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The transferred carbon nanofibers (CNFs) can be applied in flip chip package as interconnect material, as an alternative to the conventional solder and conductive adhesive (CA) materials.

The structure of CNFs was confirmed by transmission electron microscopy (TEM). The electrical performance of the vertically aligned carbon nanofibers (VACNFs) joint was measured by four points probe method and compared to conventional lead‐free solder Sn3.0Ag0.5Cu, pure indium and silver CA. A shear test was carried out in order to evaluate the mechanical performance of VACNFs joint. After the shear test, the fracture surface was analyzed by scanning electron microscopy and energy dispersive spectroscopy (SEM‐EDS).

The results showed a high success rate in the transfer of VACNFs from growth chip to target chip. The Au‐coated CNF can be wetted well with melted indium during the transfer and bonding process. In‐Au intermetallic compound (IMC) formed on the surface of CNF. The electrical and mechanical performance of VACNFs is comparable to that of the traditional interconnect materials. The fracture surface is located at the interface between VACNFs and chips. The stacked‐cone structure of CNF can be confirmed from a cross‐section of the break CNF by TEM.

Ultra‐short VACNFs were grown and first successfully transferred to the target chip using a process which required little pressure, low temperature and short time.

Studies have shown that actors who affiliate with multiple categories generally do so at their own peril. Still, category spanning is routinely observed, although it is less understood. We address this gap by a longitudinal study of category spanning among nanotube technology inventors. Our results highlight the importance of the evolving structure of category relationships, actor embeddedness within the structure, and interactions with other factors, including the attractiveness of related categories. When a category is relationally similar to others, associated inventors are more likely to engage in category spanning, whereas when a category is dissimilar, inventors are more likely to remain within it.

Solutions to the first three moments of the Boltzmann transport equation and Poisson's equation are obtained for a permeable base transistor (PBT) using linearized, block implicit (LBI) and ADI techniques. Two level electron transfer is considered. The results of the simulations are compared to results obtained from the drift and diffusion equations. The comparison indicates that nonequilibrium transport and velocity overshoot are important in the PBT. The predicted I‐V characteristics of the device show substantially higher current levels and a higher cutoff frequency are obtained with the moment equations.

In this chapter, we examine the development of a technology path in the nanotube (NT) field – one of the most well-developed areas of nanotechnology. Although early developments suggested that there were equally viable pathways related to the development of carbon nanotubes (CNTs) and others made with organic molecules and polymers, carbon-based technologies became valorized. We show how the carbon science path developed and try to unpack how it happened. We argue that it was not due to the inherent efficiency or applications of CNTs, but to sociopolitical dynamics. Even though much intellectual property research focuses on patent-level analysis, we underscore the importance of patent categories as key cognitive elements that organize the different knowledge domains within the world of NT patenting. We show that interlinkages between patent categories are crucial to the formation and development of a particular technology path. In unpacking the selection of the carbon science path, we highlight the key role played by a cadre of star scientists and the political neglect of alternative pathways as the field herded toward the CNT path.

This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.

The aim of this paper is to synthesize graphene-modified titanium dioxide (GR-TiO₂) nanorod arrays nanocomposite films, so that these can enhance the photocatalytic properties of titanium dioxide and overcome the problem of difficult separation and recovery of photocatalysts.

The GR-TiO₂ nanocomposite films were synthesized via hydrothermal method and spin-coating. The obtained samples were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), ultraviolet–visible (UV-Vis) diffuse reflectance spectrum and Raman spectrum. The photocatalytic performance of the GR-TiO₂ nanocomposite films for degrading Rhodamin B under ultraviolet (UV) was studied by a UV-Vis spectrophotometer. The photocatalytic enhancement mechanism of graphene was studied by photoelectrochemical analysis.

The introduction of graphene expanded the range of the optical response of TiO₂ nanorod arrays, improving the separation efficiency of the photogenerated electron-hole pairs, and thus dramatically increasing its photocatalytic performance.

