Processing Route Research Articles

I Feel Like Owning It! Exploring the Antecedents of Psychological Ownership and Its Impact on Brand Loyalty in Digital Content Marketing

Content marketing is an effective approach to brand communication on social media and can inspire people to develop a sense of “ownership” toward the product/brand featured in branded content. Drawing on the theory of psychological ownership and the uses and gratification theory, this study proposed a comprehensive framework to explicate how individual motivations serve as precursors of psychological ownership toward products featured in content marketing, via two processing routes or intermediaries, namely consumers’ flow experience and perceived content value. As such, this study identified six motivations explicating why consumers engage with content marketing: content inspiration, brand likeability, brand conversation, incentive, habitual pass time, and personal identity. Specifically, we found that some antecedents—the motivations of content inspiration, brand likability, brand conversation, and personal identity—positively predicted psychological ownership, via increasing flow and content value, respectively. Furthermore, psychological ownership was positively related to brand loyalty. These findings propose a comprehensive model explaining the process through which content marketing affects psychological ownership and subsequent brand loyalty.

A comprehensive review of Gum metal's potential as a biomedical material

The demand for low elastic modulus and biocompatible load-bearing implants has experienced a significant surge in recent times. Gum metal, a noteworthy category of β-Ti alloys, has gained substantial recognition in the biomedical field. This is primarily attributed to its remarkable blend of characteristics, encompassing non-toxic and biocompatible elements, a low elastic modulus, superelasticity, and high strength. Achieving these properties involves precise electronic criteria within their composition and substantial deformation, often through cold swaging. Recent years have seen innovative processing methods and alloy compositions to meet these requirements, expanding Gum metal's properties and applications. This review offers a thorough look at Gum metal, covering its discovery and highlighting its unique properties, with a particular focus on its relevance in the biomedical field. It critically analyzes the various phases of Gum metal and delves deeply into its deformation mechanism, unveiling its exceptional shape recovery capability even after significant deformation. Theoretical alloy design strategies for Gum metal are addressed, providing a glimpse into the ongoing efforts to optimize its performance for biomedical applications. The impact of oxygen on the Gum metal's properties is explored. The review also scrutinizes biocompatibility and corrosion behavior studies conducted on Gum metal, emphasizing its appropriateness for biomedical implants. Finally, various fabrication and processing routes for Gum metal are reviewed, offering insights into the techniques employed to harness its potential for practical applications.

Improving uranium oxide pathway discernment and generalizability using contrastive self-supervised learning

In the field of Nuclear Forensics, there exists a plethora of different tools to aid investigators when performing analysis of unknown nuclear materials. Many of these tools offer visual representations of the uranium ore concentrate (UOC) materials that include complimentary and contrasting information. In this paper, we present a novel technique drawing from state-of-the-art machine learning methods that allows information from scanning electron microscopy images (SEM) to be combined to create digital encodings of the material that can be used to determine the material’s processing route. Our technique can classify UOC processing routes with greater than 96% accuracy in a fraction of a second and can be adapted to unseen samples at similarly high accuracy. The technique’s high accuracy and speed allow forensic investigators to quickly get preliminary results, while generalization allows the model to be adapted to new materials or processing routes quickly without the need for complete retraining of the model.

A Q-learning-based smart clustering routing method in flying Ad Hoc networks

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly.

Beneficiation Processing of Magnetite ore from Lampung as Dense Media for Dense Medium Separator in Coal Washing Plant

