High Performance Research Articles

Combining Machine Learning techniques and Genetic Algorithm for predicting run times of High Performance Computing jobs

This study proposes a novel approach combining Machine Learning (ML) techniques and Genetic Algorithms (GA) for predicting High-Performance Computing (HPC) job run times. The objective is to create a prediction method universally applicable to any HPC system, irrespective of workload characteristics, application specific parameters, user behavior, or hardware architecture. Since user-supplied run time estimates are often inaccurate, we aim to categorize job runtimes into several classes, allowing users to select appropriate classes for their jobs. A Genetic Algorithm is developed to optimally define these runtime classes, determining both the number of classes and the time intervals represented by them. Four Machine Learning algorithms (K Nearest Neighbours, Support Vector Regression, Extreme Gradient Boosting and Deep Neural Networks) are implemented for run time prediction. A unique set of features extracted from historical job data serves as input to the Machine Learning models. The generalized nature of our method is demonstrated by validating its performance on data from six clusters with distinct configurations, applications and runtime distributions. Our results illustrate the superior performance of Machine Learning models incorporating GA-defined runtime classes for all datasets. Across all six datasets, our method achieves R2 scores exceeding 0.8, and accuracy greater than 0.7.

Activating CoP via spontaneous atomic palladium doping for efficient hydrogen evolution

Electrocatalytic hydrogen evolution is an efficient and clean hydrogen production technology with simple process and high product purity. Searching for inexpensive alternatives to platinum will help popularize proton exchange membrane water electrolysis (PEMWE) technology. However, the alternative catalytic materials are essentially limited by low catalytic efficiency. Herein, we address this challenge through chemically activating cobalt phosphide (CoP) by doping a small amount of atomic palladium. Palladium substitution occurs at the cobalt site of CoP. Theoretical calculations manifest that the phosphorus atoms near the palladium sites exhibit almost zero hydrogen adsorption free energy. The final cobalt phosphide doped with only 2 wt% of palladium shows wonderful HER performance. Specifically, its overpotential at a current density of 10 mA cm−2 is only 37.5 mV in an acidic medium, and the corresponding Tafel slope is 56 mV dec−1. This work provides a reference for preparing high performance non-platinum catalysts.

Antimony nanoparticles encapsulated in interconnected nitrogen-doped carbon nanospheres for high performances PIBs

Antimony (Sb) has attracted significant attention as an anode material for potassium-ion batteries (PIBs) due to its high theoretical capacity and suitable working voltage. However, the severe volume variations of Sb during potassiation/depotassiation process lead to sluggish kinetics and poor cyclic stability for PIBs. In this study, Sb nanoparticles are confined within interconnected N-doped carbon nanospheres (Sb@NC) through an effective carbothermal reduction process. The potassium storage performances and mechanism are elucidated through electrochemical tests combined with ex-XRD analysis. When used as an anode for PIBs, the optimized Sb@NC-2 anode exhibits excellent cycle stability of 463.0 mAh g−1 at 200 mA g−1 over 100 cycles and superior rate capability of 113.4 mAh g−1 at a high current density of 2 A g−1. Besides, a full cell comprising an Sb@NC-2 anode and a PTCDA cathode is also tested to validate the practical feasibility of the anode. Furthermore, Density Functional Theory (DFT) calculations demonstrate that N-doped carbon can enhance the adsorption of K+ and improve the kinetics of electrochemical reactions. Therefore, the unique yolk-shell structure in this study effectively restricts volume expansion and aggregation of Sb particles while facilitating rapid electron/ion transfer. These findings provide a potential alternative anode material for advanced PIBs.

