Efficient Generation Research Articles

High energy and high repetition rate QCW-LD end-pumped electro-optical Q-switched Yb:YAG laser

The quasi-continuous wave laser diode (QCW-LD) pumping is considered as the most efficient way for generating high energy nanosecond pulsed laser with repetition rate ranging from 50 to 1000 Hz. Here, a high efficiency, high repetition rate and high energy QCW-LD end-pumped electro-optical (EO) Q-switched Yb:YAG laser was demonstrated. The shortest pulse duration of 17.4 ns with a maximum single pulse energy of 5.1 mJ was obtained at the repetition rate of 100 Hz. As the repetition rate increases up to 500 Hz, the maximum output single pulse energy was obtained to be 5.38 mJ with the pulse width of 18.7 ns, corresponding to the slope efficiency of 21.6% and beam quality factor of M x 2=1.04, and M y 2=1.01. In addition, a theoretical study was carried by numerical simulation of the rate equations, and the results were agreed well with the experimental ones. The results show that QCW-LD pumping with Yb-doped crystals is a promising way for highly efficient, high-energy and high repetition rate nanosecond pulsed laser generation.

Small‐Bandgap Transducers Induced Carrier Separation for In Situ Dual‐Amplified Tumor Oxidative Stress

AbstractThe inherent and unique charge‐carrier release characteristics of piezoelectric sonosensitizers with desired bandgap can be leveraged to initiate reactive oxygen species (ROS) reactions that have considerable potential for sonodynamic therapy (SDT). In this study, a bimetallic Bi4NbO8Br‐polyvinylpyrrolidone (BNOBP) with a small bandgap (2.45 eV) is synthesized as a potential piezoelectric sonosensitizer. The robust interactions within and between the Bi─O and Nb─O layers narrow the bandgap, facilitating carrier transfer. Concurrently, compared with that of TiO2 and BiOBr, BNOBP exhibits a rapid acoustoelectric response and desirable electron–hole separation efficiency, enabling the generation of ·O2− under ultrasound (US) irradiation. Notably, BNOBP alleviates the overexpression of glutathione (GSH) in tumors and prevents the consumption of the generated ROS by GSH. Combining the previous small bandgap endow the high ROS generation efficiency, this study provides a paradigm for in situ regulation of oxidative stress levels in tumor tissue microenvironment based on bimetallic piezoelectric sonosensitizers.

Bright Bifacial White-Light Illumination by Highly Deformable Electroluminescent Devices Based on Transparent Ionic-Hydrogel Electrodes and Quantum-Dot Color Conversion.

Deformable alternating-current electroluminescent (ACEL) devices are of increasing interest because of their potential to drive innovation in soft optoelectronics. Despite the research focus on efficient white ACEL devices, achieving deformable devices with high luminance remains difficult. In this study, this challenge is addressed by fabricating white ACEL devices using color-conversion materials, transparent and durable hydrogel electrodes, and high-k nanoparticles. The incorporation of quantum dots enables the highly efficient generation of red and green light through the color conversion of blue electroluminescence. Although the ionic-hydrogel electrode provides high toughness, excellent light transmittance, and superior conductivity, the luminance of the device is remarkably enhanced by the incorporation of a high-k dielectric, BaTiO3. The fabricated ACEL device uniformly emits very bright white light (489cdm-2) with a high color-rendering index (91) from both the top and bottom. The soft and tough characteristics of the device allow seamless operation in various deformed states, including bending, twisting, and stretching up to 400%, providing a promising platform for applications in a wide array of soft optoelectronics.

Ultrafast planarization of photoexcited ligands in metal–organic frameworks gates charge transfer to promote photocatalysis

