Scalability Control Research Articles

Crystallinity improvement of physical-vapor-deposited WS2 films by controlling particle energy

Abstract High-crystallinity WS2 thin films are crucial for their applications in next-generation electronic devices, particularly in logic transistors. Consequently, a physical vapor deposition (PVD) is a promising method for achieving such films because of its scalability and precise control over deposition conditions. In this study, WS2 thin film crystallinity has been investigated by controlling particle energy during the PVD process. The scattering of particles is influenced by Ar pressure, in which a throw distance of the particles is determined. The target-substrate (T-S) distance was set in conjunction with the throw distance in terms of the composition and energy of the particles at the surface on the wafer. The crystallinity of the deposited films was assessed through Raman spectroscopy, X-ray photoelectron spectroscopy, and circular transmission line method measurements. Controlling of T-S distance and Ar pressure effectively minimized film damage during the PVD process, resulting in crystallinity improvement. Therefore PVD-WS2 films hold significant potential for various applications in next-generation logic transistors.

Exploring the Potential of High-Power Impulse Magnetron Sputtering for Nitride Coatings: Advances in Properties and Applications

High-Power Impulse Magnetron Sputtering (HiPIMS) has emerged as an excellent technology for producing high-quality nitride coatings, such as aluminum nitride (AlN), titanium nitride (TiN), chromium nitride (CrN), and silicon nitride (SiN), and composite nitride coatings such as titanium aluminum nitride (TiAlN), TiAlNiCN, etc. These coatings are known for their exceptional hardness, thermal stability, and corrosion resistance. These make them ideal for high-performance applications. HiPIMS distinguishes itself by generating highly ionized plasmas that facilitate intense ion bombardment, leading to nitride films with superior mechanical strength, durability, and enhanced thermal properties compared to traditional deposition techniques. Critical HiPIMS parameters, including pulse duration, substrate bias, and ion energy, are analyzed for their influence on enhancing coating density, adhesion, and hardness. The review contrasts HiPIMS with other deposition methods, highlighting its unique ability to create dense, uniform coatings with improved microstructures. While HiPIMS offers substantial benefits, it also poses challenges in scalability and process control. This review addresses these challenges and discusses hybrid, bipolar, and synchronized HiPIMS solutions designed to optimize nitride coating processes. Hybrid HiPIMS, for instance, combines HiPIMS with other sputtering techniques like DCMS or RF sputtering to achieve balanced deposition rates and high-quality film properties. Bipolar HiPIMS enhances process stability and film uniformity by alternating the polarity, which helps mitigate charge accumulation issues. Synchronized HiPIMS controls precise pulse timing to maximize ion energy impact and improve substrate interaction, further enhancing the structural properties of the coatings. Hence, to pave the way for future research and development in this area, insights of the HiPIMS have been presented that underline the role of HiPIMS in meeting the demanding requirements of advanced industrial applications. Overall, this review article comprehensively analyzes the recent strategies and technological innovations in HiPIMS and highlights the significant potential of HiPIMS for advancing the nitride coating field.

High‐Throughput Combinatorial Metal Complex Synthesis

High‐throughput combinatorial metal complex synthesis has emerged as a powerful tool for rapidly generating and screening diverse libraries of metal complexes, enabling accelerated discovery in fields such as catalysis, medicinal chemistry, and materials science. By systematically combining building blocks (BBs) under mild and efficient conditions, researchers can explore broad chemical spaces, increasing the likelihood of identifying complexes with desired properties. This method streamlines hit identification and optimisation, especially when integrated with high‐throughput screening (HTS) and data‐driven approaches like machine learning. Despite challenges such as scalability and purity control, recent advancements in automation and predictive modelling are enhancing the efficiency of combinatorial synthesis, opening new avenues for the development of metal‐based catalysts, therapeutic agents, and functional materials.

High-Throughput Combinatorial Metal Complex Synthesis.