Do the best of all together: Hierarchical spatial-frequency fusion transformers for animal re-identification

Animal re-identification is fundamental in the ecology and ethology community for understanding the Earth’s ecosystems. Due to the uncertainty of photographing animals in the wild, such as the position and angle of capture and the variations in animal individuals as well as in their poses and habitats, building image-based visual models for re-identifying animal individuals is a challenging task. We propose a novel framework, hierarchical spatial-frequency fusion transformers, to address animal re-identification. We first use spatial and frequency representation learning to capture effective deep features and then employ hierarchical transformer-based representation learning to consolidate low-level detailed information into high-level semantic information from a global perspective. Through this process, we construct a deeply supervised nonlinear aggregation method to enhance finer multi-scale, multi-level and cross-domain features. Our method illustrates the case of “do the best of all together”, meaning that only when both the frequency and spatial domains are combined can we achieve the best performance. The experimental results demonstrate that our approach achieves significantly higher performance than other state-of-the-art methods.

Bifunctionality of amine-modified metal-organic frameworks for CO2 capture and selective utilization in cycloaddition

In this study, copper- and chromium-based (HKUST-1 and MIL-101(Cr), respectively) metal-organic frameworks (MOF) functionalized with amine groups (HKUST-1‒NH2 and MIL-101(Cr)‒NH2, respectively) were directly synthesized using 2-aminoterephthalic acid as an organic linker via hydrothermal method without adding hydrofluoric acid. They were then investigated for their potential applications in dynamic carbon dioxide (CO2) adsorption and conversion of epoxides with CO2. The functionalized MOF (HKUST-1‒NH2 and MIL-101(Cr)‒NH2) retained their desired textural properties, while gaining a significantly enhanced Lewis basic character for CO2 capture and catalysis application. Both HKUST-1‒NH2 and MIL-101(Cr)‒NH2 not only showed an improved CO2 uptake capability, but also an excellent and stable regenerability over multiple adsorption-desorption cycles. MIL-101(Cr)‒NH2 exhibited a higher performance than the parent MOF and HKUST-1‒NH2 in the transformation of styrene oxide (SO) with CO2 to styrene carbonate (SC) and carbonate oligomers (COL) due to combined effect of its textural properties and basicity. Under solvent-free system, COL from monomeric SC was directly obtained, up to 72.4 % yield, via in situ oligomerization. Optimization of the solvent-free reaction conditions was carried out to control the selective pathway of CO2 utilization between cycloaddition and oligomerization. In the presence of acetonitrile, a > 97 % yield of SC was achieved over MIL-101(Cr)‒NH2 under a mild reaction condition (120 °C and 20 bar of CO2). Reaction mechanisms for the cycloaddition and oligomerization of SO with CO2 are also proposed to comprehend the role of MOF, amine group, and co-catalyst. The combined efficient CO2 adsorption and capability to produce CC and COL makes the synthesized MOF promising materials for CO2 capture and selective utilization.

Optimal robust online tracking control for space manipulator in task space using off-policy reinforcement learning

This study addresses the demands for adaptability, uncertainty management, and high performance in the control of space manipulators, and the inadequacies in achieving optimal control and handling external uncertainty in task space in previous research. Based on off-policy reinforcement learning, a model-free and time-efficient method for online robust tracking control in task space is devised. To address the complexity of dynamic equations in task space, a mixed-variable approach is adopted to transform the multivariable coupled time-varying problem into a single-variable problem. Subsequently, the optimal control policy is derived with the disturbance convergence, stability, and optimality of the control method being demonstrated. This marks the first instance of achieving robust optimal tracking control in task space for space manipulators. The efficacy and superiority of the presented algorithm are validated through simulation.

Identification and antifungal susceptibility profile of uncommon yeast species at Fattouma Bourguiba University Hospital in Tunisia.

