Minimal Performance Degradation Research Articles

A novel tapered quartz tuning fork-based laser spectroscopy sensing

A novel tapered quartz tuning fork (QTF) was designed to enhance its stress magnitude and charge distribution in QTF-based laser spectroscopy, which had a low resonant frequency of 7.83 kHz and a wide fork gap for long energy accumulation time and easy optical alignment. Compared to the reported rectangular QTF, this tapered QTF transfers the maximum stress position from the root to the middle to improve its sensing performance. Furthermore, the unique design eliminates the 90° right angles typically found in standard QTFs, which often lead to undesired “webs” and “facets” during the etching process. This design minimizes performance degradation by reducing the presence of residual unexpected materials. QTF-based laser spectroscopy of quartz-enhanced photoacoustic spectroscopy (QEPAS) and light-induced thermoelastic spectroscopy (LITES) were adopted to verify its performance. Compared with the widely used standard QTF, the total surface charge of the tapered QTF was improved 5.08 times and 5.69 times in QEPAS and LITES simulations, respectively. Experiments revealed that this tapered QTF-based QEPAS sensor had a 3.02 times improvement in signal-to-noise-ratio (SNR) compared to the standard QTF-based system. Adding an acoustic micro-resonator to this tapered QTF-based QEPAS sensor improved the signal level by 97.20 times. The minimum detection limit (MDL) for acetylene (C2H2) detection was determined to be 16.45 ppbv. In the LITES technique, compared to the standard QTF, this tapered QTF-based sensor had a 3.60 times improvement in SNR. The MDL for C2H2 detection was determined to be 146.39 ppbv.

Integration of deformable matrix and lithiophilic sites for stable and stretchable lithium metal batteries

In response to the growing interest in wearable devices, the demand for next-generation wearable devices that can endure various mechanical deformations such as folding and stretching is also increasing. As a result, the development of stretchable batteries, capable of operating under diverse conditions, is regarded as crucial for the advancement of these future wearable technologies. Many current studies on stretchable batteries suffer from limited energy density and complicated fabrication procedures. Thus, the development of batteries that meet both high stretchability and energy density remains challenging due to these factors. Herein, we propose a stretchable and lithiophilic matrix as a host for lithium (Li) metal anodes to realize stretchable Li metal batteries (LMBs), which consists of a polymer matrix embedded with silver nanoparticles (AgNPs). The lithiophilic AgNPs are incorporated both on the surface and within the elastic fiber matrix, providing facile Li nucleation kinetics and an electron-conductive network. Surface AgNPs serve as a primary electron pathway and offer numerous nucleation seeds to facilitate uniform Li electrodeposition. Meanwhile, AgNPs embedded in the matrix provide a sturdy conductive network even under mechanical deformation. Consequently, the structure-forming factors of stretchable lithiophilic Ag-incorporated matrix (SLiM) electrode contribute to enhanced electrochemical properties as a versatile Li metal host. As a proof of concept, the designed all-stretchable LMB with the SLiM electrode demonstrates minimal degradation of electrochemical performance in deformable conditions and confirms the feasibility of an LMB in stretchable application. This work provides insight into stretchable LMBs aimed at both highly deformable and high-energy-density wearable devices.