High-throughput combinatorial metal complex synthesis has emerged as a powerful tool for rapidly generating and screening diverse libraries of metal complexes, enabling accelerated discovery in fields such as catalysis, medicinal chemistry, and materials science. By systematically combining building blocks (BBs) under mild and efficient conditions, researchers can explore broad chemical spaces, increasing the likelihood of identifying complexes with desired properties. This method streamlines hit identification and optimisation, especially when integrated with high-throughput screening (HTS) and data-driven approaches like machine learning. Despite challenges such as scalability and purity control, recent advancements in automation and predictive modelling are enhancing the efficiency of combinatorial synthesis, opening new avenues for the development of metal-based catalysts, therapeutic agents, and functional materials.

Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks

In the past two decades, advancements in thermochemical technologies have improved biomass gasification for distributed power generation, enhancing efficiency, scalability, and emission control. This study aims to optimize syngas production from biomass gasification by comparing two computational models: a quasi-equilibrium thermodynamic model implemented in Aspen Plus and an artificial neural network (ANN) model. Operating at 850 °C with varying steam-to-biomass (S/B) ratios, both models were validated against experimental data. Results show that hydrogen concentration in syngas increased from 19.96% to 43.28% as the S/B ratio rose from 0.25 to 0.5, while carbon monoxide concentration decreased from 24.6% to 19.1%, consistent with the water–gas shift reaction. The ANN model provided rapid predictions, showing a mean absolute error of 3% for hydrogen and 2% for carbon monoxide compared to experimental data, though it lacks thermodynamic constraints. Conversely, the Aspen Plus model ensures mass and energy balance compliance, achieving a cold gas efficiency of 95% at an S/B ratio of 0.5. A Multivariate Statistical Analysis (MVA) further clarified correlations between input and output variables, validating model reliability. This combined modelling approach reduces experimental costs, enhances gasification process control and offers practical insights for improving syngas yield and composition.

Hydrogel-based Soft Robotics for Surgical Machinery

Hydrogel-based soft robotics represent a transformative approach in surgical machinery, offering unparalleled adaptability, biocompatibility, and precision for minimally invasive procedures. Hydrogels, with their high water content and tunable mechanical properties, mimic soft biological tissues, making them ideal for applications in delicate surgical environments. This research explores the integration of hydrogel materials into soft robotic systems, focusing on their fabrication, actuation mechanisms, and performance in surgical applications. Key advancements include the development of stimuli-responsive hydrogels that enable precise control of movement and force, enhancing the capability to navigate complex anatomical structures. The study also examines computational models for simulating hydrogel behavior and optimizing robotic designs. While the potential benefits of hydrogel-based soft robotics in improving patient outcomes are significant, challenges remain in ensuring durability, scalability, and reliable control systems. This research aims to address these limitations by integrating advanced materials science with robotic engineering. By doing so, it contributes to the evolution of surgical technologies, paving the way for safer and more effective procedures.

Precision Macroporous Monoliths Made Using High‐Throughput Microfluidic Emulsification

Materials with pore sizes ranging from micrometers to millimeters find use in thermal insulation, acoustics, separation, and energy‐related applications. While several routes have been developed to manufacture such macroporous materials, current fabrication technologies remain limited in either scalability or pore size control. Herein, a scalable microfluidic approach is reported to create macroporous materials with precisely controlled pore sizes from monodisperse emulsion templates. Emulsion droplets produced in a parallelized step emulsification device are assembled by gravity and converted into macroporous monoliths by polymerization and drying. Microstructural analysis and model filtration experiments show that the pore windows of the bulk macroporous material can be tightly controlled and used for the size‐selective separation of particles from suspensions. By heat treating macroporous structures of selected compositions, one can also create bulk carbon and silica glass monoliths with unique cellular architectures for potential applications as filters, separation membranes, or catalyst supports.

