Core Parameters Research Articles

Design, modeling and experimental research of a multi-mode, fast-response piezoelectric control valve

To realize the precise regulation and fast response of the valve, a piezoelectric control valve with a simple and compact structure, and easy control is proposed. The control valve is divided into the valve core that consists of a sandwich piezoelectric transducer and a valve body. When a single excitation signal is applied to the transducer, the longitudinal vibration mode of the valve core is excited. The valve is opened due to the liner reciprocating motion of the end of the valve core. By adjusting the frequency of the excitation signal, the vibration mode of the transducer is switched between the first three modes of longitudinal vibration, which in turn adjusts the water output of the valve. Compared with the single-mode valve, the piezoelectric control valve has three working modes. Therefore, it has different adjustment precision and range for each mode. First, the electromechanical coupling model of the valve core and the mechanical model of the valve body are established by using the transfer matrix method. By analyzing the dynamic characteristics of the piezoelectric valve, the structure parameters of the valve core and valve body are optimized. Secondly, the prototype of piezoelectric valve is manufactured and assembled, and the validity of the proposed theoretical model is verified by the vibration measurement experiments. Finally, the flow performance and response speed of the piezoelectric valve are measured by experiments. The results show that the maximum pressure of the valve is 1 MPa when the assembly pre-pressure is 983 N. A sinusoidal-signal has the best excitation effect. Under the experimental conditions of sinusoidal excitation, a driving voltage amplitude of 250 V, and pressure of 1 MPa, the optimal driving frequencies of the piezoelectric valve in the first three operating modes are 33.8 kHz, 42.2 kHz, and 83 kHz, and the corresponding maximum flow rates are 3.3 mL/min, 1.15 ml/min, and 0.445 ml/min, respectively. The response performance of the piezoelectric valve is tested, indicating that the prototype can achieve stable operation within 3.5 ms after being powered on, and stop working within 5 ms after being powered off. The piezoelectric valve has good response characteristics and flow control ability, which has potential applications in the field of biomedicine.

The influence of droplet breakup model on the prediction of reactor core parameters during reflood conditions

During reflood conditions, spacer grids affect the complex flow dynamics inside the reactor core. They can breakup the dispersed droplets, thereby significantly reducing their diameters. This process enhances local heat and mass interfacial transfer, consequently reducing the peak cladding temperature (PCT). Therefore, accurately predicting droplet transport and size within a reactor core is very important for reactor safety analysis. Recently, the US. NRC TRACE code has been modified by extending its governing equations to incorporate a three-field approach that captures the dynamic behavior of droplet entrainment, transport, and their interactions with structures within the reactor core. The new TRACE (v5.0 patch 8) three-field framework incorporates droplet models accounting for entrainment from liquid pools, liquid films on vertical surfaces, and droplet breakup. This paper focuses on the influence of the spacer grid droplet breakup model on the behavior of reactor core during reflood transient. The selected droplet breakup model effectively captures the enhancement in interfacial heat transfer observed in experimental data. We evaluate the influence of the droplet breakup model on the three-field version of TRACE by comparing it to recent Rod Bundle Heat Transfer (RBHT) experiments. These experiments had previously indicated that TRACE tended to overpredict the PCT for various initial and boundary conditions. Subsequently, we conducted a detailed comparison between TRACE v5.0 patch 7 and the three-field TRACE (v5.0 patch 8) with and without the spacer grid droplet breakup model. Our findings demonstrate a significant improvement in predictive accuracy with the new breakup model compared to the base capability of TRACE v5.0 patch 7. This improvement results in more accurate predictions of PCTs and quenching times for the assessed conditions.

