Application Of Processes Research Articles

ZSM-5 Zeolite Synthesis from Coal Fly Ash Synthesised Silica: Sole Silica & Alumina Source.

This study explores the synthesis of ZSM-5 zeolite using high-purity mesoporous silica exclusively derived from coal fly ash (CFA), eliminating the need for additional silica or alumina sources. Traditional ZSM-5 synthesis relies on costly and environmentally harmful pure chemicals, whereas this approach utilizes CFA, an industrial byproduct, addressing both cost and sustainability concerns. The synthesized ZSM-5 zeolite demonstrates exceptional purity, with a surface area of 455.24 m2/g, and exhibits unique structural properties, confirmed through XRD, SEM, TEM, FTIR, TGA, and BET analyses. This method highlights the potential of CFA-derived silica as a sustainable feedstock for zeolite production, promoting both environmental sustainability and cost-effective industrial applications in catalysis, adsorption, and separation processes.

Effect of Minor Reinforcement with Ultrafine Industrial Microsilica Particles and T6 Heat Treatment on Mechanical Properties of Aluminum Matrix Composites

This study examines the use of ultrafine (~128 nm) microsilica (composed of a mixture of amorphous and microcrystalline silicon dioxide phases) particles, an industrial waste product, as a reinforcing material to create aluminum matrix composites (AMCs) via ultrasonic-assisted stir casting followed by T6 heat treatment. This study aimed to improve the mechanical properties of pure aluminum, which has insufficient strength for most engineering applications. The main objective of this study is to develop environmentally and economically efficient AMCs with improved properties, namely, the balance between strength and ductility, for further application in caliber rolling processes. Attention is also paid to minor reinforcements using a low concentration of microsilica (~0.36%wt), which minimizes the problems with the wettability of the reinforcing material particles. The composites reinforced with ultrafine microsilica exhibited enhanced mechanical performance, including a 59.7% increase in Vickers microhardness and a significant improvement in tensile strength, reaching 73 MPa. Additionally, T6 heat treatment synergistically improved ductility to 60.3% elongation while maintaining high strength, achieving a balanced performance suitable for forming processes. The study results confirm that using microsilica as a reinforcing material is an effective way to improve the performance of aluminum alloys, while minimizing costs and solving environmental problems.

Abstract A007: Artificial intelligence in radiation therapy: Bringing in a new era of cancer therapy, accurate and fast

Abstract Introduction: The application of AI in RT has been adopted as innovative method that seeks to enhance accuracy, effectiveness and patient outcome of cancer treatment. This aims to understand current and potential uses of AI-based tools in RT; from planning, monitoring during therapy, adaptive approach, and after treatment assessment. This study defines the role of AI in facilitating decision-making, made to define treatment plans, and to organize the clinical practice in order to improve the therapeutic outcomes and provide individualized approach to the cancer patients. Methods and Experimental Procedures: Radiation therapy work flows which integrated AI algorithms for treatment planning and delivery were examined in a retrospective manner. Imaging data was used to predict tumor behavior and develop the best treatment plan since the models employed by practitioners involved machine learning. Another application of AI was for the shape matching of its patient anatomy during treatment and came up with better matching for therapy adjustments. This involved data from multiple clinical centers employing AI-assisted techniques for prostate, lung as well head and neck cancers. These QA procedures implemented with the help of AI were assessed with respect to their capability in identifying errors and maintaining dose accuracy. Results: Initial data showed that AI optimizes accuracy and time in treatment planning by streamlining the processes of outlining tumors and relevant tissues; planning time is cut in half in comparison with the conventional approach. Real-time positioning indicated by AI optimized adaptive therapy gave accuracy in tracking the anatomical variations thereby justifying the alterations in radiation delivery that enhanced the tumor control without affecting normal tissue. Automated quality assurance systems with AI capabilities successfully detected possible mistakes in the application of treatment processes and thus, decisively enhanced patients’ safety and minimized risks of radiation mishaps. The data also showed that models using AI enhanced accuracy in dose prediction and thus the overall treatment plans and results especially when the anatomy of a patient is unique and cannot easily be navigated. Conclusions: AI is becoming integrated into RT since it delivers, precision, personalization and efficiency that the industry desires. Advanced AI-based technologies help in treatment planning, facilitate monitoring and improve the technique of adaptive RT while also strengthening QA measures thus making patients safer and providing better therapeutic results. In the future, emerging technologies in AI offers prospects to address problems in radiation oncology for improved diagnosis and treatment of cancer. There is need for more clinical and research practice and development to actualize AI applications, remove the constraints and apply these systems in regular clinical services. The future of RT is going to be with AI incorporated in all the departments to ensure that cancer treatment is optimized for the benefit of the patient. Citation Format: Newtone Kisia. Artificial intelligence in radiation therapy: Bringing in a new era of cancer therapy, accurate and fast. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr A007.

