the reactive power response under various outages. The effectiveness of the proposed methodology has been validated in various outage scenarios, and it has been shown that it will reduce the total cost of electricity generation in the western Mediterranean region on an annual basis. Considering the results of the Turkish National Transmission System, increasing voltage sensitivity and reactive power control can be beneficial in reducing power transmission costs and maintaining the balance between generation and consumption.

Benefits of Using Functional Joint Coordinate Systems in In Vitro Knee Testing.

To measure knee joint kinematics, coordinate systems (CS) must be assigned to the tibia and femur. Functional CS have been shown to be more reproducible than Anatomical. This study aims to quantify the benefits of using Functional CS in in vitro testing. Seven cadaveric knee joints were loaded in a 6-Degree of Freedom (DOF) joint simulator. Anatomical CS were established for each joint and Functional CS were calculated based on joint kinematics during passive motion. Loading profiles were applied to the knee joints using different CS definitions. Resulting kinematics and kinetics were obtained to quantify the 1) reduction in intra-knee kinematic response variation, 2) reduction in kinematic cross-talk, 3) reduction in inter-knee kinematic response variation, and 4) improvement in force control performance, when using Functional CS compared to Anatomical. Functional CS, compared to Anatomical, 1) significantly reduced intra-knee kinematic response variation across 12 combined loading conditions for nearly all DOF, 2) significantly reduced kinematic cross-talk during anterior-posterior, varus-valgus and internal-external rotation laxity testing across many DOF, 3) significantly reduced inter-knee kinematic response variation for all DOFs over a gait profile and combined loading conditions, and 4) significantly improved Anterior-Posterior and Varus-Valgus force/torque control performance during dynamic loading profiles. The advantage of using Functional CS for in vitro testing has been demonstrated across all considered domains. Functional CS should be used when performing in vitro knee joint testing.

Optimal integration of photovoltaic sources and capacitor banks considering irradiance, temperature, and load changes in electric distribution system

This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizing the placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks (CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltage stability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects. The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE 69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms established methods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stable voltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach to enhancing both technical performance and operational reliability in distribution systems. Additionally, the consideration of temperature effects on PVRES efficiency adds depth to the study, making it a valuable contribution to the field of power system optimization.

Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method—Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models—exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)—are developed, each reflecting a different price elasticity of demand. These models are integrated with a flexible elasticity matrix to assess the dynamic consumer response to fluctuating electricity prices. The study evaluates four operational scenarios, focusing on grid participation, DER utilization, and the impact of real-time pricing (RTP), time of use (TOU), and critical peak pricing strategies. Quantitative results demonstrate the significant cost-saving potential of integrating DRPs with microgrid operations. In the optimal scenario, the GRSO achieved a minimum generation cost of 882¥ for the base load profile. Further, when critical peak pricing (CPP) was applied, the generation cost was reduced to 746¥, representing a 15.4% reduction. For a scenario where the grid’s participation was limited, the logarithmic-based demand response model decreased the generation cost to 817¥, while full grid interaction led to higher cost reductions. Additionally, our results show a significant reduction in peak load, with load factor improvements of up to 87.7% across the studied demand profiles. Furthermore, limiting the grid’s upstream power capacity to 30 kW resulted in a 7% increase in generation cost across all cases, confirming the importance of grid participation in reducing operational costs. The GRSO algorithm outperformed traditional metaheuristics in terms of both execution time and convergence, making it a viable solution for real-time microgrid optimization. In conclusion, the proposed GRSO-based framework provides an efficient approach for microgrid cost minimization, achieving up to a 15.4% reduction in operational costs and notable environmental benefits by reducing emissions. This study highlights the importance of dynamic demand response strategies and grid participation for sustainable and cost-effective microgrid management.

Designing EV Charging Energy Hubs to Meet Flexibility Requirements in Smart Grids

Energy hub stations represent a transformative approach to modern energy systems, functioning as flexible nodes within distribution networks. By seamlessly integrating electric vehicles (EVs) and battery energy storage systems (BESSs), these hubs address critical challenges such as grid stress, renewable energy utilization, and peak load management. This study introduces a novel mathematical optimization model designed to maximize the operational flexibility of EV charging stations by transforming them into fully functional energy hubs. The model incorporates key parameters such as energy demands, load profiles, and grid conditions to optimize the sizing and operation of distributed energy resources, including photovoltaic (PV) systems and BESSs. The proposed approach minimizes grid dependency, enabling energy hubs to efficiently operate varying levels of operational flexibility, from full reliance on grid power to complete independence. The results reveal that, by effectively shifting energy usage away from peak periods and leveraging PV generation, energy hubs emerge as a critical component of sustainable energy systems and can independently support approximately 45% of their load, with minimal PV and BESS capacities. It also reveals a direct correlation between higher flexibility levels and increased infrastructure requirements for PV systems and BESSs. The proposed model underscores the critical role of energy hubs in facilitating the global shift toward decarbonization, aligning with contemporary goals for resilient and environmentally sustainable energy ecosystems. This work provides a scalable framework for future energy systems, with significant implications for policymakers, researchers, and practitioners in the fields of renewable energy and sustainable mobility.