Orthotropic Material Parameters Identification Method of Stator Core and Windings in Electric Motors

The accurate equivalent orthotropic material parameters (OMPs) of stator core and windings are the basis of modal analysis in electric motors and the key to precisely predicting electromagnetic vibration and noise. A CNN (convolutional neural network)-MPGA (multi-population genetic algorithm) based OMPs identification method of stator core and windings for electric motors is proposed in this study. Firstly, the surrogate models between the material parameters and eigenmode frequencies are trained with CNN for stator core and windings. Then, MPGA is utilized to identify the equivalent OMPs of stator core and windings combined with tested eigenmode frequencies. Finally, two different classes of electric machines are selected to verify the method. The CNN-MPGA based method in this study can quickly and accurately identify OMPs of stator core and windings. It is significant for the calculation and suppression of electromagnetic noise for electric motors.

A Motion Planning Method for Visual Servoing Using Deep Reinforcement Learning in Autonomous Robotic Assembly

Assembly positioning by visual servoing (VS) is a basis for autonomous robotic assembly. In practice, VS control suffers potential stability and convergence problems due to image and physical constraints, e.g., field of view constraints, image local minima, obstacle collisions, and occlusion. Therefore, this article proposes a novel deep reinforcement learning-based hybrid visual servoing (DRL-HVS) controller for motion planning of VS tasks. DRL-HVS controller takes current observed image features and camera pose as inputs, and the core parameters of hybrid VS are dynamically optimized using a deep deterministic policy gradient (DDPG) algorithm to obtain an optimal motion scheme, considering image/physical constraints and robot motion performance. In addition, an adaptive exploration strategy is proposed to further improve the training efficiency by adaptively tuning the exploration noise parameters. In this way, the offline pretrained DRL-HVS controller in the virtual environment, where the DDPG actor–critic network is continuously optimized, can be quickly deployed to a real robot system for real-time control. Experiments based on an eye-in-hand VS system are conducted with a calibrated HIKVISION RGB camera mounted on the end-effector of a GSK-RB03A1 six degree-of-freedom (6-DoF) robot. Basic VS task experiments show that the proposed controller achieves better performance than the existing methods: the servoing time is 24% smaller than that of the five-dimensional VS method, a 100% success rate with the perturbed ranges of the initial position within 25 mm for translation and 20° for rotation, and a 48% efficiency improvement. Moreover, a planetary gear component assembly process case study, where the robot aims to automatically put the gears on the gear shafts, is conducted to demonstrate the applicability of the proposed method in practice.

Enhancing and monitoring spore production in Clostridium butyricum using pH-based regulation strategy and a robust soft sensor based on back-propagation neural networks.

Clostridium butyricumis a probiotic that forms anaerobic spores and plays a crucial role in regulating gut microbiota. However, the total viable cell count and spore yield of C. butyricum in industrial production are comparatively low. To this end, we investigated the metabolic characteristics of the strain and proposed three distinct pH regulation strategies for enhancing spore production. In addition, precise measurement of fermentation parameters such as substrate concentration, total viable cell count, and spore concentration is crucial for successful industrial probiotics production. Nevertheless, online measurement of these intricate parameters in the fermentation of C. butyricum poses a considerable challenge owing to the complex, nonlinear, multivariate, and strongly coupled characteristics of the production process. Therefore, we analyzed the capacitance and conductivity acquired from a viable cell sensor as the core parameters for the fermentation process. Subsequently, a robust soft sensor was developed using a seven-input back-propagation neural network model with input variables of fermentation time, capacitance, conductivity, pH, initial total sugar concentration, ammonium ion concentration, and calcium ion concentration. The model enables the online monitoring of total viable biomass count, substrate concentrations, and spore yield, and can be extended to similar fermentation processes with pH changes as a characteristic feature.