Design of experiments optimization of N,N,N-trimethyl chitosan synthesis using N,N-diisopropylethylamine base

This study presents a novel synthesis method of N,N,N-trimethyl chitosan (TMC) by using a non-nucleophilic base and optimizing the solvent system for enhanced scalability, while addressing critical factors such as viscosity management and stirring efficiency. The study objectives also included achieving high N,N,N-trimethylation without O-methylation while minimizing reagent use. Eight bases, three solvent systems, and varying levels of dilution were explored to mitigate viscosity challenges and gas evolution. 1H NMR spectroscopy was used to characterize the TMC products. The integral values of the peaks at 3.3, 3.0, and 2.8 ppm, corresponding to trimethyl, dimethyl, and monomethyl groups, were used to quantify the methylation degrees. The most promising initial results were obtained with N,N-diisopropylethylamine (DIPEA) base, and DMF as solvent. Using 6 eq methyl iodide (MeI) relative to chitosan and DIPEA as base, up to 68 % DTM was achieved. Applying Design of Experiments (DoE), the method was further optimized under diluted conditions, crucial for industrial scalability and viscosity control. Results from a full factorial design (32) revealed that diluted medium effectively prevented viscosity concerns, achieving a notably low viscosity of 5.9 cP in the reaction mixture, a 16-fold decrease in viscosity, compared to initial experiments. It was also established that both the MeI reagent and the base addition are significant factors for the DTM response, with both factors showing quadratic effects. The DoE model demonstrated high significance (R = 0.97), high precision for future prediction (Q2 = 0.87), good model validity (0.84) and excellent reproducibility (0.96). The results mark a notable advancement in TMC synthesis, offering an efficient and practical method with significant implications for industrial applications.

Modeling indoor thermal comfort in buildings using digital twin and machine learning

Digital Twin (DT) concept is used in different domains and industries, including the building industry, as it has physical and digital assets with the help of Building Information Modeling (BIM). Technologies and methodologies constantly enrich the building industry because the amount of data generated during different building stages is considerable and has a tremendous effect on the lifecycle of a building. Previous research underscores the importance of seamlessly exchanging information between physical and digital assets within a comprehensive framework, particularly emphasizing the integration of BIM data with various systems to enhance efficiency and prevent information loss. Despite advancements in technologies, challenges persist in optimizing methods for integrating BIM data into DT frameworks, including ensuring interoperability, scalability, and real-time monitor and control. This study addresses this research gap by proposing a comprehensive platform that integrates the DT concept with IoT and BIM technologies. The platform is developed in five main stages: 1) acquiring electronic data of the building from the laser scanner, 2) developing a Wi-Fi IoT module and BIM data for physical assets and digital replica, 3) constructing the DT elements of the platform, 4) performing data analysis 5) implementing thermal comfort prediction models. Two machine learning models (Facebook prophet, NeuralProphet) are implemented to predict thermal comfort. The best predictive model is identified by evaluating its error function using historical training data collected during facility operation. A case study demonstrates the practical application of the proposed framework. The case study involves a real building where the platform is implemented to monitor and control indoor environments. By utilizing predefined data in BIM models, the platform ensures data accuracy, consistency, and usability. The case outputs reveal that Neuralprophet provides good prediction results.

Electromechanical model for electro-ribbon actuators

The emerging field of soft actuators is anticipated to significantly impact areas that require enhanced adaptability and safe interaction including biomedical robotics. The electro-ribbon actuator, characterised by its compliance, lightweight, high efficiency, extensive scalability, and direct electrical control, emerges as a promising candidate for the future of soft robotics. Although the electro-ribbon actuator demonstrates efficient performance, a comprehensive understanding of its fundamental mechanics remains unexplored. To explain the impact of the actuation mechanism and the design parameters on the performance of the actuator, we have developed a mathematical model grounded in large deformation beam theory. As evidenced by our experimental results, this model accurately predicts the quasi-static behaviour of electro-ribbon actuators. Utilising this model, we establish a strategic road map for the development of electro-ribbon actuators with a range of passive stiffness tailored to achieve required output forces and displacement. This study provides a pivotal foundation for the model-based control and design optimisation of electro-ribbon actuators.