Effects of Storage Voltage upon Sodium-Ion Batteries

Sodium-ion batteries (SIBs) are gaining attention as a safer, more cost-effective alternative to lithium-ion batteries (LIBs) due to their use of abundant and non-critical materials. A notable feature of SIBs is their ability to utilize aluminum current collectors, which are resistant to oxidation, allowing for safer storage at 0 V. However, the long-term impacts of such storage on their electrochemical performance remain poorly understood. This study systematically investigates how storage conditions at various states of charge (SOCs) affect open circuit voltage (OCV) decay, internal resistance, and post-storage cycling stability in two different Na-ion chemistries: Prussian white//hard carbon and layered oxide//hard carbon. Electrochemical Impedance Spectroscopy before and after storage shows a pronounced increase in internal resistance and a corresponding decline in cycling performance when SIBs are stored in a fully discharged state (0 V), particularly for layered oxide-based cells, illustrating the sensitivity of different SIB chemistries to storage conditions. Additionally, a novel reformation protocol is proposed that reactivates cell capacity by rebuilding the solid electrolyte interphase (SEI) layer, offering a recovery path after prolonged storage. These insights into the long-term storage effects on SIBs provide new guidelines for optimizing storage and transport conditions to minimize performance degradation, making them more viable for commercial applications.

Impact of cold source temperature on transcritical CO2 power cycle: Design point optimization and off-design performance analysis

The transcritical CO2 (T-CO2) power cycle is regarded as one of the most promising alternatives to the steam power cycle in nuclear power plants. However, these plants often operate with transient cold source conditions, leading to deviations from the optimal design point and performance degradation. Therefore, this study examines a double recompression T-CO2 power cycle to optimize the design point pump inlet temperature and investigate part-load performance under cold source fluctuations. Results indicate that the lowest design point pump inlet temperature is optimal for low temperature cold sources and high load conditions to maximize thermal efficiency. When the system operates at 80% load with a cold source temperature of 5oC, the thermal efficiencies at the design point pump inlet temperature of 15oC and 25oC are 58% and 56.93%, respectively. In contrast, a moderate design point pump inlet temperature is better suited for accommodating wide fluctuations in operating conditions. A lower cold source temperature helps mitigate the reduction in thermal efficiency caused by load decreases. Furthermore, reducing the split ratio of low-temperature compressor can enhance thermal efficiency as the cold source temperature increases. These findings provide an effective approach to minimizing performance degradation in the T-CO2 power cycle under off-design conditions.

Enhancing adsorbent performance for direct air capture of CO2 by in-situ amine-grafting of layered double hydroxides

The development of efficient and cost-effective CO2 adsorbents is crucial for carbon capture from ultradilute conditions. Amine-grafted mesoporous materials prepared by silane chemical reactions are well-known CO2 adsorbents with high thermal stabilities, but their capacities for direct air capture (DAC) are usually restricted by low amine loadings and CO2 capture capacity. Herein, we present an in-situ amine grafting method that enables high amine loadings by in-situ grafting initial ultrathin LDHs nanosheets in an organic solvent. For in-situ triamine grafted Mg2Al-CO3 LDH (IN-TRI-LDH) amine loading is boosted to 5.91mmol N g−1 and ultimately achieving a remarkable adsorption capacity of 0.98 mmol g−1 at 25°C and 400 ppm CO2, nearly double that of the amine-LDHs grafted through a conventional two-step process. A further 22 % enhancement of the CO2 capacity is observed under 20 RH%. In addition, IN-TRI-LDH maintains a working capacity of 0.85 mmol g−1 at a low desorption temperature of 70 °C. A 50 adsorption–desorption cyclic test under simulated humid DAC conditions shows minimal performance degradation. Such a sufficient working capacity and low desorption temperature reduces the thermal energy consumption to 2.27 GJ t−1 at a desorption temperature of 40 °C.

Understanding Mechanisms and Reversibility of Anode Degradation in PEM Water Electrolysis