Easy and Straightforward FPGA Implementation of Model Predictive Control Using HDL Coder

Model Predictive Control (MPC) is widely adopted for power electronics converters due to its ability to optimize system performance under dynamic constraints. However, its FPGA implementation remains challenging due to the complexity of Hardware Description Language (HDL) programming. This paper addresses this challenge by introducing a straightforward methodology that simplifies FPGA implementation using MATLAB Simulink HDL Coder. It is shown that HDL Coder yields favorable synthesis outcomes, both in terms of area and time, compared to hand-coded HDL. Notably, the proposed method achieves a significantly reduced sampling step for the MPC algorithm—down to 32 ns—marking a substantial improvement over state-of-the-art implementations. The Integrated Logic Analyzer (ILA) IP available in the Vivado tool is used during the HIL testing phase to facilitate the real-time observation and analysis required for debugging and confirming the FPGA-implemented controller performance. Additionally, this paper discusses the advantages of utilizing HDL Coder for simplifying the FPGA programming process in power electronics applications and addresses the design challenges encountered using this methodology.

Advances in Programmable Control of Ostwald Ripening for Tailoring Material Properties: Challenges, Applications, and Future Perspectives.

During the past decade, enlightened by the better understanding of the mechanism of Ostwald ripening (OR), programmable control of OR process have gained popularity in the fields from nanocrystals to bulk materials in virtue of its important on regulating the structural and chem-physical properties. In this perspective, we systematically summarize the up-to date advanced applications of OR process involved in nanomaterials and bulk material properties. The potential challenges and perspectives for further research are highlighted. We envision that such research directions will be a base for future work and have a bright future especially for material science.

Tailoring a lignocellulosic biomass to simultaneously enhance N-doping and textural properties of porous carbons designed for effective environmental remediation.

Transforming lignocellulosic biomass waste into value-added materials like porous carbons offers a sustainable and increasingly important solution for its efficient management within a circular economy framework. Although the heteroatom-doping process enhances oxygen- or nitrogen-containing functionalities on porous carbons, it often leads to losses in structural integrity and other key functionalities. This study presents a novel protocol to produce N-doped porous carbons that efficiently introduces nitrogen groups while improving surface area, microporosity definition and the concentration of oxygen-containing functionalities. This protocol involves modifying the original lignocellulosic biomass by reducing its recalcitrance and remodeling its natural composition, followed by the mixing equivalent mass of chitosan and preceding a chemical activation process Compared to the parent material, the optimized tailored porous carbons exhibited a 15% increase in surface area (1689 m2 g-1) and 13% in microporous volume, along with rises of 22% and 20% in oxygen- and nitrogen-containing functional groups, respectively. Additionally, the anchoring mechanism, modeled using Advanced Statistical Physical Models (ASPM), based on the grand canonical ensemble in statistical physics, validated the surface versatility and heterogeneity of the porous carbons. This versatility is demonstrated by their above-average adsorption capacities for chemically distinct hazardous components in aqueous matrices - methylene blue (876 mg g-1), Pb2+ (44 mg g-1) and acetylsalicylic acid (169 mg g-1) - emphasizing the potential of these N-doped porous carbons for efficient and sustainable applications in aqueous remediation processes.

Integrating Zeolitic Imidazolate Framework-8 with DES-Treated Loofah Sponge for Enhanced Toluene Adsorption.