Despite the severe impact of uncommon yeast fungal infections and the pressing need for more research on the topic, there are still few studies available on the identification, epidemiology, and susceptibility profile of those pathogens. The aims of the current study were to define the profile of uncommon yeast species at Fattouma Bourguiba University Hospital using phenotypic, molecular, and proteomic methods and to study their antifungal susceptibility profile. Pre-identified uncommon yeast species were collected from 2018 to 2021. These isolates were further identified using phenotypic methods (ID32C® system and Vitek2® YST), matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), and sequencing. The antifungal susceptibility profile was studied using the reference CLSI broth microdilution method. In total, 30 strains were collected during the study period. Referring to the sequencing, the most isolated uncommon species were Saprochaete capitata, Candida lusitaniae, Candida kefyr, Candida inconspicua, and Candida guilliermondii. A total of 90% of isolates were correctly identified by MALDI-TOF MS compared to 76.7% and 63.3% by ID32® C and VITEK® 2 YST, respectively. The isolated species showed variable responses to antifungals. Candida guilliermondii showed increased azole minimum inhibitory concentrations. Misidentification of uncommon yeast species was common using commercial phenotypic methods. The high percentage of concordance of MALDI-TOF results with sequencing highlights its high performance and usefulness as a routine diagnosis tool.

OTMorph: Unsupervised Multi-domain Abdominal Medical Image Registration Using Neural Optimal Transport.

Deformable image registration is one of the essential processes in analyzing medical images. In particular, when diagnosing abdominal diseases such as hepatic cancer and lymphoma, multi-domain images scanned from different modalities or different imaging protocols are often used. However, they are not aligned due to scanning times, patient breathing, movement, etc. Although recent learning-based approaches can provide deformations in real-time with high performance, multi-domain abdominal image registration using deep learning is still challenging since the images in different domains have different characteristics such as image contrast and intensity ranges. To address this, this paper proposes a novel unsupervised multi-domain image registration framework using neural optimal transport, dubbed OTMorph. When moving and fixed volumes are given as input, a transport module of our proposed model learns the optimal transport plan to map data distributions from the moving to the fixed volumes and estimates a domain-transported volume. Subsequently, a registration module taking the transported volume can effectively estimate the deformation field, leading to deformation performance improvement. Experimental results on multi-domain image registration using multi-modality and multi-parametric abdominal medical images demonstrate that the proposed method provides superior deformable registration via the domain-transported image that alleviates the domain gap between the input images. Also, we attain the improvement even on out-of-distribution data, which indicates the superior generalizability of our model for the registration of various medical images. Our source code is available at https://github.com/boahK/OTMorph.

Multi-time scales prediction of aggregated schedulable capacity of electric vehicle fleets based on enhanced Prophet-LGBM algorithm

The fast-increasing applications of renewable energy and electric vehicles (EVs) pose significant challenges to the safe and economic operation of the power grid. However, through Virtual Power Plant (VPP) technology, EV fleets can be aggregated into large-scale EV generalized energy storage systems, providing scarce dispatchable flexible resources for the future power system. Rapid and accurate prediction of the Aggregated Schedulable Capacity of EV (EVASC) fleets is a crucial technology for VPP with Electric Vehicles (EV-VPP) to participate in various ancillary services of the power system. In this paper, orienting for various dispatch scenarios in the power system, a multi-time scale prediction model of the EVASC for EV-VPPs is presented based on the enhanced Prophet algorithm combining a light gradient boosting machine (LGBM) algorithm. The enhanced Prophet-LGBM algorithm is initially proposed here by incorporating the LGBM algorithm into the traditional Prophet algorithm in series and adding extra sequential features in its input, such as temperature, weather, and alike. In this way, the problem of overfitting and low capability in addressing additional input features in the traditional Prophet is overcome. The proposed enhanced Prophet-LGBM-based prediction method of the EVASC for EV-VPPs is validated using 1.8 million actual charging records of over 4000 charging piles for half a year at the provincial level. The 30-day simulation results of the day ahead and intraday predictions of the EVASC for EV-VPPs show that much higher performances in the prediction accuracy can be achieved, specifically in holidays.

