Cycle Time Research Articles

Selection of optimal polymer joining technology: Insights and decision support through interrelated case studies using CARCACS method

Polymer materials play an essential role in the automotive, aerospace, packaging, and healthcare industries, necessitating reliable joining processes to ensure structural integrity, functionality, and cost efficiency. Choosing the optimal polymer joining technology involves evaluating various criteria, such as design freedom, investment, joint quality, process adaptability, cycle time, etc., for prioritization and informed decision-making. This study aims to systematically assess polymer joining processes to improve efficiency and sustainability in industrial applications. Two interrelated case studies are conducted using the CARCACS (“Criteria-wise Alternative Ranking and Correlation Analysis for Composite Scoring”) method. The first case study identifies laser transmission welding as the most suitable joining process among eight commonly used processes based on multiple evaluation criteria. The second case study refines this selection by examining various laser welding systems, with fibre lasers emerging as the optimal choice. Validation through comparison with other MCDM (Multi-Criteria Decision-Making) methods and Spearman's rank correlation coefficients reveals strong agreement and robust decision-making. Sensitivity analysis further confirms the stability of the top-ranked alternatives across different scenarios. This research provides a robust framework for selecting polymer joining technologies and demonstrates the practical implications for optimizing these technologies in industrial applications.

Vehicle emission models alone are not sufficient to understand full impact of change in traffic signal timings

Few studies have considered the real-world impact of changes in traffic signal timings on air pollution and pedestrian exposure with most only drawing their conclusion from vehicle emission models alone. Here, we consider two distinct cycle timings at a junction in London, UK, model the impact using a traffic microsimulation and a NOx emissions model, and compare these results with NOx and other air pollution measurements collected during a two-week field study at the junction.Our models predict that extending the cycle time leads to a 23% decrease in NOx emissions within a 15 m radius of the junction itself. When the wind direction was such that our sensors were downwind of the junction a 21% decrease in traffic and background-adjusted NOx concentrations were seen, suggesting that the intervention was successful. However, when the sensors were upwind of the junction, we observed an increase of 23% in adjusted NOx concentrations. Similar patterns were found for the other pollutants NO2, lung deposited surface area, black carbon and CO2 we measured. This indicates that meteorology was by far the greatest determinant of roadside concentrations during our two-week study period.Looking at pedestrian exposure for pedestrians waiting to cross the road, we found that their NOx exposure increased by 46% as waiting times to cross the road increased and that potential small reductions in air pollution were offset by increases in waiting times on the main road.The study demonstrates the need to go beyond assessing the impact of hyper-local traffic interventions on vehicle emissions. Real-world trials over extended periods are required to evaluate the impact of meteorology and changes to air pollution concentrations and pedestrian exposures.

Machine Learning in Small and Medium-Sized Enterprises, Methodology for the Estimation of the Production Time

Data mining (DM) and machine learning (ML) are widely used in production planning and scheduling. Their application to production time estimation leads to improved planning and scheduling accuracy, resulting in increased overall efficiency. Small and medium-sized enterprises (SMEs) often have a small amount of data, which results in the limited adoption of DM and ML. Instead, production time estimation is still performed using rough approximations, which are inaccurate and non-reproducible. Therefore, this article proposes an ML methodology for production time estimation. It is adapted to the needs of SMEs and is applied with limited data. The methodology is based on the categorization of four job types (from A to D), the partitioning of data according to the limit theorem of data convergence, and the definition of risk based on metrics of probability and statistics. ML was applied by WEKA Workbench (Waikato Environment for Knowledge Analysis). It is also integrated into the Cross Industry Standard Process for DM. The methodology was implemented on data from a medium-sized company, Schoepstal Maschinenbau GmbH, for job types A and B to estimate machine/job cycle time, manufacturing cycle time, and lead time. Different accuracies were obtained for individual estimation models, confirming the strong dependence of the models on data quality. Suitable models were found for the implementation of the estimation of the manufacturing cycle time and the machine/job cycle time. The modeling of lead time estimation was unsuccessful. This was due to the weak dependence between the learning values and the values of the selected model attributes. The implementation of the methodology for job types C and D is the subject of further research.