Water electrolysis is an important route toward sustainable industrial production of hydrogen, as well as mitigation of the CO2 emissions associated with current hydrogen production from natural gas. The industrial deployment of clean electrolytic hydrogen will depend on long-term electrolyzer durability under the intermittent operating conditions associated with coupling with renewable sources of electricity. Thus, understanding cell degradation mechanisms is required to develop mitigation strategies and minimize performance degradation over extended periods of operation.Here, we investigate the effect of temperature and intermittent conditions on proton exchange membrane (PEM) water electrolyzer performance over a variety of timescales. Performance losses consisting of both reversible losses and irreversible degradation accelerate quickly during holds at high current densities and low temperatures, reaching high cell voltages within several hours. However, the cell can be returned to near beginning-of-test performance by interrupting these current holds with a variety of intermittent states of operation, such as interruption of the applied current or operation at higher temperatures. Reversible losses can harm the overall cell efficiency without being captured in short-term polarization curves, and may also contribute to stressors causing long-term degradation. The impact of different shut-down and idle states on the catalyst layers and cell degradation will also be discussed. The presented insights could contribute to the development of accelerated stress test protocols as well as performance recovery strategies for long-term operation of PEM water electrolyzers.

Multifunctional daytime radiative cooler resistant to UV aging

Passive radiative cooling presents a promising approach to mitigate global warming and reduce energy consumption. Existing coolers, however, face challenges in practical prolonged adoption due to their unappealing design, susceptibility to environmental pollutants, as well as degradation of polymers by ultraviolet (UV) irradiation from sunlight. This study introduces a multifunctional daytime radiative cooler (MDRC) with a dual-layered structure, combining practical functionality with aesthetic value. Its base layer consists of an Ag–TiO2–Ag thin film stack, imparting vibrant cyan, magenta, and yellow colors, even maintaining visually appealing from various incident angles, up to 75°, to ensure consistent color display. The top layer, comprising S–SiO2 nanospheres with UV resistance rather than polymers, not only forms a superhydrophobic surface with a contact angle of 155.502°, enhancing the self-cleaning capability of the MDRC against contamination and dust, but also renders the MDRC with extraordinary anti-aging capacity. Outdoor experiments has shown that the MDRC maintains operation at temperatures above ambient under direct solar irradiation, achieving a substantial temperature reduction of 6.82 °C compared to the same colored commercial coating. After three months of outdoor exposure, the MDRC demonstrated minimal degradation in performance, with decreases of only 0.29 °C during the day and 0.12 °C at night, underscoring its superior UV-resistant aging properties. This MDRC represents a robust solution for diverse durable outdoor raidative cooling applications, advancing toward real-world utilization.

Unveiling the enhanced hydrogen evolution performance of ternary heterostructures Mo5O14-Y(OH)3-CoMoO4/CF electrocatalysis

The challenge in electrocatalysis lies in finding stable and efficient catalysts for hydrogen evolution, especially those based on non-noble metals. Herein, the Mo5O14-Y(OH)3-CoMoO4/CF (CF stands for cobalt foam) catalyst rich in ternary heterostructures have been successfully synthesized. The catalyst is composed of cumin-like nanoparticles randomly dispersed on microcolumns. This unique structure ensures both its electrocatalytic activity and structural stability. Electrochemical tests demonstrate exceptional water splitting capabilities, with overpotentials of just 40 mV at 10 mA cm−2 (j10) and 219 mV at 1000 mA cm−2 (j1000). Furthermore, the catalyst shows minimal performance degradation after long-term testing, maintaining stable electrochemical performance for 60 h at j10 and 18 h at j800. Surface energy calculations and scanning electron microscopy (SEM) indicate that yttrium(Y) promotes the formation of cumin-like nanoparticles. Density functional theory (DFT) analysis, along with in situ Raman spectroscopy, suggests that the interaction among the three metals forms Mo5O14-Y(OH)3-CoMoO4 ternary heterostructures, effectively reducing the energy barrier for the hydrogen evolution reaction (HER). Notably, the Y sites in the CoMoO4-Y(OH)3 heterostructure exhibit the lowest hydrogen adsorption free energy (ΔGH⁎: −0.31 eV), making them the primary active sites for Mo5O14-Y(OH)3-CoMoO4/CF.