The pervasive presence of toluene in aquatic environments, primarily due to oil spills and industrial effluents, necessitates the development of effective and sustainable remediation strategies. This study introduces ZIF-8@DES-treated loofah sponge (ZIF-8@DLS), a novel adsorbent composite material, synthesized via an in situ process that integrates the high surface area of ZIF-8 with the natural loofah sponge. The composite was characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), confirming the successful loading of ZIF-8 onto the loofah substrate. The adsorption capacity of ZIF-8@DLS for toluene was evaluated, achieving a maximum of 145.3 mg/g with an efficiency of 72% under standard conditions. Kinetic studies revealed that the adsorption process predominantly followed a pseudo-second-order model, indicative of chemisorption. The adsorption isotherm data were found to align more closely with the Freundlich model, suggesting a multilayer adsorption mechanism on the composite's heterogeneous surface. The ZIF-8@DLS composite demonstrates a promising synergy of high adsorption performance, cost-effectiveness, facile synthesis, and environmental benignity, positioning it as a candidate for practical applications in wastewater treatment and adsorptive separation processes.

Machine learning approach for prediction of hole oversize using acoustic emission signal features in ultrasonic machining of inconel 718

ABSTRACT The material removal in the ultrasonic machining process is due to the fracture of the workpiece material. The fracture is owing to the energy transfer by vibrating abrasive particles. The vibrating particles impact the workpiece surface and machining takes place, leading to the size of the machining hole, which is of interest to understand to guarantee efficient machining with accuracy and precision. In the current investigation, the hole oversize (HOS) during the ultrasonic machining of Inconel 718 super alloy is attempted. An acoustic emission sensor is integrated with the machining setup, and signal information is extracted in the time and time-frequency domain. The features and process parameters are input to a support vector regression model to estimate HOS. The model developed for HOS prediction yields an accuracy of 97.87%. The developed model can be beneficial for the real-time monitoring of HOS during the ultrasonic machining process for industrial and remote applications.

Recent Advances in Nontargeted Screening of Chemical Hazards in Foodstuffs.

The emergence of several chemical substances continues to enrich and facilitate the development of food science, but their irrational use also poses a threat to food safety and human health. Nontargeted screening (NTS) has become an important tool for rapid traceability and efficient identification of chemical hazards in food matrices. NTS in food analysis is highly integrated with sample pretreatment, instrumental analysis platforms, data acquisition and analysis, and toxicology. This article is a systemic review of current sample preparation, analytical platforms, and toxicity-guided NTS techniques and provides the latest advancements in workflows and innovative applications of the NTS process based on mass spectrometric techniques. High-throughput toxicity screening platforms play an important role in NTS of unknown chemical hazards of complex food matrices. Advanced machine learning and artificial intelligence are increasingly accessible fields that may effectively process large-scale screening data and advance food NTS research.

The Application of Stochastic Processes in Financial Mathematics

Stochastic processes are crucial in financial mathematics, providing a framework to model the inherent uncertainties of financial markets. This paper explores stochastic process application across various financial domains, such as option pricing, risk management, and financial engineering. Through case studies, literature review, and case analysis, the study demonstrates the practical effectiveness of stochastic processes in finance. The findings highlight the adaptability and robustness of these models in capturing market dynamics and optimizing financial strategies. This research particularly focuses on the implementation of Brownian motion and Its processes in derivative pricing, revealing significant improvements in accuracy compared to traditional methods. Additionally, we examine the integration of machine learning techniques with stochastic models to enhance predictive capabilities in risk assessment. This study also addresses the challenges and limitations of current stochastic approaches, proposing innovative solutions for practical implementation. These insights contribute to both theoretical understanding and practical applications in quantitative finance, offering valuable guidance for practitioners and researchers in the field

Toward sustainable bioproducts from lignocellulosic biomass: Influence of chemical pretreatments on liquefied walnut shells

