Enhancement Of Efficiency Research Articles

High Efficiency Ultra-Narrow Emission Quantum Dot Light-Emitting Diodes Enabled by Microcavity.

A wide-color-gamut display enableby a narrow emission linewidth facilitates a visually immersive experience akin to the real world. Quantum dot light-emitting diodes (QLEDs) with excellent color purity and high efficiency hold great promise as future candidates for high-definition displays. However, most devices typically exhibit emission linewidths exceeding 20nm, and lack a universal strategy for further enhancing the color purity. In this study, a planar microcavity structure for realizing ultra-narrow emissions is developed by incorporating a distributed Bragg reflector into normal electroluminescent devices. By leveraging the strong optical resonance effect derived from this microcavity structure, red QLEDs are successfully fabricated with an extraordinary full width at half maximum of 11nm in the normal direction, beyond the BT.2020 color coordinates. The fabricated red-microcavity QLEDs exhibit a considerable enhancement in the external quantum efficiency, which increases from 28.2% to 35.6%, together with an extended operating lifetime. The strategy adopted herein will serve as an effective reference for achieving ultra-narrow emission and high-efficiency QLEDs.

Improved Floyd-Warshall Algorithm for Solving Travelling Salesman Problem

The shortest path problem is a fundamental challenge in graph theory, focused on identifying the most efficient routes between nodes in a network. It stands as one of the extensively researched combinatorial optimization problems. In this paper, we explained the fundamental concepts of shortest path algorithms, with a particular emphasis on Floyd Warshall algorithm. We apply the algorithm and showed how this algorithm can be effectively employed to determine the shortest path in various scenarios. Furthermore, this paper addresses real-life applications, particularly focusing on the traveling salesman problem. Individuals often have the option to travel along different routes to reach their destination. However, selecting suboptimal routes can result in time-consuming journeys. To tackle this issue, we apply the Floyd Warshall algorithm to solve the traveling salesman problem (TSP). Additionally, we demonstrate the enhancement of the Floyd Warshall algorithm's efficiency for TSP using the rectangular algorithm. This paper concludes with a comparative analysis between the original and improved Floyd Warshall algorithms, emphasizing the computational reduction achieved through the proposed enhancements. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 45−52

Influence of moisture content and inlet temperature on the incineration characteristics of municipal solid waste (MSW)

Incineration is an effective method for the treatment of municipal solid waste, pre-treatment reduces the waste moisture content to increase the incineration performance. However, the inlet temperature variation influenced by the environment is often ignored at this stage, and the coupling of moisture content and inlet temperature directly affects the incineration performance. To investigate the optimal inlet conditions for the incineration, the numerical model of a 600 t/d incinerator was established according to a reference case. Detailed analyses of the combustion characteristics of the bed and furnace is conducted under various moisture contents and inlet temperatures. The incineration efficiency under multiple factors is analyzed using response surface methodology, with sensitivity to these factors derived from the surface derivative. The results indicate that at an inlet temperature of 30 °C, the rates of volatile release and fixed carbon combustion are negatively correlated with the moisture content, and maximum incineration efficiency of 39.02 % is reached at a moisture content of 28.12 %. At a moisture content of 33.26 %, The rates of moisture evaporation, volatile release, and fixed carbon combustion are positively correlated with inlet temperature, and the maximum incineration efficiency of 38.76 % is achieved at an inlet temperature of 40 °C. The incineration efficiency exhibits a negative sensitivity to moisture content, whereas its sensitivity to inlet temperature is contingent upon the specific temperature range. The combination of 26.53 % moisture content and 40.86 °C inlet temperature yields the highest incineration efficiency of 40.69 %. The findings of this study provide useful information for the pre-treatment of municipal solid waste, contributing to the enhancement of operational efficiency in waste-to-energy plants.

How does auditing outgoing officials' natural resource asset management policies affect carbon emission efficiency? Evidence from a debiased machine learning model

Reducing carbon emissions to improve environmental quality has been the Chinese government's unwavering dedication in recent years. Several policies have been implemented with the objective of driving carbon reduction. The implementation of the outgoing leadership natural resources audit policy has played a pivotal role in encouraging local government officials to prioritize both economic and environmental development in their pursuit of performance metrics. However, whether the implementation of this policy contributes to an enhancement in carbon emission efficiency requires further empirical validation. This study, utilizing panel data from 282 cities across China, explores the potential impact of the audit policy (specifically referring to the outgoing leadership natural resources asset audit policy, without ambiguity) on carbon emission efficiency through the application of a debiased machine learning model. The results of the study show that the audit policy has been crucial in greatly raising the regional level of carbon emission efficiency. Furthermore, mechanistic analysis results suggest that the audit policy can enhance regional carbon emission efficiency by influencing green innovation and raising government environmental awareness and green financial levels. Based on the heterogeneity analysis, the regions east of the Hu Line, cities with low levels of corruption, less stringent economic growth objectives, and areas with high levels of public environmental concern are the areas where auditing rules have the greatest influence on carbon emission efficiency. In conclusion, based on the comprehensive results of this study, a series of policy recommendations are presented to further advance China's efforts in carbon reduction, ultimately achieving a more sustainable balance between economic and environmental development objectives.