Vanadium as a Ti-like mediator boosting electronic transmission in Fe-based MOFs for photocatalytic sterilization

Efficient metal–organic frameworks (MOFs) photocatalytic bactericidal catalysts are urgently needed in water purification. Herein, a Fe-MOF (MIL-88B-NH2(V1Fe5) with promoted electron transport was achieved by vanadium (V) ions doping and V/Fe ratio optimization, showing excellent photocatalytic bactericidal activity against E. coli under visible light irradiation (99.92%). The efficient antibacterial mechanism, V as a Ti-like mediator boosting electronic transmission in MIL-88B-NH2(V1Fe5), was revealed by its band structure, transient photocurrent, electrochemical impedance spectroscopy, and scavenger quenching experiments. The enhancement of photocatalytic bactericidal performance of Fe-MOFs by V–ion-doping was confirmed by two other Fe-MOFs, MIL-53-NH2(V1Fe5) and MIL-101-NH2(V1Fe5), with the same metal ions and ligands, both of which have higher performance than the corresponding undoped MOFs. Among them, MIL-88B-NH2(V1Fe5) exhibits the highest photocatalytic bactericidal activity due to its suitable metal clusters ([M(μ 3-O)] cluster) and topological structure (three-dimensional rhomboid network structure). This work demonstrated the amplification effect of V ion doping on electron transport in Fe-MOFs photocatalysts.

Kinetics prediction of normal knee and undergone total knee arthroplasty during squatting based on extreme gradient boosting

Following an arthroplasty, biomechanical evaluation of the knee joint is crucial because it plays a significant role in patient rehabilitation and physician follow-up. The sixty-two subjects, consisting of 31 with normal knees and 31 who had undergone total knee arthroplasty, evaluated the kinetics of the knee joints during squatting using inverse dynamics analysis. Artificial intelligence based on the XGBoost model was also used to predict kinetics data during group squatting. The result revealed that the maximum force, moment, and quadriceps force in the normal knee were higher than in the TKA group during squatting. The maximum resultant joint force magnitude in the normal and TKA groups revealed an average of 4.35 ± 1.00 and 3.08 ± 1.20 times body weight, respectively. Kinetics of the knee joint in a normal knee significantly differed (p < 0.05) from undergoing TKA, especially in the AP-direction force. Additionally, the resultant moment that included the quadriceps muscle force in the normal group was higher than in the TKA groups. The extreme gradient boosting algorithm (XGBoost) model has prediction performance metrics, which are considered to have high correlation performance in the case of kinetics.

Characterization of the ELM-free negative triangularity edge on DIII-D

Abstract Tokamak plasmas with strong negative triangularity (NT) shaping typically exhibit fundamentally different edge behavior than conventional L-mode or H-mode plasmas. On DIII-D, every plasma with sufficiently negative triangularity (δ &lt; δcrit ≃ −0.12) is found to be inherently free of edge localized modes (ELMs), even at injected powers well above the predicted L-H power threshold. It is also possible to access an ELM-free state at weaker average triangularities provided that at least one of the two x-points is still sufficiently negative. Access to the ELM-free NT scenario is found to coincide with the closure of the second stability region for infinite-n ballooning modes, suggesting that ballooning stability may play a role in limiting the accessible pressure gradient in NT plasmas. Despite this, NT plasmas are able to support small pedestals and are typically characterized by an enhancement of edge pressure gradients beyond those found in traditional L-mode plasmas. Further, the pressure gradient inside of this small pedestal is unusually steep, allowing access to high core performance that is competitive with other ELM-free regimes previously achieved on DIII-D. Since ELM-free operation in NT is linked directly to the magnetic geometry, NT fusion pilot plants are predicted to maintain advantageous edge conditions even in burning plasma regimes, potentially eliminating reactor core-integration issues caused by ELMs.