Advanced Nix/MoSx/MOF-2@g-C3N4 carbon nanostructures for the effective eradication of the Methylene blue dye

In this research, we investigated different hybrid photocatalysts: Ni/Mo.S2/MOF-2@g-C3N4, Mo.S2/MOF-2@g-C3N4, Ni.S2/MOF-2@g-C3N4 and Ni/Mo.S2/MOF-2. These photocatalysts were synthesized using a solvothermal method by incorporating Ni.x/Mo.Sx, the heterojunction showed improved separation of photoinduced charges due to disparity in charge transfer. This resulted in a narrower energy band gap and excellent catalytic activity in photodegradation. The Ni/Mo.S2/MOF-2@g-C3N4 heterostructure demonstrated outstanding photocatalytic efficiency, with up to 91% degradation of methylene blue (MB). This significant degradation was driven by the generation of superoxide (O2 •) and hydroxyl (•OH) radicals. The process adhered to pseudo-first-order kinetics and was effectively completed within 90 min of light exposure. Remarkably, the Ni/Mo.S2/MOF-2@g-C3N4 heterostructure displayed excellent stability, with only minimal performance degradation observed after five consecutive cycles. These results underscore the superior stability of the heterostructure. In summary, our research confirms that the Ni/Mo.S2/MOF-2@g-C3N4 heterojunction photocatalyst is a highly efficient material for the effective removal of methylene blue dye.

Healable and Conductive Two-Dimensional Sulfur Iodide for High-Rate Sodium Batteries.

Self-healing functional materials can repair cracks and damage inside the battery, ensuring the stability of the battery material structure. This feature minimizes performance degradation during the charging and discharging processes, improving the efficiency and stability of the battery. Here, we have developed a novel healing conductive two-dimensional sulfur iodide (SI4) composite cathode. This process integrates both sulfur and iodine compounds into carbon nanocages, forming a SI4@C core-shell structure. This cathode design improves electrical conductivity and repairability, facilitates rapid activation, and ensures structural integrity, resulting in a typical Na-SI4 battery with high capacity and an exceptionally long cycle life. At 10.0 A g-1, the capacity of the Na-SI4 battery can still reach 217.4 mAh g-1 after more than 500 cycles, and the capacity decay rate per cycle is only 0.06%. In addition, the cathode exhibits a cascade redox reaction involving S and I, contributing to its high capacity. The in situ growth of a carbon shell further enhances the conductivity and structural robustness of the entire cathode. The flexibility and bendability of SI4@C-carbon cloth make it applicable for flexible electronic devices, providing more possibilities for battery design. The strategy of engineering a two-dimensional self-healing structure to construct a superior cathode is expected to be widely applied to other electrode materials. This study provides a new pathway for designing novel binary-conversion-type sodium-ion batteries with excellent long-term cycling performance.

Development of a high-capacitance flexible supercapacitor with enhanced cycling stability based on nanotubular polyaniline-modified titanium sheet

Nanotubular polyaniline modified titanium (PANI/Ti) sheet was synthesized via one-step chemical polymerization of aniline on a Ti sheet. The structural and morphological investigations of the PANI/Ti sheet were performed using X-ray diffraction, field emission scanning electron microscopy, and X-ray photoelectron spectroscopy. These investigations revealed that a nanotubular PANI network with an average diameter of 37.5 nm was vertically aligned on the Ti sheet. Electrochemical properties of PANI/Ti were investigated via cyclic voltammetry, galvanostatic charging/discharging, and electrochemical impedance spectroscopy in a three-electrode system. The PANI/Ti sheet exhibited an exceptional specific capacitance (Csp) of 1174 mF/cm2 at 2 mA/cm2, which was significantly higher than that of the PANI/GC (199.53 mF/cm2) and the bare Ti sheet (0.87 mF/cm2). The prepared PANI/Ti sheet exhibited a power density of 21.99 mW/cm2 at an energy density of 3.05 mWh/cm2. Moreover, the electrode demonstrated outstanding cycle stability, maintaining 97.62% efficiency over 3000 cycles and exhibited impressive flexibility, with minimal performance degradation even when bent at angles up to 90°. Furthermore, PANI/Ti sheet significantly reduces the solution and charge transfer resistance compared to bare Ti sheet. So PANI-modified Ti sheet will be a good choice for the fabrication of new flexible energy storage as well as electronic devices in the future.