Ionization and neutral gas heating efficiency in radio frequency electrothermal microthrusters: The role of driving frequency

The development of compact, low power, charge–neutral propulsion sources is of significant recent interest due to the rising application of micro-scale satellite platforms. Among such sources, radio frequency (rf) electrothermal microthrusters present an attractive option due to their scalability, reliability, and tunable control of power coupling to the propellant. For micropropulsion applications, where available power is limited, it is of particular importance to understand how electrical power can be transferred to the propellant efficiently, a process that is underpinned by the plasma sheath dynamics. In this work, two-dimensional fluid/Monte Carlo simulations are employed to investigate the effects of applied voltage frequency on the electron, ion, and neutral heating in an rf capacitively coupled plasma microthruster operating in argon. Variations in the electron and argon ion densities and power deposition, and their consequent effect on neutral-gas heating, are investigated with relation to the phase-averaged and phase-resolved sheath dynamics for rf voltage frequencies of 6–108 MHz at 450 V. Driving voltage frequencies above 40.68 MHz exhibit enhanced volumetric ionization from bulk electrons at the expense of the ion heating efficiency. Lower driving voltage frequencies below 13.56 MHz exhibit more efficient ionization due to secondary electrons and an increasing fraction of rf power deposition into ions. Thermal efficiencies are improved by a factor of 2.5 at 6 MHz as compared to the more traditional 13.56 MHz, indicating a favorable operating regime for low power satellite applications.

COMMAND –LINE INTERFACES FOR MACHINE LEARNING: ENHANCING ACCESSSIBILITY AND EFFICIENCEY

This research paper aims to explore the integration of command-line interfaces (CLIs) in machine learning workflows to enhance accessibility, efficiency, and user experience. Traditional graphical user interfaces (GUIs) dominate the machine learning ecosystem, but CLIs offer unique advantages, particularly in resource – constrained environments and for automation purposes . The field of machine learning (ML) has witnessed rapid advancements, leading to the development of sophisticated models and algorithms. However, the accessibility and efficiency of employing these powerful tools remain significant challenges, especially for practitioners with diverse skill levels. This paper explores the role of command- line interfaces (CLIs) as a solution to address these challenges in the context of machine learning workflows. CLIs have a long-standing history in software development and system administration, providing a text-based interface for interacting with computer programs. In recent years, the integration of CLIs in the machine learning ecosystem has gained traction due to their ability to streamline and automate complex tasks This paper reviews the current landscape of ML CLIs, highlighting their advantages in terms of reproducibility , scalability and version control.

Carbon nano-onions: Individualization and enhanced water dispersibility

Carbon nano-onions (CNOs) are a unique class of carbon nanomaterials, with a concentric fullerene-like structure and sp2-hybridized carbon atoms. Onions present high mechanical strength, good conductivity, the capability to intercalate alkali metals, showing promising applications in diverse fields, such as tribology, energy storage, and nanomedicine. Their production has been developed by several techniques, with the most interesting being the thermal annealing of carbon nanodiamonds because of the easy scalability (gram-scale) and morphologic control (size and shape form perfectly spherical to polygonal). However, this synthesis is commonly affected by the formation of strong aggregates larger than 100 nm, attributed to carbon soot formation or strong van der Waals interactions, hindering the work with small and individual particles, leading to an altered yield of functionalization. In this study we propose a method for CNO individualization based on strong acid treatment, yielding highly water dispersible nanoparticles. Analytical ultracentrifugation (AUC) analysis is employed as a technique to investigate the particle dimensions directly in dispersion, permitting an ensemble analysis free from further sample preparation bias. Moreover, we also provide a comprehensive evaluation of the common post-synthetic treatment methods, and their effects. By means of scanning transmission electron microscopy with medium angle annular dark field detector and electron energy loss spectroscopy (STEM-MAADF and EELS) we have shown images of individual CNOs. Their successful separation achieved in this study is significant for future research and applications in nanomedicine, electrochemistry, and materials composites, where sample homogeneity is critical.

Smart h‐Chain: A blockchain based healthcare framework with insurance fraud detection

AbstractIn today's smart applications, very frequently used data like electronic health records (EHR's) are highly sensitive and soft target to the unauthorized agencies. Data management, integrity, and security issues plague EHR systems. Insurance firms are prohibited by law and corporate privacy from sharing patient data, but because the data are not linked and synchronized among insurance providers, there has been a rise in healthcare fraud. A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. Building a system to securely manage and track insurance operations by combining data from all sources is required to combat health insurance fraud. Over time, blockchain grew in popularity, and the healthcare sector's interest in it led to the development of design concepts that addressed security issues. In this study, a blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, their caregivers, physicians, and insurance agents to access the clinical data of the patients. By using off‐chain storage and cryptographic key exchange through blockchain our approach also covers the scalability, integrity and access control issue that a healthcare system in general is facing.A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. A blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, caregivers, physicians, and insurance agents to access the clinical data of the patients.