Adaptive isogeometric topology optimization of shell structures based on PHT-splines

This paper proposes an adaptive isogeometric topology optimization framework for shell structures by utilizing a continuous density field represented as Polynomial splines over Hierarchical T-meshes (PHT-splines). This framework ensures an exact representation of shell structures, eliminating the geometric inaccuracies commonly associated with topology optimization. In the meanwhile, the meshes used for design and analysis are refined adaptively and locally along the density boundary to achieve a smooth material layout with reduced degrees of freedom (DOF). The adaptive sensitivity filter is tailored to the characteristics of PHT-splines, where the filter radius is determined automatically and adaptively, without the need to specify parameters in advance. Numerical experiments conducted on various shell structures validate the efficacy of the proposed adaptive framework. In comparison with isogeometric topology optimization based on non-adaptive cases (i.e. B-splines), the proposed adaptive framework demonstrates a notable enhancement in computational efficiency, with a 50%−80% reduction in running time and a 30%−60% reduction in DOF. Additionally, we offer a detailed comparison between PHT-splines and other locally refinable splines in the context of shell topology optimization. Numerical experiments exhibit that the efficient and localized refinement capability of PHT-splines provides advantages in both computational efficiency and structural performance for topology optimization.

Ontology-Guided Generation of Mechanized Construction Plan for Power Grid Construction Project

Mechanized construction is being fully implemented in the electric power infrastructure domain to ensure construction safety, enhance project quality, and improve efficiency. Traditional methods of designing mechanized construction plans are often inefficient due to their labor-intensive processes and heavy reliance on human expertise. This study introduces and evaluates an ontology-guided system designed to automate mechanized construction planning for power grid projects. The developed ontology effectively models domain-specific knowledge, enabling the semantic integration of data from various sources. By leveraging SPARQL queries, the ontology-guided system incorporates knowledge reasoning capabilities that facilitate the automated selection of construction equipment and the generation of comprehensive construction plans. A prototype system incorporating an ontology-guided mechanism has been developed, showcasing marked enhancements in efficiency and accuracy over traditional manual methods, as evidenced by case studies and expert evaluations. The research results emphasize the potential of ontology-guided systems in innovating architectural planning, providing an extensible and standardized approach. Expert evaluation indicates that the system achieves 71.38% effectiveness in generating mechanized construction plans.

Comparative performance analysis of recuperative helium and supercritical CO₂ Brayton cycles for high-temperature energy systems

This study presents a comprehensive comparative analysis of recuperative Brayton cycles utilizing helium (He) and supercritical carbon dioxide (sCO2) as working fluids for high-temperature power generation, focusing on applications in next-generation concentrated solar power (CSP) systems. Thermodynamic cycle simulations were conducted across a temperature range of 1000 K–2000 K, exploring the effects of low-side pressure (1 bar–100 bar) and expansion ratio (1–5) on key performance indicators. These indicators included thermal efficiency, net specific work output, mass flow rate, volumetric flow rate, and system irreversibilities. Results demonstrate that helium-based Brayton cycles exhibit superior thermal efficiency at high temperatures, reaching up to 65 %, compared to sCO2 cycles, which achieve efficiencies up to 55 %. This advantage stems from helium's higher specific heat capacity and lower pressure drops within the cycle. Conversely, sCO2 cycles demonstrate higher net specific work output due to the fluid's higher density, leading to more compact turbomachinery. The study further investigates the impact of irreversibilities, including pressure losses, leakage losses, and heat transfer losses, on cycle performance. A detailed analysis of multi-stage Brayton cycles incorporating intercoolers and reheaters reveals the potential for further efficiency enhancements in both helium and sCO2 systems. This research provides valuable insights for the design and optimization of high-temperature power generation systems, particularly for next-generation CSP plants. The findings highlight the trade-offs between working fluid properties and cycle configurations, guiding the selection of optimal solutions based on specific application requirements and operating conditions.