Preparation and optical characterization of 3D-TiO2 thin films with brilliant colors

In this paper, three-dimensional titanium dioxide (3D-TiO2) thin films with periodic structure were obtained by periodic pulse anodization of high-purity titanium wafers under specific alternating voltage (AV) using asymmetric sawtooth waveforms. The effect of voltage amplitude and electrolyte concentration on the structural color of the 3D-TiO2 thin films were investigated. It is shown that the nanotubes grown by asymmetric sawtooth wave at a specific AV have a “bamboo” shape, and the cycle length, as well as the inner and outer tube diameters of the nanotubes are closely related to the AV. The structural color of the samples was different at varying voltages and electrolyte concentrations for the same oxidation cycle time. The structural color of films grown under low voltage is analyzed to be related to the scattering and absorption of light on their surfaces. The thickness of the film grown under high voltage satisfies the conditions for light interference. The maximum reflected wavelength is red-shifted and then blue-shifted as the electrolyte concentration decreases. The study shows that the microstructure is the main influence on the color change of 3D-TiO2 thin films.

Surrogate model-based cognitive digital twin for smart remote maintenance of fusion reactor: modeling and implementation

Abstract A remote maintenance robot system (RMRS) plays a critical role in safeguarding the fusion energy experimental device’s security and stability. State-of-the-art intelligent technology such as cognitive digital twins (CDT) is widely considered capable of improving complex equipment’s performance and reducing management burden using a visualized system. However, the CDT virtual space cannot mirror the RMRS which is a kind of flexible multi-body system in real-time and with high fidelity. Therefore, we propose a cognitive digital twin (CDT) modeling method based on a surrogate model for the RMRS. Firstly, model-based system engineering (MBSE) is leveraged to build a structural modular architecture, which can decrease the modeling complexity of CDT and increase the modeling efficiency. Then, the surrogate models are self-learning within the CDT physical space, which reconstructs the RMRS’s real-time dynamic performances and endows CDT with cognitive capabilities. Finally, after integrating the CDT system, a smart decision-making plan that compensates for the operation error is generated for RMRS’s accurate control. We take a China Fusion Engineering Test Reactor (CFETR) multi-purpose overload robot (CMOR) as an example to demonstrate the implementation process. According to the results, CDT can achieve real-time (230 ms time delay) high-fidelity (5 mm control error) monitoring and accurate control, and CMOR conducts smart maintenance based on the simulation results. This method improves the efficiency of RM and provides solutions for high-duty cycle time of CFETR, it can also be applied to other tokamak fusion energy devices.

Kinetic Study and Process Optimization of Plutonium Barrier Units for Enhanced Plutonium Stripping in the PUREX Process

In the PUREX (the plutonium uranium reduction extraction) process, a plutonium barrier unit (1BXX) is used to achieve deep plutonium stripping. According to the operating experience of the French reprocessing plant, after the separation of uranium and plutonium in the first cycle (1B + 1BXX), the plutonium barrier unit has excellent stripping effect, such that the removal of plutonium from uranium can already be achieved in the first cycle, and the second cycle only needs to focus on the removal of neptunium from uranium in order to obtain a qualified uranium product. In recent decades, China has also been actively conducting research on the plutonium barrier unit process to reduce the plutonium concentration in the primary uranium product in the first cycle to avoid the need to remove neptunium and plutonium at the same time in the second cycle, and to improve the efficiency and feasibility of reprocessing. Due to the lack of design basis for plutonium barriers to achieve deep plutonium stripping at present, this study conducts a basic study on the plutonium barrier unit, aiming to provide data for the optimization of plutonium barriers in the actual reprocessing process at a later date. In this work, a kinetic study on the reduction and stripping of trace plutonium from dibutyl phosphate-containing organic phases was carried out first, and the kinetic equations for the reduction and stripping of Pu(IV) by U(IV) under flow process conditions were obtained. The effects of U(IV) addition on the extraction loss of U(IV) and the concentration distribution of U(IV) at various stages were investigated by process simulation. Additionally, the oxidation of U(IV) under process conditions was investigated to clarify the process chemistry of U(IV) oxidation and to provide a reference for the oxidation consumption of U(IV). Finally, the process parameters of the plutonium barrier unit were preliminarily designed based on the above research.