Until recently, coal washing plants in Indonesia are still using imported magnetite from Australia as dense media for dense medium separator units. To be effectively utilized as a dense media, magnetite ore needs to be concentrated to remove gangue minerals so that the final product will have more than 95% magnetic content, 95% weight passing 53 microns, and relative density ranging from 4.9 – 5.2 g/cm3. Experimental studies have been performed at the Department of Metallurgical Engineering ITB to concentrate fine magnetite ores from Lampung Province. Two process routes were chosen: grinding-magnetic separation and magnetic separation-grinding. Products from the two routes were sieve size analyzed, assayed and characterized. Magnetism characteristic was analyzed with VSM and relative density was measured with pycnometer. The first process route products have maximum magnetic content of 99.1% and particle weight passing 53 microns of 95.7%, while the second route have magnetic content of 95.2% and particle passing 53 microns of 97.1%. Concentrates from both routes have the same relative density of 4.5 g/cm3. Characterization by XRF and AAS gives Fe content of 46.6% and 48.8% for the first route product, and 52.1% and 52.9% for the second route. Lampung magnetite ore gives lower magnetism characteristic compare to Australian magnetite ore. Finer particle size gave lower magnetic saturation value, hence lower magnetism.

AN OVERVIEW OF SELF-LUBRICATED ALUMINUM-BASED HYBRID COMPOSITES, PART A: SOLID LUBRICANTS AND PROCESSING ROUTE

Innovation in solid lubricant technology deserves the attention of design engineers for multiple applications, not only in normal environments but also in unfavorable ones. Self-lubricating composites belong to a class of materials that provide a thin layer of lubricant, known as tribofilm, when two surfaces rub against each other. This type of composite has different chemistry, microstructure, and crystal structure on the wear surface as compared to unreinforced solid lubricant metal matrix composites, thanks to the presence of solid lubricants. However, these lubricants, which offer extremely low friction and a long lifetime, do not respond appropriately to different environmental conditions. Therefore, strategies have been developed to construct novel aluminum-based self-lubricating hybrid composites that can adapt to various environmental conditions. This article explores the potential of solid lubricants as essential components in self-lubricating hybrid composites. The present review focuses on Al-based self-lubricating hybrid composites and highlights the types of solid lubricants used as reinforcing phases and several processing methods adopted to fabricate different aluminum-based hybrid composites.

Underwater Wireless Sensor Networks (Review)

Underwater wireless sensor networks (UWSNs) are becoming increasingly popular among researchers due to their potential for real-world applications such as marine surveillance, sea monitoring, deep sea archaeology, oil monitoring, and more. With almost 70% of the earth’s surface covered in water, it is challenging for humans to gather valuable information from the seabed without advanced technology. In UWSNs, sensor nodes are placed to sense the underwater environment, and the data collected is sent to a sink node, which then transfers the data to a base station for processing. The deployment of sensor nodes in UWSNs is difficult due to the harsh underwater environment, and the routing of data is complicated by the nodes’ limited communication range and high energy consumption. This study provides a comprehensive overview of UWSNs, including their applications, deployment methods, and routing algorithms. A comparative analysis of deployment techniques and routing algorithms is presented to help researchers identify research gaps in these areas. The study also reviews some UWSN applications, which offer valuable insights into the approach. The study covers the conventional technologies used in UWSNs and highlights significant research approaches towards UWSNs’ applications, deployment techniques, and routing processes. The insights provided in this study will assist researchers in understanding the present state of UWSNs and identifying future research directions in this exciting field.

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

AbstractRecently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC‐Juniata‐hydroDL2) that mimics the classical Muskingum‐Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach‐scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real‐world data, the trained differentiable routing model produced more accurate long‐term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national‐scale hydrologic flood simulations.

Nanoparticle technology for mRNA: Delivery strategy, clinical application and developmental landscape.

The mRNA vaccine, a groundbreaking advancement in the field of immunology, has garnered international recognition by being awarded the prestigious Nobel Prize, which has emerged as a promising prophylactic and therapeutic modality for various diseases, especially in cancer, rare disease, and infectious disease such as COVID-19, wherein successful mRNA treatment can be achieved by improving the stability of mRNA and introducing a safe and effective delivery system. Nanotechnology-based delivery systems, such as lipid nanoparticles, lipoplexes, polyplexes, lipid-polymer hybrid nanoparticles and others, have attracted great interest and have been explored for mRNA delivery. Nanoscale platforms can protect mRNA from extracellular degradation while promoting endosome escape after endocytosis, hence improving the efficacy. This review provides an overview of diverse nanoplatforms utilized for mRNA delivery in preclinical and clinical stages, including formulation, preparation process, transfection efficiency, and administration route. Furthermore, the market situation and prospects of mRNA vaccines are discussed here.