Anti-fouling rotating polymer-based heat exchanger for zero liquid discharge humidification-dehumidification desalination

The objective of this investigation is to assess the performance of various heat exchangers for application in a novel solar-powered zero liquid discharge humidification-dehumidification desalination system. In this study four heat exchangers (HX) having two different flow configurations namely counter flow (cf), cross flow (cr) made up of three different materials namely high-density polyethylene (HDPE), aluminum (Al), and Polypropelyne (PP) were compared in terms of their effectiveness and overall heat transfer coefficient under varying salinity levels (up to 10%) and mixing ratios (0.22–0.45). At a mixing ratio of 0.22 and 0% salinity, PP-HXcr and Al-HXcf exhibited similar effectiveness (∼85%), surpassing that of HDPE-HXcf (∼65%). Despite PP-HXcr's lower thermal conductivity in comparison to Al-HXcf, comparable effectiveness was achieved due to the superior flow distribution in PP-HXcr. Further investigations focused on the impact of salinity on heat exchanger performance. At 3.5% salinity, all heat exchangers experienced a decline in effectiveness and heat transfer coefficient (HTC), with Al-HXcf experiencing a more pronounced decrease compared to PP-HXcr. The higher thermal conductivity of Al-HXcf led to greater salt accumulation, while PP-HXcr demonstrated minimal fouling. As the experiment progressed, fouling increased for all heat exchangers, with the Al-HXcf being practically ineffective at 10% salinity with an effectiveness below 10%. To address the issue of fouling, a rotating cross-flow heat exchanger (RPP-HXcr) was introduced. While the effectiveness of the PP-HXcr drops from 85% to approximately 60% with increasing salinity from 0% to 10%, the RPP-HXcr demonstrates only a marginal decline in effectiveness with increasing salinity. For instance, at mixing ratio of 0.22 when the salinity is increased from 0% to 10%, the effectiveness of RPP-HXcr only drops from 83% to 77%. This exceptional performance was attributed to the continuous contact between the rotating tubes and the incoming feed, effectively preventing fouling and ensuring sustained efficiency. Rotating cross-flow heat exchanger (RPP-HXcr) is introduced and validated as a potentially reliable solution for mitigating fouling, as it demonstrates sustained efficiency and minimal performance degradation across different salinity conditions.

Soft independence guided filter pruning

Filter pruning (FP) is an effective method for reducing the computational costs of convolutional neural networks, and herein, the most critical task involves evaluating the significance of each convolutional filter and eliminating the less important ones while minimizing performance degradation. Most existing FP methods consider only local information, which may prevent them from accurately recognizing the most important filters. To address this limitation, we propose the soft filter independence (SFI) method, which leverages global information to identify the most important filters using their magnitude and correlation information in different functional layers. The SFI criterion measures the replaceability of filters from a global perspective in a network. Filters with low independence can be represented effectively by others, so their information can be accurately conveyed by other filters. In addition, we introduce a novel SFI-based asymptotic pruning ratio, which improves training and pruning stability. Compared to the most advanced FP methods, our method enables CNNs to achieve higher pruning rates and better classification performance.

An Agile Super-Resolution Network via Intelligent Path Selection

In edge computing environments, limited storage and computational resources pose significant challenges to complex super-resolution network models. To address these challenges, we propose an agile super-resolution network via intelligent path selection (ASRN) that utilizes a policy network for dynamic path selection, thereby optimizing the inference process of super-resolution network models. Its primary objective is to substantially reduce the computational burden while maximally maintaining the super-resolution quality. To achieve this goal, a unique reward function is proposed to guide the policy network towards identifying optimal policies. The proposed ASRN not only streamlines the inference process but also significantly boosts inference speed on edge devices without compromising the quality of super-resolution images. Extensive experiments across multiple datasets confirm ASRN’s remarkable ability to accelerate inference speeds while maintaining minimal performance degradation. Additionally, we explore the broad applicability and practical value of ASRN in various edge computing scenarios, indicating its widespread potential in this rapidly evolving domain.