SiNPs Decoration of Silicon Solar Cells and Size Analysis on the Downshifting Mechanism Response for the Enhancement of Solar Cells Efficiency

SiNPs Decoration of Silicon Solar Cells and Size Analysis on the Downshifting Mechanism Response for the Enhancement of Solar Cells Efficiency

Optimal execution of logical Hadamard with low‐space overhead in rotated surface code

AbstractFault‐tolerant quantum computation requires error‐correcting codes that enable reliable universal quantum operations. This study introduces a novel approach that executes the logical Hadamard with low‐space requirements while preserving the original definition of logical operators within the framework of the rotated surface codes. Our method leverages a boundary deformation method to rotate the logical qubit transformed by transversal Hadamard. Following this, the original encoding of the logical qubit is reinstated through logical flip‐and‐shift operations. The estimated space–time cost for a logical Hadamard operation with a code distance d is 5d2 + 3d2. The efficiency enhancement of the proposed method is approximately four times greater than those of previous approaches, regardless of the code distance. Unlike the traditional method, implementing a logical Hadamard requires only two patches instead of seven. Furthermore, the proposed method ensures the parallelism of quantum circuits by preventing interferences between adjacent logical data qubits.

High Coverage Sub-Nano Iridium Cluster on Core-Shell Cobalt-Cerium Bimetallic Oxide for Highly Efficient Full-pH Water Splitting.

The construction of sub-nanometer cluster catalysts (<1nm) with almost complete exposure of active atoms serves as a promising avenue for the simultaneous enhancement of atom utilization efficiency and specific activity. Herein, a core-shell cobalt-cerium bimetallic oxide protected by high coverage sub-nanometer Ir clusters (denoted as Ir cluster@CoO/CeO2) is constructed by a confined in situ exsolution strategy. The distinctive core-shell structure endows Ir cluster@CoO/CeO2 with enhanced intrinsic activity and high conductivity, facilitating efficient charge transfer and full-pH water splitting. The Ir cluster@CoO/CeO2 achieves low overpotentials of 49/215, 52/390, and 54/243mV at 10mAcm-2 for hydrogen evolution reaction/oxygen evolution reaction (HER/OER) in 0.5m H2SO4, 1.0m PBS, and 1.0m KOH, respectively. The small decline in performance after 300h of operation renders it one of the most effective catalysts for full-pH water splitting. DFT calculations indicate that oriented electron transfer (along the path from Ce to Co and then to Ir) creates an electron-rich environment for surface Ir clusters. The reconstructed interface electronic environment provides optimized intermediates adsorption/desorption energy at the Ir site (for HER) and at the Ir-Co site (for OER), thus simultaneously speeding up the HER/OER kinetics.

Nanowires for Solid‐State Lithium Batteries

AbstractA vital approach to accessing high‐safety and high‐energy‐density lithium batteries is to develop solid‐state electrolytes (SSEs) instead of liquid electrolytes. However, lithium‐ion transport and interface stability issues puzzle the construction of solid‐state lithium batteries (SSLBs). Thus, developing fast‐ionic conductors with high electrochemical performances and chemical stability is crucial to SSLBs. Nanowires (NWs) possess high aspect ratios for maintaining carrier transport along the radial direction, thus being extensively employed in SSLBs for the enhancement of ion transport efficiency, mechanical properties, thermostability, flame retardancy, and interface stability between electrodes and electrolytes, consequently boosting the cycle stability and safety of SSLBs. In this work, the advances in NWs for SSLBs, from rational design and synthesis strategies to applications in composite cathodes, anode materials, and SSEs of SSLBs, are systematically reviewed. The key role of NWs in electrodes and the enhancement mechanism of SSE performance by introducing NWs are concluded in detail. Finally, the existing challenges and anticipated prospects for the future development of advanced nanowire‐based SSLBs are summarized and demonstrated. This review aims to provide a comprehensive understanding to facilitate the application of NWs in SSLBs.

One-Stone-for-Multiple-Birds Additive Strategy for Highly Efficient and Stable Carbon-Based Hole-Transport-Layer-Free CsPbI2Br Solar Cells.