Effective and Local Constraint-Aware Load Shifting for Microgrid-Based Energy Communities

The rising energy demand, coupled with increased integration of distributed energy resources (DERs) and fluctuating renewable generation, underscores the need for effective load management within energy communities. This paper addresses these challenges by implementing effective, constraint-aware load shifting within microgrid-based energy communities. Specifically, the goal of this study is to flatten the electrical load profile of a High-Voltage (HV)/Medium-Voltage (MV) power transformer. The load of a central power transformer includes (a) the diverse, fluctuating electrical and thermal demands of buildings within the energy community and (b) the load of the area supplied by the substation excluding the energy community loads. To achieve a flattened load profile, we apply time shifting to both electrical and heating, ventilation, and air conditioning (HVAC) loads of the energy community, allowing for a redistribution of energy consumption over time. This approach entails shifting non-critical loads, particularly those related to HVAC and other building operations, to off-peak periods. The methodology considers critical operational constraints, such as maintaining occupant thermal comfort, ensuring compliance with building codes, and adhering to technical specifications of HVAC and electrical systems and microgrid organized energy communities. Detailed simulations were conducted to prove the effectiveness of this constraint-aware load-shifting approach.

Achieving enhanced stabilization and controlled release of curcumin via cross-linked polydopamine particles.

Development of efficient drug delivery systems remains a critical challenge in pharmaceutical applications, necessitating novel approaches to improve drug loading and release profiles. In this study, a novel method is presented for fabricating crosslinked polydopamine particles (XPDPs) using a water/water Pickering emulsion system. The emulsion is composed of poly(ethylene glycol) and dextran, stabilized by polydopamine (PDA) particles. This method yields XPDPs with a mean particle size of 0.55μm, significantly smaller than PDA particles (1.025μm), resulting in a higher surface area favorable for drug loading. The adsorption mechanism involves electron sharing and covalent bonding between the carrier and drug molecules. The adsorption, release, and drug delivery kinetics of the XPDPs are compared with those of the non-crosslinked PDA particles. The results demonstrate that XPDPs exhibit improved adsorption properties due to their crosslinked structure and increased positive charge due to presence of secondary amines. During a 28-h period, curcumin release from PDA declines from around 80%-40%, while for XPDA, it decreases from approximately 60%-35% as the pH shifts from 7.4 to 5. While PDA particles display a burst release profile, XPDPs show a more gradual and sustained release, attributed to their enhanced structural stability. Molecular simulations were conducted to estimate the solubility parameters, confirming the compatibility between PDA and dextran for effective drug loading.

SARS-CoV-2 Viral Load and Cytokine Dynamics Profile as Early Signatures of Long COVID Condition in Hospitalized Individuals.

The global pandemic caused by SARS-CoV-2 has resulted in millions of people experiencing long COVID condition, a range of persistent symptoms following the acute phase, with an estimated prevalence of 27%-64%. To understand its pathophysiology, we conducted a longitudinal study on viral load and cytokine dynamics in individuals with confirmed SARS-CoV-2 infection. We used reverse transcriptase droplet digital PCR to quantify viral RNA from nasopharyngeal swabs and employed multiplex technology to measure plasma cytokine levels in a cohort of people with SARS-CoV-2 infection. Our study included individuals with long COVID condition and those without, all of whom had at least three nasopharyngeal and plasma samples collected within 55 days after diagnosis of SARS-CoV-2 infection. Individuals affected with long COVID symptoms had delayed viral clearance and lower viral loads at diagnosis compared to those without symptoms. Additionally, cytokine analysis revealed variations in IL-18, MIG, and IP-10 levels, with delayed normalization in individuals affected by long COVID syndrome. Correlation analysis indicated associations between viral load and IP-10 and interrelations among cytokines IL-1β, IL-18, MIG, and IP-10. Our study provides insights into the association between nasopharyngeal viral load, cytokine dynamics, and the development of long COVID syndrome, providing an early signature of this condition.

An integrated geospatial modelling framework of hybrid microgrid sizing for rural electrification planning.

Pursuing rural electrification in developing countries through hybrid generation systems is constrained by a lack of suitable energy modelling tools. Few tools include geographical parameters relevant to capturing specific spatial and socio-economic circumstances. Even less are openly available and find applications for rural areas of developing countries. This work presents an integrated geospatial energy modelling framework based on an extended tool, the GISEle (GIS for rural electrification) model, which aims for a least-cost energy solution. GISEle is an open-source tool supporting rural electrification planning strategies and challenges through optimal hybrid microgrid integration. The developed framework is universally applicable and explains how the extended GISEle tool can be used to become suitable for analysing decentralised hybrid generation systems within the context of rural areas of developing countries. This presented framework includes:•Advancing the approach to proper data collection to better capture local specificities and (future) demand and reporting results in rural areas of developing countries;•Adding the Remote-Areas Multi-energy systems load Profiles (RAMP) to improve load demand assessments, while considering the impact of electrification on growing demand scenarios;•Linking the Soil and Water Assessment Tool (SWAT) model to allow for hydropower sizing in GISEle.