Improving the design of blast furnace blast regenerative devices by calculating coolant flows through the channels of the air heater nozzle

The air heaters used for heating blast furnace blast have recently undergone a number of design changes, which has affected their key thermal performance indicators. There are established temperatures at which settled operating conditions show sufficient technical and economic performance. Deviation from the conditions by increasing the temperature of the hot blast entails negative consequences of a different nature, which ultimately makes it unfeasible from an economic point of view. New design solutions for air heaters must be determined by maximum temperatures, which affects the service life of the equipment and system. Consequently, the development of the heat transfer surface of the nozzle plays an important role in solving the problem. Increasing the reliability of the apparatus and achieving high thermal performance indicators will result from improving the design of regenerative devices. By applying a mathematical model to the calculation of flows in a system of channels of an arbitrary given structure, calculations of coolant flows through the channels of a blast furnace heater nozzle were obtained. Modeling of flow non-uniformity at the inlet to the nozzle was carried out for different pressure fields at the inlet to the nozzle with horizontal channels. The results of the specified pressure fields at the entrance to the system and the results of calculating the mass flow rates of the coolant through vertical and horizontal channels are presented. It has been confirmed that the pattern of the pressure field at the inlet to the nozzle and the velocity field in the supply vertical channels are different due to the mutual influence of adjacent vertical channels because of their being connected into a single system by horizontal channels. Calculations have confirmed that horizontal channels do not actively participate in the heat exchange process. For better efficiency, it was proposed to make horizontal channels not along the entire height of the nozzle, but only in its upper and lower layers.

Multiple modulating energy-efficient nanofiltration membranes during high-quality drinking water towards high mineral/organic matter selectivity

Solving the low mineral/organic matter selectivity of nanofiltration (NF) membranes and realizing high permeability are effective strategies to satisfy the energy-saving production of high-quality drinking water. Controlling the interfacial polymerization (IP) reaction rate at the molecular level is a feasible approach to customize the functionality of NF membranes and simultaneously modulate multiple membrane properties. Here, we selected 1.4-piperazinediethanesulfonic acid (PIPES) as an aqueous-phase additive, and by taking advantage of the hydrogen-bonding, long-range electrostatic, and steric hindrance effects between PIPES and piperazine (PIP)monomers, as well as the reduction of solution pH, we achieved precise molecular-level regulation of the IP reaction rate. This strategy simultaneously enables the integrated tailoring of various membrane properties such as high hydrophilicity, high surface charge density, uniform pore size distribution and roughness reduction. Molecular dynamics (MD) and computational fluid dynamics (CFD) simulations were used to explore the effects of structural and morphological changes of the polyamide layer on the permeation performance, and the microscopic adsorption mechanism of foulants on the membrane was analyzed by a Quartz Crystal Microbalance (QCM). For pacificating natural surface water, the PIPES-0.75 NF membrane exhibited high permeation performance (23.7 LMH/bar), high mineral/organic selectivity (KCa2+/DOM = 16.5), excellent anti-fouling and anti-scaling properties, and reduced CO2 emissions per ton of water produced up to 90 %. In addition, an attempt was made to correlate the physicochemical properties of the membranes with the permselectivity and membrane fouling, and the economic availability of PIPES-conditioned NF membranes was examined, which provides a novel option for achieving NF in drinking water systems from the perspective of cost reduction and efficiency.

Removal of benzo[a]pyrene from the soil by adsorption coupled with degradation on saponin-modified bentonite immobilized crude enzymes

Bentonite is a non-metallic mineral with montmorillonite as the main component. It is an environmentally friendly mineral material with large reserves, wide distribution, and low price. Bentonite can be easily modified organically using the surfactant saponin to obtain saponin-modified bentonite (Sap-BT). This study investigates the immobilization of crude enzymes obtained from Trametes versicolor by physical adsorption with Sap-BT. Thus, saponin-modified bentonite immobilized crude enzymes (CE-Sap-BT) were developed to remove benzo[a]pyrene. Immobilization improves the stability of free enzymes. CE-Sap-BT can maintain more than 80% of activity at 45 °C and after storage for 15 d. Additionally, CE-Sap-BT exhibited a high removal rate of benzo[a]pyrene in soil, with 65.69% after 7 d in highly contaminated allotment soil and 52.90% after 6 d in actual soil contaminated with a low concentration of benzo[a]pyrene at a very low laccase dosage (0.1 U/3 g soil). The high catalytic and removal performance of CE-Sap-BT in contaminated sites showed more excellent practical application value.