The reaction mechanism of SO3 with the multifunctional compound ethanolamine and its atmospheric implications.

SO3 is an important reactive species in sulfur cycle and sulfuric acid formation processes and its reactions with some functional group substances, such as H2O, NH3, CH3OH, and organic and inorganic acids, have been extensively studied. However, its loss mechanism with multifunctional species is still lacking in detail. Herein, the reaction mechanism between SO3 and monoethanolamide (MEA) was investigated in the gas phase and on water droplets. The quantum chemical calculations indicate that the gas-phase reactions of SO3 with the OH and NH2 moieties of MEA hardly occur as their reaction energy barriers are up to 21.9-29.4 kcal mol-1. When a single water molecule is added into the SO3 + MEA reaction, it not only decreases the reaction barrier by at least 15.0 kcal mol-1 and thus enhances the rate obviously, but can also lead to the main product changing from HOCH2CH2NHSO3H to NH2CH2CH2OSO3H. The Born Oppenheimer molecular dynamics simulations on a water droplet show that the routes of the NH2CH2CH2OSO3-⋯H3O+ ion pair, HSO4- and HOCH2CH2NH3+ ions and zwitterionic formations of HOCH2CH2NH2+-SO3- and SO3--OCH2CH2NH3+ occur through a loop-structure route or chain reaction process, and can be finished within several picoseconds. Interestingly, the nucleation simulations show that the products of HOCH2CH2NHSO3H and NH2CH2CH2OSO3H have a potential ability to participate in the formation of new particles as they can form larger clusters with H2SO4, NH3 and H2O molecules within 20 ns. Thus, the present study will not only give new insight into the reaction between SO3 and multifunctional compounds, but also provide a new potential formation mechanism for particles resulting from the loss of SO3.

Assessing aspects of solution-based chemical synthesis to convert waste Si solar cells into nanostructured aluminosilicate crystals

An affordable, scalable, and easily adoptable solution-based processing route provides a potential way of converting waste Si solar cells into aluminosilicate minerals to contribute to sustainability in the solar energy value chain.

Controlling Dielectric Film Defects to Increase the Breakdown Voltage of Conductive Polymer Solid Capacitors.

Aluminum solid polymer capacitors are promising devices for the increased demand for power electronics applications. Nonetheless, the low breakdown voltage of commercially available catalysts (∼100 V) limits their applications. In this study, a hydroxide-film-covered high-purity aluminum was anodized at 700 V in boric acid at 85 °C, and the effect of a second hot water immersion (posthydration treatment) after anodizing on the breakdown voltage was studied as a possible future treatment to enhance the withstand voltages of solid electrolytic capacitors. The dielectric breakdown voltage of the anodized aluminum with a PEDOT:PSS coating was ∼500 V, being ∼200 V less than the anodizing voltage; however, the dielectric breakdown voltage was increased above 700 V by introducing the posthydration treatment due to the formation of a nanovoid layer above the dielectric alumina film. Our research suggests that the highly dispersed nanovoids incorporated with PEDOT:PSS avoid the current concentration at some local regions, effectively increasing the dielectric breakdown voltage. The posthydration treatment increased the leakage current by introducing physical defects in the dielectric film. However, the leakage current was reduced by a voltage sweep below the breakdown voltage after the PEDOT:PSS coating or a second anodizing process before the coating, keeping the breakdown voltage above 600 V. A promising processing route to obtain aluminum solid capacitors with high withstand voltage (600 V) found in our research is, first, dipping in hot water; second, anodizing at 700 V; then a second hot water treatment; and a second anodizing at 400 V, which keeps the capacitance invariable with a breakdown voltage enhanced.