SIM-FED: Secure IoT malware detection model with federated learning

Many IoT devices are presently in use without sufficient security measures. The vulnerability of these devices to malware highlights the necessity for effective methods to identify malicious activity while protecting data privacy. Federated Learning is a collaborative learning technique that can create a fair model while keeping the local data private. To this end, this paper presents the SIM-FED, a novel model for detecting malware in IoT devices. This model utilizes both deep learning and federated learning algorithms. The SIM-FED offers a privacy-preserving solution for IoT malware detection, without sharing data, enhancing security in distributed environments. The model utilizes a lightweight one-dimensional CNN with optimized hyperparameters, reducing preprocessing, and computational overhead. Various federated aggregation strategies are evaluated, and the FedAvg strategy is selected to integrate the results of local models in the proposed model. Moreover, the model's resilience against white-box and black box cyber-attacks is assessed, revealing minimal performance degradation and highlighting its robustness, which is often overlooked in previous studies. The performance of the SIM-FED is assessed by conducting a series of experiments using the IoT-23 dataset. The results show that the SIM-FED outperforms other deep learning and federated learning models in terms of all evaluation metrics, achieving a remarkable 99.52 % accuracy.

What Makes Quantization for Large Language Model Hard? An Empirical Study from the Lens of Perturbation

Quantization has emerged as a promising technique for improving the memory and computational efficiency of large language models (LLMs). Though the trade-off between performance and efficiency is well-known, there is still much to be learned about the relationship between quantization and LLM performance. To shed light on this relationship, we propose a new perspective on quantization, viewing it as perturbations added to the weights and activations of LLMs. We call this approach ``the lens of perturbation". Using this lens, we conduct experiments with various artificial perturbations to explore their impact on LLM performance. Our findings reveal several connections between the properties of perturbations and LLM performance, providing insights into the failure cases of uniform quantization and suggesting potential solutions to improve the robustness of LLM quantization. To demonstrate the significance of our findings, we implement a simple non-uniform quantization approach based on our insights. Our experiments show that this approach achieves minimal performance degradation on both 4-bit weight quantization and 8-bit quantization for weights and activations. These results validate the correctness of our approach and highlight its potential to improve the efficiency of LLMs without sacrificing performance.

A lightweight multi-task learning network based on key area guidance for counterfeit detection

Counterfeit detection traditionally relies on manual efforts, but manual detection efficiency is notably low. The accuracy of deep learning methods is challenging because of the insufficient samples, so it is crucial to allow the model to learn effective representation at a lower training cost. Given the above problems, we proposed a lightweight multi-task learning method that employs an uncomplicated auxiliary task to enhance the main task’s attention and reduce the training sample requirements. A key area guidance algorithm is designed to construct the auxiliary task, disturbing key image areas to generate new samples and training the auxiliary task to recognize the disturbance. This guides the main task in discerning authenticity from these key areas. Additionally, a tailored data preprocessing strategy was designed to improve the method’s performance further. Achieving an impressive 98.8% accuracy in identifying various counterfeiting points, our method outperforms existing advanced methods. Importantly, the method significantly reduces training costs. Even with an 80% reduction in the sample size, the method maintains a 92.1% accuracy, demonstrating minimal performance degradation compared to alternative methods.