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Sustainable broiler chicken meat production: A three-layer model for emissions reduction and traceability

The world's major issues are climate change and global warming. The broiler chicken meat production industry plays a significant role in greenhouse gas emissions, which are responsible for global warming and climate change. The world urgently needs a new emission-free, eco-friendly, and sustainable meat production system. In this research, a three-layer broiler chicken meat production supply model is developed with greenhouse gas emissions reduction and an eco-friendly meat production system. The green technology investments and carbon tax policy are implemented to reduce greenhouse gas emissions from broiler chicken farmers and processors. The optimal cycle time, green investment, and number of shipments in the proposed model are computed in the Mathematica 9.0 software flatform by using the solution-finding algorithm. The concavity of the objective function is verified through two methods: mathematically, using the Hessian matrix concavity test, and graphically, by three-dimensional plotting of the objective function. The numerical examples, optimal solution table, results discussion, sensitivity analysis, and managerial implications are presented for the broiler chicken meat-producing industry. The emission cost of the processor and farmer are minimized by 5.29% and 15%, respectively, by the implementation of green technology investments. The AITP has increased by 1.01%. This study found that greenhouse gas emissions from the broiler chicken meat-producing industry can be significantly reduced by carbon tax implementation and utilizing renewable energy resources for energy requirements.

E-Procurement Systems and Organizational Performance in Germany

Purpose: The aim of the study was to analyze the e-procurement systems and organizational performance in Germany. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The implementation of e-procurement has streamlined procurement processes, reducing costs and improving efficiency through automation and better supplier management. Organizations adopting e-procurement systems report enhanced transparency, reduced procurement cycle times, and improved compliance with procurement regulations. Additionally, the data-driven nature of e-procurement allows for better decision-making and strategic sourcing, contributing to overall organizational effectiveness and competitiveness in the market. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), resource-based view (RBV) & institutional theory may be used to anchor future studies on analyze the e-procurement systems and organizational performance in Germany. Establish regular and comprehensive training programs tailored to the needs of procurement staff to ensure effective utilization of e-procurement systems. Develop a national policy framework that encourages e-procurement adoption in both public and private sectors.

Optimizing Quality Control on Electric Vehicle Production Lines with AI and Machine Learning

The study explores the use of AI and ML technologies to optimize quality control in EV production lines. By applying neural networks and machine learning algorithms, the research achieved significant improvements: a 74.4% reduction in torque deviation, a 75.9% enhancement in speed consistency, and a 70.8% decrease in defect rates. These gains also resulted in a 16.0% reduction in production cycle time and a 50.0% decrease in downtime, leading to an 8.4% increase in Overall Equipment Effectiveness (OEE). The methods employed included AI-driven predictive maintenance, real-time monitoring, and statistical process control (SPC). Despite the clear benefits, challenges such as integrating these technologies with existing systems and ensuring robust data infrastructure remain. Future research should focus on refining these approaches and extending their application across the automotive industry.

Membrane compaction in batch reverse osmosis operation and its impact on specific energy consumption

Reverse osmosis (RO) membrane water permeability is measured under cyclic pressure loading conditions corresponding to batch and semi-batch operation modes. Due to the viscoelasticity of the membrane support layer, compaction effects carry forward across multiple pressurization cycles, with the intracycle behavior stabilizing after a large number of cycles. The average membrane permeability of semi-batch RO systems is lower than that of batch RO, since semi-batch RO spends a larger fraction of its cycle time at higher pressures. The instantaneous permeability in batch RO decreases during the cycle as pressure is increased, but at a lower rate than the steady-state permeability variation with applied pressure. Therefore, the batch permeability values are lower at low pressures and higher at high pressures than the corresponding steady-state values. Since more water is recovered in the initial low-pressure stages of a multi-stage RO system, compaction increases the specific energy consumption of batch RO more than that of staged systems. Other loss mechanisms such as channel and minor pressure losses and high-pressure motor+pump efficiency variation with load further result in batch RO becoming energetically inferior to two- or three-stage RO, especially for low-salinity, high recovery applications.