During fabrication and operation of perovskite solar cells (PSCs), defects commonly arise within the crystals as well as at grain boundaries. However, conventional additive strategies typically only serve to mitigate the occurrence of a single defect and fail to significantly enhance device performance. Herein, carbon-based hole-transport-layer-free CsPbI2Br devices are focused on, one kind of important PSCs with more stable structure and an appropriate bandgap for a semitransparent solar cell or a top cell in a tandem configuration, and present a highly efficient one-stone-for-multiple-birds additive strategy based on lanthanide trifluoromethanesulfonates (Ln(OTF)3, Ln: neodymium (Nd), europium (Eu), dysprosium (Dy), thulium (Tm)). Density functional theory calculations reveal that the Ln3+ ions with a smaller radius can elevate defect formation energy for Pb and I vacancies within the crystals, while the presence of OTF- can effectively passivating uncoordinated Pb2+ at grain boundaries. In addition, Ln(OTF)3 addition increases the grain size and meanwhile reduces the surface roughness of the CsPbI2Br layers. All these positive contributions lead to a significant enhancement in power conversion efficiency (PCE) to 15.13% which is among the top PCEs reported for the corresponding solar cells, from 11.80% of the pristine device without Tm(OTF)3 addition, while notably boosting long-term stability and reducing current-voltage hysteresis.

Retraction Note: Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

Retraction Note: Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

Quantum superposing algorithm for quantum encoding

Efficient encoding of classical data into quantum state—currently referred to as quantum encoding—holds crucial significance in quantum computation. For finite-size databases and qubit registers, a common strategy of the quantum encoding entails establishing a classical mapping that correlates machine-recognizable data addresses with qubit indices that are subsequently superposed. Herein, the most imperative lies in casting an algorithm for generating the superposition of any given number of qubit indices. This algorithm is formally known as quantum superposing algorithm. In this work, we present an efficient quantum superposing algorithm, affirming its effectiveness and superior computational performance in a practical quantum encoding scenario. Our theoretical and numerical analyses demonstrate a substantial enhancement in computational efficiency compared to existing algorithms. Notably, our algorithm has a maximum of 2n − 3 controlled-not (CNOT) counts, representing the most optimized result to date.

Assessing Student Satisfaction and Institutional Efficiency in Dormitory Management: A Qualitative Analysis Based on Student Perspectives

This research examines the information management processes implemented in student dormitories and evaluates the enhancement of institutional efficiency based on student satisfaction. The study involved 150 students residing in dormitories across two universities, and data were collected through participant interviews. The findings indicate that while dormitory management is perceived as effective in some areas, there are significant opportunities for improvement in information management, communication, and service quality. Based on the student feedback, this article proposes several strategies to enhance dormitory management, including the adoption of digital platforms for better communication, regular maintenance schedules, and improved transparency in financial management. The challenges of implementing these measures, such as resource constraints and institutional resistance to change, are also discussed. These recommendations aim to improve student satisfaction and, consequently, institutional efficiency.

Prediction of low-velocity impact responses for bio-inspired helicoidal laminates based on machine learning

The inherent capacity of natural protective systems to withstand impact loadings, attributed to their microscale helicoidal architectures, has garnered significant interest. Drawing inspiration from this mechanically robust design, this study aims to introduce the composite laminates with a helicoidal distribution and to accurately and efficiently predict their Low-Velocity Impact (LVI) responses. Initially, the Latin hypercube design (LHS) was employed to generate 500 samples representing various pitch angles. An experimentally verified finite element model was then established to capture the load-displacement curves and energy-time curves for these 500 samples. Subsequently, the Convolutional Neural Network (CNN) model was utilized to accurately predict the load-displacement curves and energy-time curves for bio-inspired helicoidal laminates across different pitch angles. The principal component analysis (PCA) was used to enhance the efficiency of learning the load-displacement and energy-time curves in a reduced dimensional space, and the SHapley Additive exPlanations (SHAP) method was employed to investigate the feature importance of pitch angle. Finally, the helicoidal laminate with the highest energy absorption for a given volume was obtained by the genetic algorithm (GA) combined with the CNN model. This optimized laminate demonstrates a remarkable 9.5 % improvement in energy absorption compared to the best-performing sample within the original data set. Furthermore, the "spiraling" delamination damage of the helicoidal laminates was studied, which indicates that the delamination with small pitch angle is more pronounced for that with large pitch angle. The proposed method offers significant advantages in terms of cost reduction and efficiency enhancement for predicting the LVI responses of helicoidal laminates, holding immense potential in structural design and optimization of composite materials.