Abstract The depletion of resources and the generation of significant waste pose considerable environmental challenges. Post-utilization of walnut kernels leaves behind substantial amounts of shells as the unused residue. Walnut shells find application in various production processes, offering an opportunity to mitigate environmental impacts through resource utilization. This study investigates the influence of chemical pretreatment on the properties of liquefied lignocellulosic biomass, specifically focusing on walnut shells as a prominent lignocellulosic material. The results reveal that samples subjected to alcohol pretreatment exhibited the lowest degree of liquefaction (85.00% at 120°C for 45 min), while the highest degree of liquefaction was observed in samples after alkaline pretreatment (90.76% at 90°C for 15 min). Analyzing functional groups in liquefied walnut shell biomass, formed during the addition of polyhydric alcohols, glycols, and organic acids, underscores its potential for diverse bioproducts. Pretreatment significantly increases the hydroxyl (OH) number, irrespective of the type, temperature, and duration of chemical pretreatment. Compared to the untreated sample, alkali pretreatment produces the highest OH number (1288.03 KOH/g), surpassing mean values after acid and alcohol pretreatment. The results highlight the efficacy of chemical pretreatment in tailoring the properties of liquefied walnut shell biomass, addressing the challenges associated with resource depletion and waste accumulation.

International Patent Families: When to Apply

ABSTRACTWe model the decision of an innovating firm on where and when to make cross‐border patent applications given the rules of the Paris Convention and Patent Cooperation Treaty (PCT). We show that an innovating firm will have an incentive to apply at the beginning (end) of the Paris Convention application window when returns are front‐loaded (back‐loaded). The preliminary review provided by a PCT application expedites the review process in subsequent national applications, leading to the selection of firms with more productive innovations into the PCT route for cross‐border applications. We also show that the PCT's preliminary application may be used to provide information on the patentability of an innovation. To provide empirical evidence on these predictions, we compare the patenting behavior of semiconductor applicants with those of pharmaceutical applicants, where the former has among the shortest times from innovation to market and the latter the longest. Consistent with the predictions of the model, we find that pharmaceutical applications are 15 percentage points likelier than semiconductor applications to be filed at the Paris deadline for national applications under the Paris convention. PCT applications for pharmaceuticals are 32 percentage points likelier to be filed at the Paris deadline. We also find that as many as 30% of PCT applications may result in information about patentability that makes subsequent applications unprofitable.

Aqueous Light‐Harvesting Systems Constructed from Pillar[n]Arene‐Based Supra‐Amphiphiles

AbstractEfficient light energy utilization is important for a wide range of technological applications, including photoelectric conversion, photocatalysis, sensing, and bioimaging. In recent years, supramolecular light‐harvesting systems (LHSs) have attracted significant attention due to their potential applications in solar energy conversion and photochemical processes. Pillar[n]arene‐based supra‐amphiphiles, with their unique structural properties and versatile macrocyclic host–guest chemistry, offer promising platforms for constructing efficient aqueous LHSs. This Concept highlights the design, self‐assembly, and photophysical behaviour of pillar[n]arene‐based supra‐amphiphiles, focusing on their role in enhancing light absorption and energy transfer. Key strategies for improving the stability and efficiency of these systems are discussed, such as molecular tuning and environmental adaptability. The Concept also explores the challenges and future directions in optimizing these supramolecular systems for real‐world applications.

A Gate-Opening Control Strategy via Nitrate-Chloride Anion Exchange for Enhanced Hydrogen Isotope Separation in Metal-Organic Frameworks.

Efficient separation of hydrogen isotopes, especially deuterium (D2), is pivotal for advancing industries such as nuclear fusion, semiconductor processing, and metabolic imaging. Current technologies, including cryogenic distillation and Girdler sulfide processes, suffer from significant limitations in selectivity and cost-effectiveness. Herein, we introduce a novel approach utilizing an imidazolium-based Metal-Organic Framework (MOF), JCM-1, designed to enhance D2/H2 separation through temperature-dependent gate-opening controlled by ion exchange. By substituting NO3⁻ ions in JCM-1(NO3⁻) with Cl⁻ ions to form JCM-1(Cl⁻), we precisely modulate the gate-opening threshold, achieving a significant enhancement in isotope selectivity. JCM-1(NO3⁻) exhibited a D2/H2 selectivity (SD2/H2) of 14.4 at 30 K and 1 bar, while JCM-1(Cl⁻) achieved an exceptional selectivity of 27.7 at 50 K and 1 mbar. This heightened performance is attributed to the reduced pore aperture and higher gate-opening temperature resulting from the Cl⁻ exchange, which optimizes the selective adsorption of D2. Our findings reveal that JCM-1 frameworks, with their tunable gate-opening properties, offer a highly efficient and adaptable platform for hydrogen isotope separation. This work not only advances the understanding of ion-exchanged MOFs but also opens new pathways for targeted applications in isotope separation and other gas separation processes.