The effect of central flux trap on-line sampling on Tehran Research Reactor(TRR) core safety parameters

Introducing the Neutron Flux Trap Channel to research reactors is an effective method to increase the neutron flux utilization for conducting the irradiation experiment and radioisotope production. The maximum thermal neutron flux could be achieved due to increased moderation in the flux trap area surrounded by the fuel elements, while all safety parameters and criteria must be met during the operation of the reactor. Regarding the TRR operator’s report about induced positive reactivity due to the entrance of some samples during reactor operation, it is predicted that this region is over-moderated, and the moderator reactivity coefficient is positive locally. Hence, this study aims to analyze experimental observations and the reactor safety parameters during online sampling into the flux trap channel. In this analysis, TRR Core’s different operating cycles were studied, and static, kinetic, and dynamic parameters of the reactor core with or without a flux trap channel were evaluated. For this purpose, some experiments were conducted to determine the safety parameters resulting from the sample placement, and then calculation results were validated against the experimental ones. Results indicate that void and temperature reactivity coefficients in the flux trap channel are locally positive; for example, the temperature and void reactivity coefficients for operating cycles with 33 fuel assemblies are +3.60 pcm/°C and +18.39 pcm/%, respectively. If 56.7% void is created in this channel, about 1.080$ positive reactivity will be induced to the core.Finally, accident analysis was conducted during various scenarios of reactivity insertion of different samples, and the variation of reactor power, reactivity, and fuel and clad temperatures were estimated in different transients. It is indicated that the maximum fuel and cladding temperatures in the worst case will be 183 °C and 144 °C, respectively, less than the melting point of aluminum meat of fuel.

Effect of Non-Uniform Minerals Distribution on Hydraulic Fracture Evolution during Unconventional Geoenergy Exploration

To study the effect of the non-uniform distribution of minerals on the development of microcracks within the rock during hydraulic fracturing, a novel numerical model considering multiple random mineral distributions was designed. The model investigated the impacts of mineral grain size, composition, and spatial arrangement on fracture initiation and propagation. The results indicate that the presence of the hard-phase mineral quartz can alter the propagation path of fractures, and increase the width of hydraulic fractures. In coarse-grained granite, the range of crack deflection is maximized, while in medium-grained granite, it is more prone to forming convoluted elongated cracks. A higher quartz content in granite further contributes to the formation of complex crack networks. Simultaneously, the evolution of granite fractures and variations in breakdown pressure in heterogeneous granite were investigated, considering the influence of core parameters such as fluid injection rate, fracturing fluid viscosity, and horizontal stress difference. The research reveals that a high injection rate promotes straight-line fracture expansion. Moreover, modest fluctuations in fracturing fluid viscosity have minimal effects on fracture morphology. When the fracture development avoids quartz, under the influence of high horizontal stress differential, it clearly turns toward the direction of the maximum principal stress. This study can offer insights into innovative and optimized deep reservoir fracturing techniques.

A unified approach for the prediction of the effective properties of laminated composite cellular core

The performance of cellular core sandwich panels is influenced significantly by the choice of geometric and material parameters of the cellular core and skins. In order to determine the optimal design of sandwich panels, it is important to relate the geometric and material parameters of the cellular core to the resulting performance of the sandwich panel. Theoretical homogenisation models are computationally efficient and plays an important role in the inverse design of sandwich panel. This paper presents a methodology to determine the equivalent homogenised properties for composite cellular core systems. The methodology was developed based on a strain energy-based homogenisation approach. Unlike other existing models, the proposed model in this paper is applicable to different core shapes and different composite wall layer configurations. An existing model was further extended for comparison purposes to determine the equivalent properties of hexagonal composite honeycomb core. Results from both models were compared with finite element (FE) results for hexagonal honeycomb cores, and the predictions from the proposed model are also compared with FE results for different cellular core shapes. A good agreement was found between the predicted and FE results. The proposed model was found to be superior to existing models in its ability to consider different core shapes and composite wall layer configurations.

Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program.