Optimal robot task scheduling in cluttered environments considering mechanical advantage

Abstract In various industrial robotic applications, the effective traversal of a manipulator amidst obstacles and its ability to reach specific task-points are imperative for the execution of predefined tasks. In certain scenarios, the sequence in which the manipulator reaches these task-points significantly impacts the overall cycle time required for task completion. Moreover, some tasks necessitate significant force exertion at the end-effector. Therefore, establishing an optimal sequence for the task-points reached by the end-effector’s tip is crucial for enhancing robot performance, ensuring collision-free motion and maintaining high-force application at the end-effector’s tip. To maximize the manipulator’s manipulability, which serves as a performance index for assessing its force capability, we aim to establish an optimal collision-free task sequence considering higher mechanical advantage. Three optimization criteria are considered: the cycle time, collision avoidance and the manipulability index. Optimization is accomplished using a genetic algorithm coupled with the Bump-Surface concept for collision avoidance. The effectiveness of this approach is confirmed through simulation experiments conducted in 2D and 3D environments with obstacles employing both redundant and non-redundant robots.

Smartphone Assessment of the Sitting Heel-Rise Test.

The study presents a new approach for assessing plantarflexor muscles' function using a smartphone. The test involves performing repeated heel raises for 60 s while seated. The seated heel-rise test offers a simple method for assessing plantarflexor muscles' function in those with severe balance impairment who are unable to complete tests performed while standing. The study aimed to showcase how gyroscopic data from a smartphone placed on the lower limb can be used to assess the test. Eight participants performed the seated heel-rise test with each limb. Gyroscope and 2D video analysis data (60 Hz) of limb motion were used to determine the number of cycles, the average rise (T-rise), lowering (T-lower), and cycle (T-total) times. The number of cycles detected matched exactly when the gyroscope and kinematic data were compared. There was good time domain agreement between gyroscopic and video data (T-rise = 0.0005 s, T-lower = 0.0013 s, and T-total = 0.0017 s). The 95% CI limits of agreement were small (T-total -0.1118, 0.1127 s, T-lower -0.1152, 0.1179 s, and T-total -0.0763, 0.0797 s). Results indicate that a smartphone placed on the thigh can successfully assess the seated heel-rise test. The seated heel-rise test offers an attractive alternative to test plantarflexor muscles' functionality in those unable to perform tests in standing positions.

Ligand extension of aluminum fumarate metal-organic framework in transferring higher water for adsorption desalination

This article presents the synthesis and characterization of a new microporous metal organic framework (MOF) for adsorption desalination for the first time. The new MOF, designated as “Al-t,t-Ma”, is an analogue of aluminum fumarate (Al-Fum) and is fabricated via ligand extension method. Firstly, the MOF is synthesized, and then characterized by XRD, FTIR spectroscopy, TEM, SEM, TGA and N2 adsorption techniques. Secondly, the water adsorption experiments on the pristine Al-Fum and Al-t,t-Ma are performed for a wide temperature (30 °C ≤ T ≤ 80°C) and pressure (0 < P/Ps ≤ 0.9) ranges. The water adsorption isotherms, kinetics, hydro-thermal stabilities as well as the isosteric heat of adsorption (Qst) are evaluated. Thirdly, based on the experimentally confirmed isotherms, kinetics and isosteric heats data, the adsorption assisted desalination (AD) system is modelled and simulated. Finally, the performances of AD system employing Al-Fum and Al-t,t-Ma MOFs are calculated in terms of the performance ratio (PR) and specific daily water production (SDWP) under various half cycle times (100 s ≤ tcycle ≤ 1000 s) and regeneration temperatures (55°C ≤ Tregen ≤ 80°C). As compared with the pristine Al-Fum, the pore volume of Al-t,t-Ma MOF is found 115 % higher. The proposed Al-t,t-Ma MOF possesses higher water transfer (+57.5 % more) with promising hydro-thermal stability. Employing Al-t,t-Ma MOF as porous adsorbent, the SDWP is achieved 28 m3 per tonne of MOFs per day at the regeneration temperature of 55°C. These results confirm that Al-t,t-Ma MOF is a competent candidate for adsorption desalination.