Metal–organic frameworks (MOFs) have emerged as a highly tunable class of porous materials with wide-ranging applications from gas capture to photocatalysis. Developing these exciting properties to their fullest extent requires a thorough mechanistic understanding of the structure–function relationships. We implement an ultrafast spectroscopic toolset, femtosecond transient absorption and femtosecond stimulated Raman spectroscopy (FSRS), to elucidate the correlated electronic and vibrational dynamics of two isostructural 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy)-based MOFs, which manifest drastically different photocatalytic behaviors. Systematic comparisons between the M3+-TBAPy MOFs and bare ligands in various environments reveal the unproductive dimer formation in Al-TBAPy, whereas Sc-TBAPy is dominated by a catalytically active charge-transfer (CT) process. Two ground-state FSRS marker bands of the TBAPy ligand at ∼1267 and 1617 cm−1 probe the chromophore environment at thermal equilibrium. For comparison, the excited-state FSRS of Sc-TBAPy suspended in neutral water unveils a key ∼300 fs twisting motion of the TBAPy peripheral phenyl groups toward planarity, promoting an efficient generation of CT species. This motion also exhibits high sensitivity to solvent environment, which can be a useful probe; we also showed the CT variation for ultrafast dynamics of Sc-TBAPy in the glyphosate aqueous solution. These new insights showcase the power of table-top tunable FSRS methodology to delineate structural dynamics of functional molecular systems in action, including MOFs and other photosensitive “nanomachines.” We expect the uncovered ligand motions (ultrafast planarization) to enable the targeted design of new MOFs with improved CT state characteristics (formation and lifetime) to power applications, including photocatalysis and herbicide removal from waterways.

MODELLING AND 3D PRINTING OF GAS ENGINE TURBINE BLADE

A turbine blade is the individual component which makes the turbine section of a gas turbine. The blades are used for extracting energy from the high temperature, high pressure gas produced by the combustor. This research focuses on the modeling, Analysis and 3D printing of gas engine turbine blades, aiming to enhance efficiency and performance in power generation. Utilizing advanced modelling softwares such as CATIA software, a detailed digital representation of the turbine blade was created, considering the aerodynamic feature. Further, the model is analysed in ANSYS for Subsequently, additive manufacturing techniques were employed to produce physical prototypes using high-strength, heat-resistant materials. The study evaluates the structural integrity, thermal resistance, and overall performance of the 3D-printed turbine blades, comparing them with traditional manufacturing methods. The findings contribute to the advancement of turbine technology, demonstrating the feasibility and benefits of 3D printing in optimizing gas engine turbine blade design for increased efficiency and reliability.

Advancements in efficient Terahertz generation techniques for diverse applications in spectroscopic studies

Advancements in efficient Terahertz generation techniques for diverse applications in spectroscopic studies

Ultramicrotomy‐Assisted Fabrication of Nanochannels for Efficient Ion Transport and Energy Generation

AbstractNano‐ and angstrom‐scale channels fabricated from 2D‐layered materials provide a unique platform for studying fluidic behavior at atomic‐scale confinement, with applications in desalination, osmotic power generation, and fuel cells. While various fabrication methods exist, achieving precision, scalability, and minimal fabrication time is challenging. Ultramicrotomy‐based nanofabrication is shown here as an efficient approach to create nanofluidic membranes containing nanochannels with atomically flat walls. This approach is compatible with both bottom–up nanolaminates and top–down nanochannels, produces multiple devices from the same 2D assembly and allows swift variation of channel length. Integration of these membranes into macroscopic silicon fluidic‐chips is achieved maintaining the structural and functional properties. The robustness of the sliced membranes is demonstrated through restacked vermiculate laminates that sustain applied pressures and generate ionic streaming currents. Sliced pristine vermiculite channels exhibit charge‐selective ion transport, leading to osmotic power density (Posm/A) ranging from 9.2 to 300.4 Wm−2 under various KCl concentration gradients. Maximum conversion efficiency of 23.3% is obtained at a 100‐fold gradient, yielding Posm/A of 234.6 Wm−2. Short channel lengths sliced by ultramicrotomy contribute to high Posm/A, promising applications of miniature energy devices for pressure‐driven electricity generation and osmotic power generation.

Harnessing Dual-Functionality of N, F-Codoped SnO2 Material for Efficient Hydrogen Generation and Dye Degradation

Harnessing Dual-Functionality of N, F-Codoped SnO2 Material for Efficient Hydrogen Generation and Dye Degradation

Non-covalent complexes of polycationic/polyanionic fullerene C60 derivatives with cyanine dyes: Spectral properties and superoxide generation under NIR light irradiation

Polycationic/polyanionic fullerene C60 derivatives and heptamethine cyanine dyes IR780, IR783, IR806, IR820 and piperazine-substituted derivative of IR780 dye form complexes through an electrostatic interaction in aqueous solutions. The fluorescence of the dye is strongly quenched in such complexes, and they efficiently generate superoxide anion-radical under near-infrared (NIR) irradiation due to the photoinduced electron transfer from the photoexcited dye to the fullerene core. The superoxide generation efficiency under NIR light irradiation increases 52 times for the IR783−(polycationic fullerene derivative) complex compared to the native dye. The demonstrated results show promising potential of such [cyanine dye]−fullerene structures as new NIR type I photosensitizers for the photodynamic therapy.