In-situ immobilization of ZIF-67/ZIF-8 on wood biomass for degradation of methylene blue and formaldehyde

Metal-organic frameworks (MOFs) have been extensively studied for the removal of pollutants in wastewater and air. However, the existence of MOF materials in powder form with easy agglomeration and difficult separation has greatly confined their application potential. To address this challenge, ZIF-67/ZIF-8 composites were in-situ grown on wood biomass, with its porous structure providing channels for the growth of MOFs and active sites for MOFs catalysis. The structure, i.e., morphology, crystallinity and chemical components, and optical properties were characterized. The degradation efficiency of methylene blue (MB) by the ZIF-67/ZIF-8@wood composites photocatalyst was more than 85.7 % under visible light irradiation within 90 mins, reflecting the good photocatalytic activity of the composites. The ZIF-67/ZIF-8@wood composites completely adsorbed and decomposed HCHO (22.3 ppm) within 30 mins. The high catalytic performance of the ZIF-67/ZIF-8@wood composites can be attributed to the high surface area of wood that results in a high absorption of contaminants. This simple, low-cost, and green preparation method has promoted the sustainable utilization of biomass resources with great significance for practical environmental remediation.

Mechanical and microstructure characterisation of 2.5D C/C-SiC composites applied for the brake disc of high-speed train

Carbon fibre reinforced silicon carbide (C/C-SiC) composite materials have attracted increasing attention in brake system of high-speed trains, due to their low density, high specific strength, thermal stability, and frictional wear performance. This study aims to explore the mechanical properties of the 2.5D C/C-SiC composites, in order to characterise its potential application to the friction braking system of high-speed trains. To comprehensively evaluate the mechanical performance of the 2.5D C/C-SiC composites, the chemical vapor infiltration (CVI) method was adopted to prepare novel samples with high densification and low content of residual Si. The mechanical properties and failure mechanisms of the composites were investigated under various loading conditions, including tension, compression, bending, and shear tests. The experimental results indicated that the 2.5D C/C-SiC composites exhibited superior mechanical properties, with average in-plane tensile strength, compressive strength, bending strength, and interlaminar shear strength reaching 127.67 MPa, 326.92 MPa, 355.74 MPa, and 9.77 MPa, respectively, which are 2–8 times higher than the mechanical properties of C/C-SiC composites in existing publications. Under different loading conditions, the composites demonstrated characteristics of ductile fracture, pseudo-plastic compression fracture, pseudo-plastic bending fracture, and brittle shear fracture. Both high fibre content and the formation of SiC structure were advantageous for enhancing the load-bearing performance of C/C-SiC composites. The outstanding mechanical properties of the 2.5D C/C-SiC composite render the brake disc highly resistant to deformation and cracking under high stress, thereby enhancing its durability and establishing it as the ideal material for the next generation of high-speed train brake discs.

Siloxane-based segmented poly(urethane-urea) elastomers with enhanced mechanical properties, hydrophobicity and anti-calcification based on hierarchical phase separation for potential applications of polymeric heart valve