Phase-field modeling of lithium dendrite deposition process: When an internal short circuit occurs

Lithium dendrite growth is the main trigger for internal short circuits (ISC) of lithium-ion batteries. This paper investigates the lithium dendrite growth during the whole short circuit evolution process. In this paper, the lithium dendrite growth is divided into the initial deposition of the initial operation and the secondary deposition due to the internal short circuit. Firstly, a two-dimensional model based on the phase-field theory is developed and the growth in static electrolyte is investigated. Secondly, we summarize the whole evolution process and analyze the internal structural changes as well as the external features due to the internal short circuit. Based on this analysis, a model of secondary deposition in a flowing electrolyte is proposed. The model takes into account the effect of the flowing electrolyte on the dendrites. Finally, we investigate the growth of dendrites during secondary deposition. Due to an internal short circuit that damages the separator, a flowing electrolyte appears around the damaged region. This flowing electrolyte leads to uneven growth of dendrites, with “splitting” and “branch inhibition”. This phenomenon will accelerate the continued deposition of dendrites and induce a secondary internal short circuit. This work provides a quantitative simulation model for dendrite growth when an ISC occurs. Moreover, it also provides a reference for designing the structure of liquid batteries with high cycle times and safety.

Experimental research on chemisorption energy storage performance for industrial waste heat recovery and conversion

This study investigates a solid chemisorption energy storage system utilizing a multi-component chloride salt composite adsorbent with a mass ratio of NH4Cl, CaCl2, MnCl2, and expanded natural graphite treated with sulfuric acid (ENG-TSA). The system features a shell-and-tube heat exchanger and analyzes refrigeration and heat storage performance under varying industrial low-temperature waste heat (60–110 °C) conditions, with condensation and evaporation temperatures of 0–25 °C and discharging temperatures of 40–60 °C. The results indicate that the multi-salt composite adsorbent reduces hysteresis between adsorption and desorption stages. The highest coefficient of performance (COP) and specific cooling power (SCP) were 0.461 and 328 W/kg, respectively, at an evaporation temperature of 25 °C and a charging temperature of 110 °C. Excessively long cycle times negatively affect system efficiency, with optimal performance achieved at a 40-min cycle. The findings highlight the system's effectiveness for industrial low-grade waste heat recovery, providing a solid foundation for future reactor design and material selection.

Integrating discrete event simulation and data envelopment analysis for system performance and efficiency evaluation

ABSTRACT Healthcare facilities, particularly outpatient clinics, are crucial for local communities’ medical needs. To enhance their performance, these facilities need to optimize their operations. Key system performance metrics, such as patient throughput, waiting times and cycle times, can quantitatively be measured using discrete event simulation (DES). DES replicates facility structures and behaviours, offering a valuable platform for assessing the configuration effects of resources on system performance. However, current DES tools cannot identify the best resource configurations for future performance improvement. To address this issue, this paper develops a DES model for an outpatient clinic and improves its performance through data envelopment analysis (DEA). The case study involves a clinic in Northern Malaysia, where relevant data were observed and collected. The analysed data were then input into the DES model. The model measured the clinic’s current performance and assessed the impact of its resource configurations. 37 different resource configurations were tested, and their relative efficiencies were evaluated using DEA. The analysis revealed room for improvement in resource allocation.