Realizing thermoelectric cooling and power generation in N-type PbS0.6Se0.4 via lattice plainification and interstitial doping

Thermoelectrics have great potential for use in waste heat recovery to improve energy utilization. Moreover, serving as a solid-state heat pump, they have found practical application in cooling electronic products. Nevertheless, the scarcity of commercial Bi2Te3 raw materials has impeded the sustainable and widespread application of thermoelectric technology. In this study, we developed a low-cost and earth-abundant PbS compound with impressive thermoelectric performance. The optimized n-type PbS material achieved a record-high room temperature ZT of 0.64 in this system. Additionally, the first thermoelectric cooling device based on n-type PbS was fabricated, which exhibits a remarkable cooling temperature difference of ~36.9 K at room temperature. Meanwhile, the power generation efficiency of a single-leg device employing our n-type PbS material reaches ~8%, showing significant potential in harvesting waste heat into valuable electrical power. This study demonstrates the feasibility of sustainable n-type PbS as a viable alternative to commercial Bi2Te3, thereby extending the application of thermoelectrics.

Solar photothermal self-deicing composite films based on fluorinated polyimide and phosphorene nanoflakes for passive anti-icing of photovoltaic panels

Snow and ice coverage greatly deteriorates the power output of photovoltaic (PV) solar cells due to sunlight obstruction and thus makes a great impact on their electricity generation. To address this problem, we design a type of passive self-deicing composite films based on colorless fluorinated polyimide as a polymeric matrix and phosphorene (PR) nanoflakes as a light absorber for the photothermal deicing of PV panels driven by solar energy. The composite films were successfully fabricated through in situ polycondensation, followed by temperature-programmed thermal imidization. The resultant composite films exhibit a colorless transparent feature along with high transmittance and high absorptivity. The presence of PR nanoflakes enhances the absorption of solar light and conversion of photothermal energy by the composite films, resulting in good photothermal self-deicing effectiveness for solar PV panels. There is an accelerated melting process occurring in the ice covering on the surface of the composite films under solar illumination. The composite film containing 2.0 wt % PR nanoflakes exhibits an optimal photothermal self-deicing performance under a high photothermal conversion efficiency of 92.9 %. The use of this composite film can promote a rapid recovery of the output voltage to an ideal value for the snow- or ice-covered PV panels through fast photothermal self-deicing. This study provides a novel approach for the development of passive self-deicing materials to enhance the power output and electricity generation efficiency of solar PV panels in the snowy and icy weather.

A solid-state pulse power sub-nanosecond SiC DSRD-based generator with high-voltage and high repetition frequency for pulse discharge water treatment

Water treatment is one of the most important issues for all walks of life around the world. The unique advantages of the solid-state power electronic pulses in water treatment make it attractive and promising in practical applications. The output voltage, rising time, repetition rate, and peak power of output pulses have a significant impact on the effectiveness of water treatment. Especially in pulse electric field treatment and pulse discharge treatment, the pulse with fast rising time achieves the advantage of generating plasma without corona, which can avoid water heating effect and greatly improve the efficiency of the pulse generator. High repetition rate can significantly reduce the peak power requirement of the pulse in water treatment application, making the equipment smaller and improving the power density. Therefore, the study developed a high-voltage high frequency sub-nanosecond pulse power generator (PPG) system for wastewater treatment. It adopts SiC DSRD (Drift Step Recovery Diode) solid-state switches and realize modular design, which can achieve high performance and can be flexible expanded according to the requirements of water treatment capacity. Finally, an expandable high-voltage PPG for water treatment is built. The output parameters of the PPG include output pulse voltage range from 1 to 5.28 kV, rise time <600 ps (20%–90%), repetition up to 1 MHz. The experiment results of PPG application for pulse discharge water treatment is presented. The results indicate that the proposed generator achieves high-efficiency degradation of 4-Chlorophenol (4-CP), which is one of the most common chlorophenol compounds in wastewater. From experiment, the homemade system can degrade 450 mL waste water containing 500 mg/L 4-CP in 35 min, with a degradation rate of 98%. Thereby, the requirement for electric field intensity decreased. Through the further quantitative analysis, the impact of frequency, voltage, and electrode spacing on the degradation effect of 4-CP is confirmed.