Green ethylene production in the UK by 2035: a techno-economic assessment

A techno-economic analysis comparing thermocatalytic and electrocatalytic routes to green ethylene from air-captured CO2 and off-shore wind electricity.

Analysis of synthetic route and case correlation of methamphetamine crystals seized by ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) and Pearson correlation coefficient method.

Analysis of impurities in illicit drugs provides key information on the manufacture and distribution of methamphetamine (MA). An analysis of 22 specific target compounds in unlawful MA products was conducted using ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). The peak area of the alkaloids was utilized as an important calculation data variable to determine the synthesis method. The results demonstrated that the application of target screening techniques for trace impurity components in samples can aid in determining the synthesis process routes for toxin production in 169 samples. These samples were initially categorized based on their synthesis modes, specifically synthesis routes N, E, and R. The correlation analysis of MA samples with known synthetic routes was carried out using principal coordinate analysis (PCoA) and the Pearson correlation coefficient method. Among the N and R synthesis routes, 15 and 7 groups with strong correlation were screened, respectively. Consistent with the observed scenario, the samples from the synthetic route E showed limited correlation with each other. The experiment provides a focused framework for the management of synthetic raw materials used for the production of drugs and the investigation of drug-related cases. This facilitates a holistic strategy to combat drug-related crimes, from manufacturing laboratory to end-user.

A data management framework for life cycle simulation in extruded Aluminium products production

Sustainability analysis in complex systems demands dynamic rather than static methodologies. Technological advances allow industry to model the life cycle of their products and services towards making better decisions regarding their environmental, economic, and social dimensions. Life Cycle Simulation (LCS) is a time-dependent simulation tool used for testing and estimation of scenarios, capable of addressing sustainability loads. Aluminium (Al) is present in most modern applications and technologies, being a strategic material for the green future. Due to this global outspread, the Al supply chain has a multiple net of nodes and flows and when second life Al is considered the system gains complexity. The LCS is an appropriate tool when considering the analysis of loads from the life cycle of Al products. A crucial step when performing a LCS is input data management. Appropriate care should be taken because poor data quality yields voided simulation results. In this sense, the goal of this paper is to present a data management framework for LCS of an Al product production line. The process route includes casting, extrusion, forming, and welding. The structured methodology will cover relevant processes with accuracy and variation implications. The framework will greatly support practitioners in providing time efficiency and precise input data management in the Al supply chain.

Additive Manufacturing Process Root Selection Using Bayesian Network

Additive manufacturing emerged as a highly promising technology within the realm of manufacturing, offering advantages such as rapid customization, on-demand production, the ability to create intricate shapes, and the absence of economies of scale dependency. Its widespread adoption is evident across various industries, including aviation, oil and gas, and the automotive sector. But finding the most favorable process root to 3D print a part that makes sense both technically and economically is a challenging and tricky task because there are many different methods, each with its own unique constraints. This article focuses on the complex decision-making process of choosing which process root to be selected for 3D printing something, considering various attributes. It introduces a novel framework combining the Delphi methodology, the Neutrosophic Best-Worst Method, and Bayesian Network analysis to discern the most viable additive manufacturing process root for a given part. To validate the efficacy of this framework, a practical case study involving Ferro Oil Tech India Pvt. Ltd. is employed. The study evaluates three discrete additive manufacturing process routes: direct fabrication via direct metal laser sintering, selective laser sintering combined with investment casting, and stereolithography coupled with investment casting. The findings indicate that the direct metal laser sintering process proves to be both economically viable and technically feasible.

Coupling the high-temperature Fischer–Tropsch synthesis and the skeletal isomerization reaction at optimal operation conditions in the Power-to-Fuels process route for the production of sustainable aviation gasoline

Power-to-Fuels route for the production of aviation gasoline: stable operation of the coupled high-temperature Fischer–Tropsch synthesis and the skeletal isomerization reaction, at optimal operating conditions respectively.