We are developing 10 de novo population-level mathematical models in 4 malignancies (multiple myeloma and bladder, gastric, and uterine cancers). Each of these sites has documented disparities in outcome that are believed to be downstream effects of systemic racism. Ten models are being independently developed as part of the Cancer Intervention and Surveillance Modeling Network incubator program. These models simulate trends in cancer incidence, early diagnosis, treatment, and mortality for the general population and are stratified by racial subgroup. Model inputs are based on large population datasets, clinical trials, and observational studies. Some core parameters are shared, and other parameters are model specific. All models are microsimulation models that use self-reported race to stratify model inputs. They can simulate the distribution of relevant risk factors (eg, smoking, obesity) and insurance status (for multiple myeloma and uterine cancer) in US birth cohorts and population. The models aim to refine approaches in prevention, detection, and management of 4 cancers given uncertainties and constraints. They will help explore whether the observed racial disparities are explainable by inequities, assess the effects of existing and potential cancer prevention and control policies on health equity and disparities, and identify policies that balance efficiency and fairness in decreasing cancer mortality.

Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population

BackgroundAlthough the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.ResultsWe used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.ConclusionsThe SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.

Stochastic dynamics of a semiconductor laser with delayed optoelectronic feedback and noise

Semiconductor lasers(SLs), versatile tools in communication, medicine, and scientific research, demand close examination of their dynamic behavior. With their unique geometric forms and diminutive sizes, these lasers possess a heightened sensitivity to noise. In this paper, we delve into time-delay semiconductor laser systems, noise included, honing in on time-delays’ effects on noisy laser equations. Carrier density (N) and laser intensity (I) constitute the core parameters that define the performance of these lasers, and we scrutinize their dynamic trajectories. A cutting-edge method fusing stochastic center manifold theory with the stochastic averaging method enables us to probe the stochastic dynamical attributes of semiconductor laser equations. We uncover that the time-delay parameter (τ 1) produces varied consequences on the laser system’s stability under both positive and negative optical feedback conditions. The negative feedback typically bolsters laser performance, fostering increased stability, whereas the positive counterpart could incite instability. By deftly managing the time-delay parameter, one can fine-tune the dynamic performance of semiconductor lasers, mitigating output power fluctuations and tightening linewidths, thereby augmenting stability and spectral purity. Moreover, in the realm of positive optical feedback scenarios, adroitly modifying the time-delay parameter can quell instability to a degree, paving the way for a novel regulatory approach to optimize laser performance. This lays the groundwork for further enhancement of semiconductor laser performance, as well as curbing the detrimental effects of noise on their operation.

Exploiting local label correlation from sample perspective for multi-label classification via three-way decision theory

In multi-label classification, the expansion of output dimension seriously interferes learning performance, and even fails to build a joint prediction model. In order to restrain the proliferation of multi-label classifier’s hypothesis space, the current works focus on the application of global positive label correlation. However, the “black or white” mechanism ignore other possible forms of label correlation, such as negative or neutral correlation. By introducing the doctrine of the mean, three-way decision (3WD) theory provides a solution for in-depth research on local label correlation, and aims to handle the uncertainty of multi-label learning tasks. In this paper, a novel learning algorithm for multi-label joint classification, namely ML-3WD, is proposed by considering the 3WD label correlation from the perspective of samples. According to the weights of different features on any label, the comprehensive loss of each sample to three action strategies can be measured. Obviously, the 3WD rules for any label variable in multi-label output space is obtained. By aggregating the cutting thresholds between different labels, the division principles of 3WD label correlation are further established. Given any multi-label sample, the local fuzzy membership to co-occurrence or mutual state for label pair is examined based on kernelized fuzzy rough sets. The 3WD local label relevance of each sample is confirmed, that is, positive, negative or neutral. The global application strategy for multi-label classification is utilized to avoid over-fitting induced by local mining strategy. Based on the integral mean of the distribution of 3WD local label relevance in multi-label sample space, two different versions of empirical label relevance are constructed. By constraining the relative position between sub-separation hyperplanes, the 3WD label correlation distribution-based model for multi-label joint classification is designed. The experiment results on fifteen real world multi-label datasets reflect that our algorithm achieves good classification ability and versatility. The impact of core parameters on learning performance is also dissected.