Structural effects of asymmetric magnet shape on performance of surface permanent magnet synchronous motors

This study proposes a novel surface permanent magnet synchronous motor (N-SPMSM) structure, which features asymmetric magnets attached to the rotor surface. The N-SPMSM structure exhibits reduced structural complexity and minimal electromagnetic performance degradation. The properties of N-SPMSM are analyzed by comparing its structural complexity (in terms of the shape) and manufacturing complexity and electromagnetic performance [in terms of the cogging–mutual torque ratio and back-electromotive force (EMF) values], with the corresponding values of a ring-type SPMSM (R-SPMSM) and step-skew-type SPMSM (T-SPMSM). The analysis results demonstrate that N-SPMSM has lower shape complexity than T-SPMSM and lower manufacturing complexity than both R-SPMSM and T-SPMSM. The cogging torque reduction and back-EMF performances of N-SPMSM are similar to that of R-SPMSM and T-SPMSM.

Composite aerogel incorporating low temperature phase change microcapsules for enhanced thermal insulation

Cryogenic transportation and storage confront significant challenges from harsh weather conditions, heightened energy consumption, and epidemic situations, compelling the need for the creation of exceptionally efficient thermal insulation materials. To address this demand, a composite phase change aerogel was designed in this study through incorporating low-temperature microencapsulated phase change microcapsules (MPCM) into a cellulose nanofiber/polyvinyl alcohol (CNF/PVA) system. The MPCM, consisting of a polyurethane-acrylate (PUA) shell and an n-tetradecane core, exhibited excellent encapsulation performance with leak-proof capability. Remarkable low-temperature phase change energy storage properties were observed, including a phase change temperature of approximately 6 °C and an impressive phase change enthalpy of 112 J/g. The MPCM also demonstrated stability during successive heating–cooling repetitions, maintaining its heat storage capacity and morphology for at least 300 cycles. These exceptional thermal characteristics endowed the resulting aerogel with effective thermal insulation and temperature retardation abilities. Meanwhile, the integration of CNF/PVA as the matrix in the composite aerogel led to minimal degradation of thermal storage performance compared to pure MPCM. Moreover, the addition of MPCM significantly enhanced the compressive strength, reaching 5.6 times that of the neat CNF/PVA aerogel. The composite aerogel showed a notably low density of 0.165 g/cm3 and could be reshaped through heating. This work provides a simple yet effective idea for designing bulk materials with low-temperature phase change capabilities, offering promising prospects in the field of thermal insulation.

Improved chaos grasshopper optimizer and its application to HRES techno-economic evaluation

Current political and economic trends are moving more and more toward the use of renewable and clean energy as a result of rising energy use and diminishing fossil fuel supplies. In this paper, an improved chaos-based grasshopper optimizer used for techno-economic evaluation in integrated green power systems is investigated. The integrated system consists of a fuel cell system, a wind farm, and solar energy. The integrated solar, wind, and hydrogen fuel cell architectures increase the effectiveness and electrical output of the system while needing less energy storage in structures that are unconnected from the grid. The grasshopper optimization technique and chaos theory have been combined to create the suggested chaotic grasshopper optimizer in this study. The performance, precision, and robustness of this optimization were then assessed, using four benchmark tasks. The ICGO model is utilized to assign suitable ratings to all system devices, thereby guaranteeing the attainment of optimal performance and efficiency. The Net Present Cost (NPC) analysis revealed that the ICGO algorithm attained the lowest minimum NPC value of 274.541E4 USD and the highest maximum NPC value of 311.94E4 USD. The average NPC value of the ICGO algorithm (289.176E4 USD) was found to be comparable to the other algorithms examined in the study. These findings indicate that the ICGO algorithm outperformed other optimization algorithms in minimizing the cost of the renewable energy system. The chaotic grasshopper optimizer can handle several targets, restrictions, and variables with ease, and the results demonstrate that it is substantially more efficient and precise than standard optimization techniques. It is also quite durable, with minimal performance degradation as compared to the benchmark solutions. This study demonstrates the effectiveness of the chaos grasshopper optimizer as an HRES technique.