An efficient finite element mesh generation methodology based on μCT images of multi-layer woven composites

High-fidelity models are essential for accurate finite element (FE) simulations of composite material behavior. This paper proposes an efficient meshing methodology based on micro-Computed Tomography (μCT) images. U-Net convolutional neural network was used for image segmentation. Connected yarns were then separated using an improved procedure based on watershed algorithm and geometric transformations. The proposed Constrained Delaunay-Advancing Front Technique (CD-AFT) surface reconstruction algorithm extracts point cloud of yarns from segmented images and outputs high-quality and smooth orientable manifold watertight triangulated surface. Intersecting meshes of yarns are separated through node position detection and Laplacian moving. Experimental results show that proposed methodology is capable of accomplishing mesh generation for different mesh sizes. Compared with commercial software, it has obvious advantages in mesh quality and size control. Since the proposed method operates independently of commercial software and manual operation, it facilitates the automated generation of numerous high-fidelity models from μCT images for FE simulations.

Exploring Nanomaterials for Hydrogen Storage: Advances, Challenges, and Perspectives.

Hydrogen energy heralded for its environmentally friendly, renewable, efficient, and cost-effective attributes, stands poised as the primary alternative to fossil fuels in the future. Despite its great potential, the low volumetric density presents a formidable challenge in hydrogen storage. Addressing this challenge necessitates exploring effective storage techniques for a sustainable hydrogen economy. Solid-state hydrogen storage in nanomaterials (physically or chemically) holds promise for achieving large-scale hydrogen storage applications. Such approaches offer benefits, including safety, compactness, lightness, reversibility, and efficient generation of pure hydrogen fuel under mild conditions. This article presents solid-state nanomaterials, specifically nanoporous carbons (activated carbon, carbon fibers), metal-organic frameworks, covalently connected frameworks, nanoporous organic polymers, and nanoscale metal hydrides. Furthermore, new developments in hydrogen fuel cell technology for stationary and mobile applications have been demonstrated. The review outlines significant advancements thus far, identifies key barriers to practical implementation, and presents a perspective for future sustainable energy research. It concludes with recommendations to enhance hydrogen storage performance for cost-effective and long-lasting utilization.

Artificial Neural Network Based Performance prediction of Exergy and Energy Analysis of Gas Turbine Generator using Cycle-Tempo Software

It is crucial for research to continuously seek innovative methods of generating additional electricity in order to meet the demands of everyone requiring it. Engineers can work to make the companies that provide electricity better. It is not possible to separate the parts of the system from how well the gas turbine generator works. This research introduces a new method to predict the performance of Gas Turbine Generators (GTGs) using Artificial Neural Networks (ANNs) combined with Cycle-Tempo software. The main emphasis of this method is on analysing the exergy and energy of the GTGs. GTGs are very important in many different industries. It is crucial to make them work efficiently to produce more energy and protect the environment. This research is new and different because it uses ANNs in a smart way to improve how research measure how well GTG works. The main advancement is in the automation part, where artificial neural networks are taught to independently collect and study data. They can adjust to changing conditions on their own, which greatly reduces the amount of manual work needed for analysis. This automation makes things easier and can change to different situations quickly. It helps industries that rely on GTG by meeting their changing needs. Moreover, the research uses complex computer algorithms to make very accurate forecasts about energy and exergy effectiveness, voltage output, and hydrogen production—an important measurement in GTG function. The results show that the accuracy is really good, with a small error of less than 1% and a high value of 0. 99 for the R-squared when compared to what was expected. The automation, detailed analysis, and accurate predictions of the novel technology help improve the efficiency and sustainability of gas turbine generators. This makes it a valuable tool for industries that rely on these generators for power.