A flux growth technique for high quality cubic boron arsenide bulk single crystals

The growth of single crystal cubic boron arsenide (c-BAs) has attracted considerable interest due to its high room-temperature thermal conductivity and high ambipolar electrical mobility. However, currently the only growth technique reported for c-BAs crystals is the chemical vapor transport (CVT) method, which exhibits several drawbacks with regard to size scalability and crystal quality control, thereby hindering the further advancement of this semiconductor material. Herein, we report a flux growth technique using liquid arsenic (l-As) as a reaction medium at high pressures for the growth of high-quality c-BAs crystals with several millimeters size. The outstanding properties, including high uniformity, lower defect density, and lower carrier concentration of the as-grown c-BAs single crystals from flux growth, have been verified via a combination of techniques including x-ray diffraction, Raman scattering, photoluminescence spectroscopy, and electrical transport measurements, in comparison with the CVT-grown crystals.

Achieving Concurrency Control: Practical Throttling Techniques for AWS Lambda

This technical paper explores nuanced approaches for managing concurrency in Lambda functions, shedding light on the intricacies of AWS services like SQS and Kinesis. It underscores the significance of reserved concurrency to regulate function execution, acknowledging its effectiveness while cautioning about its limitations, particularly in scenarios requiring scalable solutions. The focus extends to the advantages of SQS as a pull-based service, emphasizing its built-in concurrency control and dynamic scalability capabilities. The paper then delves further into Kinesis data streams, showcasing the distinctive architecture of shard-based scaling and introducing parallelization factors for precise control over Lambda concurrency. Notably, these concurrency control strategies are presented as a means to scale Lambda and as a mechanism to manage traffic downstream, considering potential technical constraints. The discussion highlights multiple factors in selecting an appropriate messaging service: cost, message replay capability, error-handling mechanisms, message processing order, and concurrency management. Collectively, these insights serve as a comprehensive reference for architects and developers striving to create efficient and scalable serverless applications within the AWS environment, addressing Lambda scalability and downstream traffic control challenges.

Vapor Deposited Zeolitic Imidazolate Framework-8 Derived from Porous ZnO Thin Films

In recent years, the vapor deposition of zeolitic imidazolate framework-8 (ZIF-8) has gained high attraction due to its good scalability, conformality, and thickness control. The present study provides new fundamental insights regarding the vapor deposition of ZIF-8 from zinc oxide (ZnO). During synthesis, ZnO thin films with different percentages of open porosity (14.5%–24%) were subjected to a 2-methylimidazole vapor for different conversion times (20 min–24 h). For the first time, the impact of the porosity of ZnO thin films onto the converted ZIF-8 is investigated. Grazing incidence X-ray diffraction reveals randomly oriented crystallites of ZIF-8, which already appear after 20 min of conversion. The thickness, roughness, and average particle height of the ZIF-8 layers increase with the conversion time, reaching values up to (172 ± 20) nm, (29 ± 3) nm, and (113 ± 8) nm, respectively, for ZIF-8 obtained from ZnO with 14.5% open porosity. At long conversion times (i.e., 24 h), the results hint at greater precursor porosities resulting in lower thicknesses of ZIF-8, as the thickness, roughness, and average particle height for ZIF-8 obtained from 24%-porous ZnO show values of (132 ± 20) nm, (25 ± 3) nm and (80 ± 8) nm, respectively. Additionally, the potential of the ZIF-8 layers as a photocatalyst for the degradation of the organic dye methylene blue was studied. The ZIF-8 enhances the degradation by approximately 8% when compared to degradation without a photocatalyst.