The polymeric heart valves have attracted considerable attention for treating valvular heart diseases with favorable biocompatibility, stability and adjustable mechanical properties. However, the thrombosis, calcification, and mechanical durability in the dynamic blood environment have limited further applications. Polycarbonate (PCDL) and polysiloxane (PDMS) had high flexibility, low toxicity, excellent hydrolytic and oxidative stability and efficient biocompatibility, but PCDL was susceptible to calcification and PDMS was susceptible to protein adsorption. To solve these difficulties, PDMS and PCDL were chosen as soft segments to combine the virtues of excellent biocompatibility, hydrolytic stability, and good anti-calcification and anti-protein adsorption, and a series of PCDL-PDMS poly(urethane-urea) elastomers (PCDL-PDMS PUU) were synthesized using a two-step polymerization reaction based on the amino-terminated polydimethylsiloxane (H2N-PDMS-NH2), polycarbonate diol (PCDL) and isophorone diisocyanate. The thermodynamic incompatibility between PDMS and PCDL and hierarchical hydrogen bonds were used to construct microscopic micro-separation, enabling the combination of enhanced mechanical strength, toughness, tear resistance and biocompatibility. The effect of the molar ratio of PCDL and PDMS and hierarchical H-bonding interactions on the physical properties, biostability and biocompatibility of PCDL-PDMS PUU were investigated. It was found the PCDL-PDMS PUU could achieve relatively stable mechanical strength behavior, and the Young’s modulus and tear strengths increased with the increasing proportion of H2N-PDMS-NH2. Meanwhile, PCDL-PDMS PUU showed low water uptake rate lower than 2 %, very good anti-hydrolysis performance, low hemolysis rate of less than 2 % and low adhesion to platelet cells. What’s more, PCDL-PDMS PUU showed enhanced calcification resistance when compared with a commercial polyurethane, ElastollanTM 1180A. Therefore, the results demonstrated that PCDL-PDMS PUU elastomers showed great potential to be explored as heart valves material considering high biostability, superior biocompatibility and mechanical performances.

A new CsPbI2Br/CuZnSnSSe/Si tandem solar cell with higher than 32 % efficiency

To avoid Shockley-Queisser limit in single p-n junctions, tandem solar cells were proposed. A new tandem cell is simulated here using the 1-dimensional SCAPS. The new cell combines two reported single solar cells together, aiming at achieving high performance by optimizing various layer characteristics. The bottom sub-cell is Mo/Si(p)/CZTSSe(p)/CdS(n)/ZnO(i)/ZnO(Al), where ZnO is an electrodeposited transparent-conductor oxide, with high UV transmittance, ZnO(i) is intrinsic layer, CZTSSe/Si is bi-absorber layer of p-CuZnSnSSe and p-Si, Mo is back contact. The optimized sub-cell exhibits a high fill factor of 85.18 % with overall efficiency 20 %. Based on literature, a perovskite CsPbI2Br layer is included in the top sub-cell Cu2O(HTL)/CsPbI2Br)/TiO2(ETL), where Cu2O is a hole-transport layer and TiO2 is electron-transport layer. The top sub-cell layers have been carefully selected for best alignment. Matching and optimizing various parameters in the two sub-cells is a simulation challenge. Therefore, layers in the two sub-cells have been studied separately, keeping in mind the proper combinations between various layers. With optimized layer thicknesses and band gaps, together with proper alignment of band edges, the proposed tandem solar cell exhibits high characteristics of 80 % fill factor and higher than 32 % overall efficiency.

Experimental study on hybrid fiber reinforced high performance concrete properties: Investigatingcomprehensive energy consumption capacity

The brittleness of concrete in tension increases with higher compressive strength, necessitating the study of cementitious composites that combine high strength and ductility. This study investigated the fresh and mechanical properties of high-strength concrete (HPC) incorporating fibers: steel, basalt, and polyethylene (PE). Hybrid fiber reinforced high performance concrete (HyFHPC) was created by tailoring mix proportions, with eight series of specimens cast, each carrying different fiber combinations alongside plain HPC as a reference. Compressive, tensile, and flexural tests were conducted, and results were analyzed. The electron microscopy techniques were employed to observe the microstructural morphology and perform elemental analysis of the composites. Findings revealed a dense HPC matrix with 1.5 % total fiber content, exhibiting uniform dispersion and ideal bonding. Digital image correlation (DIC) technique was utilized to analyze strain distribution and crack development of specimens in tension and bending, proving effective in tracking the strain and crack evolution. Statistical analysis of five influencing elements—cubic compressive strength, axial compressive strength, tensile energy dissipation, tensile fracture energy, and energy release rate—revealed Series F9 (S0.5P1) as optimal for comprehensive energy consumption performance. HyFHPC demonstrated enhanced strength, ductility, and toughness compared to HPC with mono fiber or none. The hybridization achieved positive synergy and improved reinforcing efficiency while conserving resources.