Bridging gaps in groundwater management in International River Basin Districts (IRBDs) – through harmonized monitoring and data sharing

Study regionEurope, with focus on Scandinavia (Denmark, Norway, Sweden), the Baltic countries (Estonia, Latvia, Lithuania), Finland, Poland, and Ukraine. Study focusThe Water Framework Directive (WFD) aims to obtain harmonised holistic Europe-wide water management based on an integrated river basin management model. This study analyses the efficiency of national groundwater monitoring programmes in meeting cross-border demands for harmonised data in IRBDs. Emphasis is placed on the core parameters, oxygen content, pH, conductivity, nitrate, and ammonium, specified by the WFD to be monitored in surveillance monitoring programmes. The findings offer recommendations for national water authorities on infirmities in groundwater management and how they could improve data transparency and availability for enhanced long-term management of shared resources. New hydrological insights for the regionA questionnaire distributed to nine European countries indicates large national differences in the extent of groundwater monitoring. This study highlights several key recommendations for moving forward with transborder groundwater management. Firstly, it is imperative that all nations adhere to ISO standards for field practices and analysis. Secondly, transborder collaborative projects to harmonise e.g., design of monitoring programmes, sampling, and analysis and cooperation on data analysis must be established. Lastly, there is a need to enhance the accessibility of groundwater data and associated metadata. The achievement of the WFD objectives encounters obstacles, due to weak transborder integration and the effects related to administrative boundaries.

Nuclear data effect on BWR stability parameter uncertainty at stable core conditions

The objective of this study is to quantify the influence of nuclear data uncertainties on both steady-state and dynamic core parameters in BWRs, with a specific focus on the uncertainty quantification of stability parameters such as the decay ratio and resonance frequency at stable core conditions. This investigation is carried out within the framework of the OECD/NEA benchmark on the Oskarshamn-2 stability event that occurred in February 1999. The nuclear data uncertainties are propagated, using the in-house SHARK-X platform, through the 2-D lattice calculations using CASMO5 and followed by downstream static and dynamic calculations using and SIMULATE3 and SIMULATE-3 K, respectively. For steady-state, the uncertainty is estimated in terms of k-eff, radial and axial power peaking factors, void fraction, and average fuel temperature, while for the transient, the estimated uncertainties are obtained, for the first time, for the decay ratio and resonance frequency. The results indicate that nuclear data uncertainties have a non-negligible impact on most of the analyzed parameters. More specifically, the uncertainty in k-eff, radial and axial power peaking factors, nodal void fraction peak, and nodal fuel temperature peak for steady-state could reach about 0.5 %, 0.4 %, 2.8 %, 0.1 %, and 1.5 %, respectively, For transient, the uncertainty due to nuclear data for the two stability parameters could reach about 9 % and 2 % for the decay ratio and resonance frequency, respectively.

A numerical control machining tool path step error prediction method based on BP neural network

Step error calculation of numerical control (NC) machining tool path is a premise for generating high-quality tool path and promoting its application. At present, iterative methods are generally used to calculate step error, and the computation time increases when accuracy improves. Neural networks can be calculated on GPUs and cloud platforms, which is conducive to reducing computation time and improving accuracy through continuous learning. This article innovatively introduces a BP neural network model to predict step error values. Firstly, the core parameters required for step error calculation are taken as the data samples to construct the neural network model, and map to the same scale through Z-score normalization to eliminate the adverse effects of singular parameters on the calculation results. Then, considering only a small number of parameters determine theoretical values of step error, the Dropout technique can drop hidden layer neurons with a certain probability, which is helpful to avoid overfitting and used in the neural network model design. In the neural network model training, this paper adds the Stochastic Gradient Descent with Momentum (SGDM) optimizer to the back propagation of network training in order to improves the network’ stability and accuracy. The proposed neural network predicts step error of samples from three surface models, the results show that the prediction error decreases as sample training increases. After trained by 15% of the surface samples, the neural network predicts the step errors of the remaining samples. Compared with theoretical values, more than 99% of the predicted values have an absolute error less than 1 μm. Moreover, the cost time is only one-third of the geometric method, which verifies the effectiveness and efficiency of our method.