State-of-the-Art of the Swarm Ship Technology for Alga Bloom Rapid Monitoring

The swarm intelligence has become an interesting topic for employing of multi-agent robotics with specific purpose. The capability of multi-coordination, scalability and goals-oriented control in spatial and temporal environment are already concerned and proven for several applications such as in military patrol, and drones leader-follower coordination. In marine-based environment, swarm intelligence adopted by ASV or ROV has been used for water quality and environment monitoring with sufficient optimized results making it convenient for rapid assessments. In this paper, the arrangement for building a trusted cyber-physical systems for algal bloom rapid assessment using swarm ship technology were explained in state-of-the-art perspectives. The minimum requirements for sensing, vehicle controlling, and communication of this system with others were explored as well as algorithm chosen for the best known configuration to monitor algal bloom events before spreading so fast to larger area. Some models were explained to show the robustness of autonomous unmanned ship control. From this point of view, we concluded that swarm ship technology has become an important potential implementation for near real time in situ monitoring compared to other decision making method such as laboratory examination or remote sensing-based results. The results of this review open the opportunity to realization of swarm ship technology in cyber physical system for monitoring algal bloom in specific area near real time efficiently.

Flexible access control mechanism for cloud stored EHR using consortium blockchain

The implementation of cloud computing to store and access electronic health records has shown substantial benefits for both clinical organizations and patients in managing electronic health records. The prime security issue of cloud-based electronic health records is that the patient is physically unable to own a medical record whereas a clinical organization can maintain one for them. The latter may collude with centralized cloud servers. So, there is a vulnerability of such records being tampered with in order to hide the medical malpractices. So, maintaining data integrity and data privacy becomes a significant challenge when deploying cloud computing. Therefore, in this paper, a consortium blockchain-based cloud-stored electronic health record is proposed which provides data integrity, data privacy, storage scalability, and fine-grained access control. Each process in outsourcing electronic health records to the cloud is incorporated as a transaction in a consortium ethereum blockchain through smart contracts. Through smart contracts, an attribute-based contract key is generated for the users that can decrypt the encrypted data stored in the cloud. The attribute-based contract key allows only users who are authorized to access the information ensuring data privacy and fine-grained access control. Moreover, the proposed scheme is proved to provide tamper-proof although the medical records are controlled by a group of clinical organizations.

Exploration of Multiple Unknown Areas by Swarm of Robots Utilizing Virtual-Region-Based Splitting and Merging Technique

This paper describes an efficacious virtual-region-based shape control scheme for exploring an unknown occluded dynamic environment, likely to have multiple targets, by a swarm of robots. The traditional leader-follower strategy has been intertwined with the shape control technique to achieve group-splitting of the robotic swarm in multi-target or multi-path scenarios. If multiple passages are detected by the parent swarm during the navigational process, several sub-swarms with proportionate agents will be created for progressing through these separate paths. The splitting philosophy is dependent on the fitment of the virtual elliptical regions in each of the paths found ahead, which will guide their respective sub-swarms to navigate further. This work, subsequently, conceives a realistic condition where two or more pathways merge to form a single lane, leading to a unique target. In such situations, the sub-swarms (operating in those paths) rejoin and proceed to the goal as a unified unit. Therefore, in this work, two different features of a swarm robotics framework, namely splitting and merging, have been studied. The scalability control plan ensures strict cohesiveness amongst the agents (inside the mother swarm or any sub-swarm) during the exploration steps. To substantiate the efficacy of the proposed technique, simulation results along with hardware experiment are duly furnished in this article. Note to Practitioners—In recent years, autonomous robots have played an increasingly significant role in civil applications. The search and rescue operation is one such example. In this specific field, swarm robotic systems have the potential to significantly improve efficiency with faster search and rescue of victims, initial assessment and mapping of the environment, real-time monitoring, and surveillance operations. Motivated from this specification, in this work, we offer a novel trajectory planning technique for a robotic swarm employing the region-based shape control scheme with the leader-follower approach for achieving the splitting and merging. The proposed solution can be used in any kind of environmental circumstance, such as single or multiple targets, single or multiple pathways, and so on, such as a normal rescue operation in which several victims may be found in different locations. Moreover, to undertake complete surveillance, it would be cost-effective to deploy one large swarm and then split it into sub-swarms based on the requirements.