Significant augmentation of hydrogen and benzaldehyde production through mediator-free in-situ synthesis of CuNi/CN photocatalysts for benzyl alcohol splitting

Amidst growing concerns surrounding global warming and energy scarcity, there is an urgent demand for more efficient and environmentally friendly methods of hydrogen production. In response, we introduce a novel mediator-free in-situ approach for synthesizing CuNi/CN composite photocatalysts. This method entails the synthesis of copper and nickel complexes to fabricate bimetallic Cu and Ni nanoparticles on carbon nitride (CN). These catalysts are deployed for the photocatalytic dehydrogenation of benzyl alcohol, resulting in the generation of hydrogen and benzaldehyde. The CuNi bimetal-loaded CN structure provides ample active sites and demonstrates a low hydrogen reduction overpotential, thereby enhancing both charge separation and surface hydrogen reduction processes. In comparison to pure CN, the composite photocatalyst exhibits significantly improved photocatalytic activity. Notably, the composite 2 % Cu1Ni1/CN photocatalyst achieves a record-high hydrogen production rate of about 80 μmol g-1h−1, representing an impressive increase of approximately 533 times compared to pristine CN. Moreover, the catalyst offers cost-effectiveness, ease of preparation, and reusability, ensuring stable hydrogen production efficiency over multiple cycles. This precious metal-free composite photocatalyst not only facilitates the synthesis of valuable compounds but also promotes sustainable advancements in efficient hydrogen energy generation.

High-power Raman lasing and efficient anti-Stokes generation in mm-sized crystalline disk resonators.

We have previously experimentally observed high-power Stokes and second-order Stokes output from a mm-sized CaF2 disk using stimulated Raman scattering. A pump laser at a wavelength of 1.06 µm was coupled via a tapered fiber to the whispering gallery modes (WGM) of the disk. In this Letter, we extend this work and demonstrate the production of the first anti-Stokes sideband at power levels as high as 60 µW in near continuous-wave (CW) operation. The result is a four-component Raman comb at the output, with a wavelength range covering from 1.023 to 1.14 µm. We discuss the threshold dependence of the observed Raman lines on the crystal orientation and provide experimental validation. These advances enable the use of such mm-sized resonators as compact, efficient sources for terahertz-level frequency modulation.

Energy Production Optimization by Positioning of Solar Panels and Surveillance System with Cleaning of Panels

The pursuit of efficient and sustainable energy generation has led to the widespread adoption of solar panels. However, the optimal performance of these panels can be hindered by factors such as dirt accumulation, suboptimal orientation, and security concerns. To address these challenges, we propose an integrated Solar Panel Tracking, Cleaning, and Surveillance System (SPTCSS). Our system employs advanced tracking technology to optimize solar panel orientation, ensuring maximum exposure to sunlight throughout the day. Additionally, it incorporates automated cleaning mechanisms, utilizing water and brushes to remove dust, debris, and other contaminants, thereby maintaining peak efficiency. Furthermore, the surveillance aspect of the system provides real-time monitoring and security features, safeguarding against theft, vandalism, and unauthorized access. Through a network of sensors and cameras, any anomalies or suspicious activities can be promptly detected and addressed. By integrating tracking, cleaning, and surveillance functionalities into a single system, our solution offers a comprehensive approach to enhancing the performance, longevity, and security of solar panel installations. This not only maximizes energy generation but also minimizes maintenance requirements and mitigates risks, ultimately contributing to a more sustainable and reliable renewable energy infrastructure

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

Text to image synthesis with multi-granularity feature aware enhancement Generative Adversarial Networks

Synthesizing complex images from text presents challenging. Compared to autoregressive and diffusion model-based methods, Generative Adversarial Network-based methods have significant advantages in terms of computational cost and generation efficiency yet remain two limitations: first, these methods often refine all features output from the previous stage indiscriminately, without considering these features are initialized gradually during the generation process; second, the sparse semantic constraints provided by the text description are typically ineffective for refining fine-grained features. These issues complicate the balance between generation quality, computational cost and inference speed. To address these issues, we propose a Multi-granularity Feature Aware Enhancement GAN (MFAE-GAN), which allows the refinement process to match the order of different granularity features being initialized. Specifically, MFAE-GAN (1) samples category-related coarse-grained features and instance-level detail-related fine-grained features at different generation stages based on different attention mechanisms in Coarse-grained Feature Enhancement (CFE) and Fine-grained Feature Enhancement (FFE) to guide the generation process spatially, (2) provides denser semantic constraints than textual semantic information through Multi-granularity Features Adaptive Batch Normalization (MFA-BN) in the process of refining fine-grained features, and (3) adopts a Global Semantics Preservation (GSP) to avoid the loss of global semantics when sampling features continuously. Extensive experimental results demonstrate that our MFAE-GAN is competitive in terms of both image generation quality and efficiency.