Methodology for Testing Key Parameters of Array-Level Small-Area Hafnium-Based Ferroelectric Capacitors Using Time-to-Digital Converter and Capacitance Calibration Circuits.

Hafnium-based ferroelectric memories are a promising approach to enhancing integrated circuit performance, offering advantages such as miniaturization, compatibility with CMOS technology, fast read and write speeds, non-volatility, and low power consumption. However, FeRAM (Ferroelectric Random Access Memory) still faces challenges related to endurance and retention susceptibility to process variations. Hence, testing and obtaining the core parameters of ferroelectric capacitors continuously is essential to investigate these phenomena and explore the potential solution. The traditional method for measuring ferroelectric capacitors has limitations in timing generation capability, introduces parasitic capacitance, and lacks accuracy for small-area capacitors. In this study, we analyzed the working principle of ferroelectric capacitors and designed a method to detect the remnant polarization, saturation polarization, and imprint offset of ferroelectric capacitors. Further, we further proposed a circuit implementation method. The proposed test circuit conquers these limitations and enables high-precision testing of ferroelectric capacitors, contributing to developing hafnium-based ferroelectric memories. The circuit includes a flip-readout circuit, a capacitance calibration circuit, and a voltage-to-time converter and time-to-digital converter (VTC&TDC) readout circuit. According to simulation results, the capacitance calibration circuit reduces the deviation of the capacitance by 84%, and the accuracy of the readout circuit is 5.91 bits, with a readout time of 150 ns and a power consumption of 1 mW. This circuit enables low-cost acquisition of array-level small-area ferroelectric capacitance data, which can guide subsequent device optimization and circuit design.

Influence of Nonlinear Analysis Technology on Damage Analysis of Asphalt Pavement Structure

As a means of predicting the damage pattern and design life of asphalt pavement structures, the reliability of damage analysis is highly dependent on the calculation accuracy of the pavement mechanical responses under wheel load. The nonlinear analysis, on its part, can practically describe the stress dependence of the modulus of granular materials and fine-grained soils, so that the mechanical responses of the wheel-loaded asphalt pavement structure can be obtained more accurately. Therefore, the correct application of nonlinear analysis technology is essential to obtain reliable damage analysis results. For this reason, computer program KENLAYER was utilized to explore the effects of stress adjustment methods and core parameters of nonlinear iterative analysis on the damage analysis results. According to the calculation results, this paper explains the reasons why the stress adjustment Methods 2 and 3 are not applicable to the structural analysis of pavements containing nonlinear granular materials in the case of thin surface layers, illustrates the effects of improper selection of the adjustment methods and each iteration parameter on the dominant damage pattern, finds out the reasons for unreliable calculation results due to improper selection of the iteration parameters, and makes corresponding suggestions for carrying out damage analysis accurately.

Residual Saturation Effects on CO2 Migration and Caprock Sealing: A Study of Permeability and Capillary Pressure Models

In CO2 geological storage, multiphase flow plays a vital role in the movement and distribution of CO2. However, due to the limitations of fluid buoyancy and capillary forces, CO2 encounters challenges in penetrating the caprock, and the potential for leakage remains a concern due to variations in injection conditions. The migration and distribution of CO2 in the process of CO2 geological storage in saline formations are determined by relative permeability and capillary pressure, which are key factors. Consequently, this study focuses on two essential models: relative permeability and capillary pressure models. A two-dimensional isothermal reservoir–caprock model was constructed, utilizing data from the Shenhua CCS demonstration project. The analysis indicates that the core parameters in the model are residual gas saturation and residual water saturation. Specifically, residual gas saturation governs the diffusion distance of CO2 within the reservoir–caprock system, while its combined effect with residual water saturation affects the permeation rate of CO2. Through the application of the Analytic Hierarchy Process (AHP) to analyze the impact of different models on caprock integrity, it was determined that when selecting caprock models and optimizing parameters, precedence should be given to models with lower residual saturation and caprocks that offer sufficient capillary pressure for optimal sealing effects. These research findings can serve as references for practical CO2 storage projects, providing guidance on activities such as adjusting water injection strategies and controlling gas injection pressures to optimize geological storage efficiency.