Formalization of Elements and Control Schemes in Intelligent System Models

The purpose of the work is to formalize the concepts and principles that form the basis of the mathematical model for controlling the life cycles of intelligent systems. The controlling elements formal description is part of a universal model of such systems. This model part is important for implementing complex intelligent systems with a large amount and diversity of knowledge flows and knowledge processing schemes performed simultaneously. The other two areas of this model are associated with a unified logical-algebraic description of the concept of knowledge representation formalism, as well as concepts related to the architecture of intelligent systems. Formalization of ideas about control in intelligent systems is carried out according to the principle of a hierarchy of controlling elements and processes. The elements of the mathematical model that implements this principle correspond to event-controlled automata with infinite memory and associated algorithms for the automata simulation in specific systems. The objectives of the research include the development of formats for describing the hierarchy of diagrams and assemblies of control elements in a universal model of an intelligent system, and diagrams of algorithms for the functioning of intelligent systems based on the processing of model descriptions. Models of intelligent system control elements hierarchy and execution algorithms for the sets of activated control elements for the implementation time of life cycles of intelligent systems are described by extending the special ontological structures. These structures implement customizations of universal control model

Progress in the Study of Quality and Mechanical Properties of Selective Laser Melting Molded Parts

Abstract: Selective laser melting (SLM) is considered to be a widely promising additive manufacturing technology with the advantages of high machining precision, high manufacturing freedom, and short cycle time, which is widely used in aerospace, the integration of medicine, and industry, chemical, and other fields. The research progress on the temperature field, stress field, forming quality, and mechanical properties during the SLM manufacturing process is reviewed. The study aims to systematically analyze how SLM process parameters affect the temperature field, stress field, forming quality, and mechanical properties, and to discuss the importance of the selection of process parameters and performance regulation to achieve high-quality, highperformance metal parts. The effects of SLM process parameters on temperature field, stress field, surface roughness, densification, hardness, strength, and fatigue properties are analyzed and summarized. The importance of process parameters in the SLM forming procedure in the quality of formed components is emphasized, and conducting an in-depth study on the optimization and performance regulation of the process parameters is of great significance in achieving the high quality and performance of metal parts. With the rapid advancement of technology, the potential of SLM technology in terms of molding quality and mechanical performance has become increasingly significant, heralding significant breakthroughs. These potential breakthroughs will greatly promote the widespread application of SLM technology in various industries, thus more efficiently meeting the growing needs and expectations of industries such as petrochemicals, transportation, aerospace, nuclear energy, as well as food and medical sectors.

Synergistic ionic liquid encapsulated MIL-101 (Cr) metal-organic frameworks for an innovative adsorption desalination system

The transformations of brackish water into freshwater employing porous materials such as metal-organic frameworks (MOFs) show a great potential for a green environment. But the adsorption-based desalination slows down due to slow water adsorption/desorption rates and inadequate water storage capacity in conventional adsorbents. Therefore, the restructuring of porous MOFs increases water storage and transfer rates. This paper presents a comprehensive investigation on the fabrication of ionic liquid encapsulated MIL-101 (Cr) MOFs (metal-organic frameworks), and its interactions with water for desalination applications. Firstly, the pristine MIL-101 (Cr) is encapsulated with various types/amounts of ionic liquids. Next, these MOFs are characterized via SEM (Scanning electron microscope), FTIR (Fourier transform infrared), XRD (X-ray diffraction), TGA (Thermal gravimetric analysis), N2 adsorption techniques. The water adsorption on these MOFs is performed experimentally for a wide temperature (30–80 °C) and pressure (0 < P/Ps < 0.95) ranges. The effects of the size (or type) as well as the encapsulation ratio of ionic liquids on water adsorption behaviors are conducted. Based on the experimentally proven water adsorption (isotherms and kinetics) data, an AD (adsorption desalination) system is modelled and simulated. Finally, the AD performances in terms of the specific daily water production (SDWP) and performance ratio (PR) are parametrically studied with respect to various regeneration temperature (50–80 °C) and cycle times (100 s–1000 s). It is found that by ionic liquid encapsulation, the water uptakes/offtake rates are expedited up to 48% (for adsorption)/55% (for desorption), respectively. In addition, the water transfer (Δq) improves up to 45% more than the parent MIL-101(Cr) MOFs. Furthermore, the SDWP increases from 38 to 56 m3 of water per tonne of MOFs per day at the regeneration temperature of 70 °C. The simulation results also show that the ionic-liquid-encapsulated MIL-101 (Cr) MOFs generates water (>25 m3 water per tonne of IL-MOFs) at the regeneration temperature of 50 °C and is potential for the next generation desalination applications.