Functional Hydrogel Interfaces for Cartilage and Bone Regeneration.

Effective treatment of bone diseases is quite tricky due to the unique nature of bone tissue and the complexity of the bone repair process. In combination with biological materials, cells and biological factors can provide a highly effective and safe treatment strategy for bone repair and regeneration, especially based on these multifunctional hydrogel interface materials. However, itis still a challenge to formulate hydrogel materials with fascinating properties (e.g., biological activity, controllable biodegradability, mechanical strength, excellent cell/tissue adhesion, and controllable release properties) for their clinical applications in complex bone repair processes. In this review, we will highlight recent advances in developing functional interface hydrogels. We then discuss the barriers to producing of functional hydrogel materials without sacrificing their inherent properties, and potential applications in cartilage and bone repair are discussed. Multifunctional hydrogel interface materials can serve as a fundamental building block for bone tissue engineering.

Enhancing Electrocatalytic Activity Through Targeted Local Electrolyte Micro‐Environment

AbstractThe local electrolyte micro‐environment surrounding the catalyst reaction center, including critical factors such as pH, reactant concentration, and electric field, plays a decisive role in electrocatalytic reactions such as water splitting. Recently, this topic has garnered significant attention due to its potential to significantly enhance catalytic performance. While various strategies to optimize electrocatalytic processes have been explored, deliberate control over the micro‐environment and the fundamental principles guiding these local adjustments remain in their early stages of development. This review provides a comprehensive examination of key efforts aimed at designing and tailoring localized micro‐environments to improve electrocatalytic performance. It discusses advances in micro‐environmental design, methodologies for evaluating micro‐environmental shifts, and the mechanistic insights driving these developments. Additionally, this review highlights existing challenges and prospective industrial applications of localized micro‐environment strategies. By offering a detailed analysis of recent developments, this review aims to equip researchers with practical knowledge on controlling micro‐environments, thereby accelerating progress toward practical real‐world applications in electrocatalytic processes.

Verification of Vulnerable Road User Pre-Crash Scenarios in Vision-Based Safety Applications

<div>Verifying training datasets in vision-based vehicle safety applications is crucial to understanding the potential limitations of detection capabilities that may result in a higher safety risk. Vision-based pedestrian safety applications with crash avoidance technologies rely on prompt detection to avoid a crash. This research aims to develop a verification process for vulnerable road user safety applications with vision-based detection functionalities. It consists of reviewing the application’s safety requirements, identifying the target objects of detection in the operational design domain and pre-crash scenarios, and evaluating the safety risks qualitatively by examining the training dataset based on the results of pre-crash scenarios classification. As a demonstration, the process is implemented using open-source pedestrian tracking software, and the pre-crash scenarios are classified based on the trajectories of pedestrians in an example training dataset used in a pedestrian automatic emergency braking system. The dataset provides abundant vulnerable road users under various operating conditions. Results show that the dataset does not cover snowy weather, fog, smoke, smog, and dust/dirt of particulate matter conditions. Crossing, cut-in, and front pre-crash scenarios are less well-represented in the dataset than pedestrians on the sidewalk. Very few pedestrians were found in the front-near zone, which suggests the dataset lacks sufficient imminent pre-crash scenarios that pose the greatest safety risk. The performance of the pedestrian tracking software is evaluated and future research for improving the proposed mechanism is discussed.</div>

Review of advanced sensor system applications in grinding operations.