Confinement-enabled infusion-alloying of iron into platinum nanoparticle for core-shell PtFe@Pt intermetallic electrocatalyst

Platinum-based intermetallics are efficient electrocatalysts for the oxygen reduction reaction (ORR) in polymer electrolyte fuel cells (PEFCs) because of their high catalytic activity and durability. However, conventional manufacturing methods are complex and require high-temperature heat treatment, resulting in reduced uniformity in particle size and composition. Therefore, a facile and efficient synthesis method was developed for intermetallic PtFe@Pt core–shell nanoparticles using ultrasound-assisted coating of FeOx and subsequent post-treatment. Fe atoms in the oxide coatings of Pt@FeOx/C samples were incorporated into the Pt lattices as the heat-treatment temperature increased, resulting in the formation of random PtFe alloy cores at 400 °C and intermetallic PtFe alloy cores at temperatures above 600 °C. Interestingly, PtFe@Pt/C samples annealed at different temperatures had similar particle sizes (5–6 nm) and their PtFe cores were enclosed in 1–2 layers of Pt shells. Electrochemical analysis indicated that compared to the random PtFe core, the intermetallic PtFe core exhibited superior ORR performance owing to the modification of the Pt electronic structure. Additionally, increasing the chemical ordering parameters of the intermetallic PtFe core improved durability. Therefore, ultrasound-assisted metal oxide coating is a promising approach for synthesizing advanced electrocatalysts and, consequently, enhancing the commercial viability of PEFCs as clean energy converting systems.

University-Campus-Based Zero-Carbon Action Plans for Accelerating the Zero-Carbon City Transition

After three decades of global climate initiatives, local governments’ capabilities to implement policies and solutions have not always been effective in making the urban environment more resilient and adaptive to climate change. All the previous climatic initiatives and decisions were mostly carried out by governments or affiliated actors on global or regional scales. However, the lack of notable climate actions at the community level is evident in the current crisis of urban sustainability. To drive a radical change toward a zero-carbon transition at the city scale, massive decarbonization is required at the institutional level (academic/nonacademic campus) of a city. Among all the nongovernmental actors, it is always expected that Higher Education Institutes (HEIs) would take the lead in promoting a resilient and sustainable future for the cities through their education, research, and innovation. HEIs’ multidimensional activity resembles the “small scale model of a city” interacting with different subsystems like education, administration, transport, housing, health, etc. However, the present studies were found to be mostly based on specific regions and developed countries. In addition, the previously developed methods of assessing energy consumption and CO2 emissions at the university level lack adaptability for other countries and urban settings. Following the need for a comprehensive method of evaluating energy consumption and accelerating the zero-carbon practice to a broader scale, a new framework is proposed here for a university campus. It can be implemented regardless of the campus type and geographic and weather conditions. After implementing the evaluation methods on a 753-acre campus of Rajshahi University, the campus typology and natural resources were identified. Following that, the behavior patterns of the users in terms of energy usage and waste generation were also determined. Finally, the results show that 1900.71 tons of CO2 was emitted in the academic year 2022. The per-capita CO2 emission was 0.041 tons of CO2. To boost the zero-carbon city transition, three core parameters of scaling-up methods were taken into consideration to evaluate the benefits of zero-carbon campuses. The scalability of the zero-carbon practices was evaluated based on the ideas of (1) expansion—how educating future generations about the environment can have a long-lasting impact, (2) demonstration—adopting innovative practical and technological solutions to exhibit the benefits of zero-carbon practices to society, and (3) collaboration—building strong alliances with state and nonstate actors of the city to promote sustainability through sharing knowledge, innovation, and technology.