Impact of Load Profile on the Installation of Capacitor Banks in Conventional and Modern Distribution Grids Considering Provision Cost of Reactive Power

Impact of Load Profile on the Installation of Capacitor Banks in Conventional and Modern Distribution Grids Considering Provision Cost of Reactive Power

Bi-level real-time pricing model in multitype electricity users for welfare equilibrium: A reinforcement learning approach

The diverse load profile formation and utility preferences of multitype electricity users challenge real-time pricing (RTP) and welfare equilibrium. This paper designs an RTP strategy for smart grids. On the demand side, it constructs utility functions reflecting user characteristics and uses multi-agents for different user interests. Considering industrial users, small-scale microgrids, distributed generation, and battery energy storage systems are included. Based on supply and demand interest, a distributed online multi-agent reinforcement learning (RL) algorithm is proposed. A bi-level stochastic model in the Markov decision process framework optimizes the RTP strategy. Through information exchange, an adaptive pricing scheme balances interest and achieves optimal strategies. Simulation results confirm the effectiveness of the proposed method and algorithm in peak shaving and valley filling. Three load fluctuation scenarios are compared, showing the algorithm's adaptability. The findings reveal the potential of the RL-based bi-level pricing model in resource allocation and user benefits in smart grids. Innovations in user modeling, model construction, and algorithm application have theoretical and practical significance in the electricity market research.

Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles With High Penetration of Behind-the-Meter Solar

Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles With High Penetration of Behind-the-Meter Solar

Assess the Dilemma between the Use of Conventional and Unconventional Energy in Nigerian Shallow Water Oil Fields

This study focused on a techno-economic modelling, simulation, and analysis approach, which was used to determine the techno-economic and environmental sustainability feasibility of Solar PV-BESS as the proposed case and an outright replacement of the existing fossil fuel power generation technology based on the load profile of the given location of study. The result from the technical analysis presented shows that the study area was able to utilize an annual solar radiation at 4.14 kWh/m2/d via horizontal positioning at 15⁰ inclination, which was optimal enough to meet the electrical power demand at 17,520,000kWh annually of the facility when the number of solar panels was increased to deliver 100% fraction of load, most especially in the low sunny days from May to October. Also, the economic analysis presented showed positive NPV and IRR on the proposed case. Although the initial cost of the proposed is quite high as already associated with renewable energy solutions but showed substantial payback results andoverall cost-benefit ratio at 1. The results showed that the proposed case eliminated 100% of the GHG emissions from the base case, with tremendous benefits for revenue generation from emission trading schemes. The study recommends that NUPRC and its stakeholders should form strategic partnerships with existing local solar panel manufacturing collaborators and manufacturers, in other to promote in-country production of solar PVs to reduce the initial cost of the solar projects.

L-Threonine-Derived Biodegradable Polyurethane Nanoparticles for Sustained Carboplatin Release.

Background and objectives: The use of polymeric nanoparticles (NPs) in drug delivery systems offers the advantages of enhancing drug efficacy and minimizing side effects; Methods: In this study, L-threonine polyurethane (LTPU) NPs have been fabricated by water-in-oil-in-water emulsion and solvent evaporation using biodegradable and biocompatible LTPU. This polymer was pre-synthesized through the use of an amino acid-based chain extender, desaminotyrosyl L-threonine hexyl ester (DLTHE), where urethane bonds are formed by poly(lactic acid)-poly(ethylene glycol)-poly(lactic acid) (PLA-PEG-PLA) triblock copolymer and 1,6-hexamethylene diisocyanate (HDI). LTPU is designed to be degraded by hydrolysis and enzymatic activity due to the presence of ester bonds and peptide bonds within the polymer backbone. LTPU NPs were fabricated by water-in-oil-in-water double emulsion solvent evaporation methods; Results: The polymerization of LTPU was confirmed by 1H-NMR, 13C-NMR, and FT-IR spectroscopies. The molecular weights and polydispersity, determined with GPC, were 28,800 g/mol and 1.46, respectively. The morphology and size of NPs, characterized by DLS, FE-SEM, TEM, and confocal microscopy, showed smooth and spherical particles with diameters less than 200 nm; Conclusions: In addition, the drug loading, encapsulation efficiency, and drug release profiles, using UV-Vis spectroscopy, showed the highest encapsulation efficiency with 2.5% carboplatin and sustained release profile.