Today, in a wide variety of industries, grinding operations are an extremely important finishing process for obtaining precise dimensions and meeting strict requirements for roughness and shape accuracy. However, the constant wear of abrasive tools during grinding negatively affects the dimensional and surface conditions of the workpiece. Therefore, effective monitoring of the wear process during grinding operations helps to predict tool life, plan maintenance and ensure consistent product quality. The objective of this review is to examine current tool condition monitoring techniques, both direct and indirect, in various sensor systems and their application in both traditional and AI-driven grinding processes. By examining these techniques, the review provides insight into how different monitoring techniques can improve process efficiency, reduce downtime, and improve finished product quality, as well as the application of intelligent and adaptive processes to traditional grinding operations. Key Scientific Concepts of Review: The review discusses the critical role of sensor systems in monitoring tool condition, including technologies such as imaging, vibration analysis, acoustic emission, and force measurement. These systems are vital for detecting wear and predicting failures, allowing for timely interventions and preventing unplanned downtimes. The integration of artificial intelligence into these monitoring systems greatly enhances their capabilities, as they enable more proactive strategies and adapt to changing conditions during the grinding process.

Analysis of Porosity-Water/Binder Index for Prediction of Strength and Stiffness of Cemented Sands: A Comparative Study.

Artificial sand cementation improves stability, stiffness, and mechanical strength, making it a critical process in geotechnical applications. This study analyzes the capability of the porosity-water/binding agent index (ηCw/Biv) to predict cemented sands' unconfined compressive strength (qu) and stiffness (Go). Four Colombian sands, i.e., Luruaco, Medellín, Lorica, and Bogotá (stabilized with Portland cement), and were compared with three Brazilian sands: i.e., Osorio, Porto Alegre, and Rio Pardo were evaluated, stabilized with combinations of carbide lime and glass powder, using varying binder contents and a curing period of seven days subjected to ultrasonic pulse velocity (UPV) tests and unconfined compressive strength (UCS) tests. The results indicate that incorporating water content into the index significantly enhances predictive accuracy, achieving R2 values above 0.94 for Colombian sands and considerably better fits for Brazilian sands than the traditional porosity/binder index. This new alternative provides an appropriate parameter for representing the small-strain stiffness and unconfined compressive strength of artificially cemented sands stabilized with various types of binders. Furthermore, the new index proved to be more effective in predicting the behavior of uniform and loose-graded sands, such as those from Bogotá and Lorica, which rely more heavily on binder volume and water content to achieve greater strength and stiffness. Lastly, the predictive model, validated against experimental results, achieved reliability indices (R2) of 0.9791 for stiffness and 0.9799 for strength prediction.

Effective semi-fed-batch saccharification with high lignocellulose loading using co-culture of Clostridium thermocellum and Thermobrachium celere strain A9.

Maximizing saccharification efficiency of lignocellulose and minimizing the production costs associated with enzyme requirements are crucial for sustainable biofuel production. This study presents a novel semi-fed-batch saccharification method that uses a co-culture of Clostridium thermocellum and Thermobrachium celere strain A9 to efficiently break down high solid-loading lignocellulosic biomass without the need for any external enzymes. This method optimizes saccharification efficiency and enhances glucose production from alkaline-treated rice straw, a representative lignocellulosic biomass. Initially, a co-culture of C. thermocellum and T. celere strain A9 was established with a treated rice straw loading of 150 g/l, supplemented with Tween 20, which enhanced enzymes stability and prevented unproductive binding to lignin, achieving a remarkable glucose concentration of up to 90.8 g/l. Subsequently, an additional 100 g/l of treated rice straw was introduced, resulting in a total glucose concentration of up to 140 g/l, representing 70.1% of the theoretical glucose yield from the 250 g/l treated rice straw load. In contrast, batch saccharification using an initial substrate concentration of 250 g/l of alkaline-treated rice straw without Tween 20 resulted in a glucose concentration of 55.5 g/l, with a theoretical glucose yield of only 27.7%. These results suggest that the semi-fed-batch saccharification method using co-cultivation of C. thermocellum and T. celere strain A9, supplemented with Tween 20 is an efficient microbial method for saccharifying high-concentration biomass. Moreover, this approach effectively manages high solids loading, optimizes efficiency, and reduces the need for external enzymes, thus lowering production costs and simplifying the process for industrial applications.