Implementation of Machine Learning in the Categorization of Improvement Areas for Kaizen Submissions

This paper explores the synergy between Kaizen methodologies and Machine Learning, with the goal of improving the efficiency of categorizing areas of improvement. The implementation of Machine Learning algorithms, utilizing the ML.NET tool, has proven to be highly advantageous. These algorithms automate the categorization of Kaizen proposals based on structured data and predefined labels. This automation significantly reduces manual workload, expedites the classification process, and ultimately accelerates decision-making within the realm of continuous improvement. Furthermore, the application of machine learning has improved the categorization accuracy, reduced the risk of human errors, and ensured a more precise assignment to the relevant categories. This, in turn, enhances the quality of the collected information, facilitating well-informed decision-making. Additionally, the automated categorization has streamlined the process for users submitting Kaizen proposals, eliminating the need for manual category selection. This reduces cognitive load and promotes active engagement in the continuous improvement process. Furthermore, the system has introduced a category-based incentive structure, where users accrue points corresponding to the category assigned to their Kaizen proposals. These points can be exchanged for items provided by the company, acting as an additional incentive for active employee participation in the continuous improvement process. In conclusion, the integration of Machine Learning with the ML.NET tool has become a valuable tool for optimizing the management of improvement proposals, resulting in a marked enhancement in efficiency, accuracy, and user engagement in the categorization process.

Improved Charge Recombination Efficiency in Organic Light‐Emitting Transistors via Luminescent Radicals

AbstractLuminescent radicals are attracting attention as emitters in electroluminescent devices thanks to the exploitation of doublet excitons. Recent studies reveal that exciton formation in radical organic light‐emitting diodes (OLEDs) primarily occurs through a charge trapping mechanism. Although typically detrimental for OLEDs, this might be a key process to elucidate light emission in organic light‐emitting transistors (OLETs). Here, a unipolar n‐type architecture suitable for the implementation of radical emitters is introduced, designed based on computational calculations. The operation of the as‐realized devices incorporating the newly synthesized [2,6‐dichloro‐4‐(2,6‐dimethoxyphenyl)phenyl](3,5‐dichloro‐4‐pyridyl) (2,4,6‐trichlorophenyl)methyl radical is investigated via transient electroluminescence measurements to demonstrate the occurrence of long‐living emission ascribed to the charge trapping mechanisms. Moreover, a comprehensive understanding of the processes governing radical‐OLET is obtained by recording complete 2D maps of both optical and electrical response of the device as a function of applied voltages. Notably, the trapping of electrons by radical moieties is demonstrated to generate a negative charge density in the emissive layer that facilitates holes to be injected: increasing the balance of opposite charge carriers, a tenfold enhancement of the external quantum efficiency (EQE) at the proper source‐drain and source‐gate voltage conditions is reported to reach a maximum EQE value of 0.2%.

Superconducting cable with energy storage function and its potential for next-generation power system compatible with large-scale renewable energy installation

Abstract The mass introduction of renewable energy is essential to realize a sustainable society. On the other hand, when photovoltaic (PV) and wind power generation are used as main power sources in a power system, it is indispensable to compensate for their severe output fluctuations up to the rating of the power system; however, this is difficult to achieve with conventional energy storage devices alone. To solve this problem, we have proposed a superconducting cable with energy storage function and its use in a DC power system. This cable provides large inertia to the power system without the need for additional energy storage equipment; as a result, the power system itself become capable of high-speed and high-power compensating operations for output fluctuations from renewable energy sources. This paper summarizes our recent works on this research topic: an overview of the proposal, conceptual design of the cable, experimental verification of the principle, and positive effects on the energy use efficiency of renewable energy. For example, an experiment with the use of a small model cable showed the capability of high-speed charge–discharge operations at a high power up to the rating of the microgrid. It was also showed that such a function was indispensable for real-time use of electric power from PV power generation resulting in significant enhancement of energy use efficiency of renewable energy. These results suggest that this concept hold the key to next-generation power systems enabling massive use of renewable energy in the future society.

Innovative enhancements in solar still performance: A comprehensive study on wick-absorber configurations

This study introduces a new thermo-fluid analysis of using wick-absorbers to enhance solar still performance. Three configurations of wick absorbers were tested in a conventional single-slope solar still: (i) Case I: individual wick balls, (ii) Case II: zig-zag wick pattern, and (iii) Case III: rectangular wick array (grid-like pattern). The study evaluates multiple parameters, including saline water temperature, daily water productivity, Nusselt number, Sherwood number, thermal and exergy efficiencies, water cost, and payback period. The addition of an absorbing plate with an insulation layer led to lower saline water temperatures and shifted peak values. Compared to a traditional still, wick utilization enhanced water productivity by 45.8 %, 84.5 %, and 86 % for Cases I, II, and III, respectively, due to increased evaporation rates. This resulted in a relative enhancement in daily thermal efficiency by 64 %, 76 %, and 97.5 %, respectively. Additionally, four correlations were developed to describe Nusselt and Sherwood numbers, with a maximum deviation of 25 %. Economic analysis demonstrated cost-effectiveness, significantly reducing water cost and the payback period by 37 %, 39 %, and 44 % for Cases I, II, and III, respectively, compared to the conventional system.