Preparation and characterization of starch-based binders for binder jetting.

In recent years, binder jetting technology has made significant advances across industries, expanding the range of material options to meet diverse needs. Commonly used binders may leave residues during the sintering process, affecting surface quality and performance, and some may contain harmful substances. Therefore, there is a high demand for binders that are environmentally friendly and easy to remove. This study proposes to use sodium alginate and polyvinylpyrrolidone as additives to prepare starch-based inks that are both environmentally friendly and safe. The effects of additive composition, starch content, dispersant content, and dispersant ratio on the viscosity and stability of starch-based inks were studied. Through performance testing, the particle size, surface tension, rheological properties, and printability of inks with different components were demonstrated. The optimal ink formulation consists of 1 wt% starch and 0.3 wt% additives (30 wt% sodium alginate and 70 wt% PVP). The viscosity reaches 23 mPa s and the stability is excellent. The surface tension of the ink is 69.5 mN m-1, which is slightly higher than the surface tension requirements for the printhead. This article provides a new process route for binder jetting technology and lays the foundation for its application in green and environmental protection.

Size and temperature dependent shapes of copper nanocrystals using parallel tempering molecular dynamics.

We performed parallel-tempering molecular dynamics simulations to predict the temperature- and size-dependent equilibrium shapes of a series of Cu nanocrystals in the 100- to 200-atom size range. Our study indicates that temperature-dependent, solid-solid shape transitions occur frequently for Cu nanocrystals in this size range. Complementary calculations with electronic density functional theory indicate that vibrational entropy favors nanocrystals with a shape intermediate between a decahedron and an icosahedron. Overall, we find that entropy plays a significant role in determining the shapes Cu nanocrystals, so studies aimed at determining minimum-energy shapes may fail to correctly predict shapes observed at experimental temperatures. We also observe significant shape changes with nanocrystal size - sometimes with changes in a single atom. The information from this study could be useful in efforts to devise processing routes to achieve selective nanocrystal shapes.

Gallium oxide cantilevered thin film-based solar-blind photodetector and its arc detection applications

The performance of gallium oxide (Ga<sub>2</sub>O<sub>3</sub>) thin film detector based on metal-semiconductor-metal (MSM) is highly dependent on the uniformity of the Ga<sub>2</sub>O<sub>3</sub> thin film, and the manufacturing process is quite sophisticated, which poses a challenge for the scale-up and mass production of thin film photodetectors. In this work, an MSM Ga<sub>2</sub>O<sub>3</sub> thin film solar-blind photodetector with five-finger interdigital electrodes is fabricated by physically depositing Ga<sub>2</sub>O<sub>3</sub> thin film on the surface of a mass-produced cantilevered thin film chip. Through the microelectromechanical system (MEMS) process, the cantilever electrode structure is prepared, which protects the internal circuit and the integrity of the thin film. The Ga<sub>2</sub>O<sub>3</sub> thin film treated by argon plasma at a low temperature is amorphous, but the photodetector still possesses considerable ultraviolet detection performance. At a bias voltage of 18 V, it approaches the detection performance of crystalline Ga<sub>2</sub>O<sub>3</sub> thin film, with a detectivity of 7.9×10<sup>10</sup> Jones, an external quantum efficiency of 1779%, rise time and decay time of 1.22 s and 0.24 s, respectively. Moreover, a system of arc detection is built in outdoor environments. Without any optical focusing system, this photodetector achieves sensitive detection of pulsed arc in an outdoor sunlight environments. For pulsed arcs with an output voltage of 100 kV, the photodetector is capable of sensitive detection at a distance of 25 cm. Besides, the maximum detection distance of 155 cm indicates that the photodetector will have a favorable potential application value in the field of solar-blind detection. This work develops a sensitive functional thin film deposition technology based on the cantilever electrode structure fabricated by the MEMS process, which avoids the influence of the large-area uniformity of the functional thin film on the etching circuit. It provides a new technical approach and process route for preparing MSM photodetectors.