A simple and novel way for synthesizing GR-TiO₂ nanocomposite films has enhanced the photocatalytic performance of TiO₂.

The photocatalyst synthesized is easy to separate and recycle in the process of photocatalytic reaction, so it is possible to achieve industrialization.

The present research investigates tailoring the physical properties of stereolithography (SL) epoxy‐based resins by dispersing controlled small amounts of multi‐walled carbon nanotubes (MWCNTs) directly in SL resins prior to layered manufacturing.

A modified 3D Systems 250/50 SL multi‐material machine was used where the machine was equipped with a solid‐state (355 nm) laser, unique ∼ 500 ml vat, overfill drain vat design that continuously flowed resin into the vat via a peristaltic pump, and 8.89 by 8.89 cm² platform. The vat did not include a recoating system. Pumping the composite resin assisted in maintaining the MWCNTs dispersed over long periods of time (with MWCNT settling times on the order of one week). The research approach required developing a method for dispersing the MWCNTs in SL resin, determining new SL build parameters for the modified resin and SL machine, and building and testing tensile specimens.

Mechanical mixing and ultrasonic dispersion provided simple means for dispersing MWCNTs in the SL resin. However, MWCNT agglomerates were observed in all the parts fabricated using the filled resins. Each concentration of MWCNTs resulted in a “new” resin requiring modifications to the SL build parameters, E_C and D_P. Once characterized, the modified resins performed similar to traditional resins in the SL process. Small dispersions of MWCNTs resulted in improvements in the tensile strength (TS) (or ultimate tensile stress) and fracture stress (FS) of tensile specimens as 0.025 percent (w/v) MWCNTs in DSM Somos^® WaterShed™ 11120 resin resulted in increases in TS and FS of 5.7 percent and 26 percent, respectively, when compared to unfilled resin. Increasing the concentration of MWCNTs to 0.10 percent (w/v) resulted in increases in TS and FS of 7.5 percent and 33 percent, respectively, over the unfilled resin. Transmission and scanning electron microscopy showed strong affinity between the epoxy resin and the MWCNTs.

Additional MWCNT type and concentrations in various SL resins should be investigated along with additional means for dispersion to provide sufficient information on developing new SL resins for unique functional applications.

It is anticipated that the methods described here will provide a basis for further development of advanced nanocomposite SL resins for end‐use applications.

This research successfully illustrated the dispersion and use of MWCNTs as a reinforcement material in a commercially available SL resin.

An empirical velocity‐field relationship, based on Monte Carlo simulation, is used to modify a drift‐diffusion model for the characterization of short gate GaAs MESFET's. The modified drift‐diffusion model is used to generate both the steady‐state and the small‐signal parameters of submicron GaAs MESFET's. The current, transconductance, and cutoff frequency are compared with two‐dimensional Monte Carlo simulation results on a 0.2 µm gate‐length. The model is also used to predict measured I‐V and s‐parameters of a 0.5 µm gate‐length ion‐implanted GaAs MESFET. The comparison and the analysis made, support the accuracy of the modified drift‐diffusion simulator and makes it computationally efficient for analysis of short‐gate devices.

This paper aims to discuss that a conventional Al₂O₃, 1.5 Wt.% carbon nanotubes (CNTs)-Al₂O₃, 2 Wt.% CNTs-Al₂O₃ and 4 Wt.% CNTs-Al₂O₃ composite coatings were deposited with the help of Plasma spray process.

To better understand the effect of CNT reinforcement on oxidation resistance, high-temperature oxidation behaviour of conventional Al₂O₃, 1.5 Wt.% CNTs-Al₂O₃, 2 Wt.% CNTs-Al₂O₃ and 4 Wt.% CNTs-Al₂O₃ composite coatings at 900°C was compared with the performance of the uncoated ASME-SA213-T11 boiler tube steel substrate.

The results showed that the CNT-reinforced alumina coatings exhibited better oxidation resistance and thermal stability than uncoated ASME-SA213-T11 boiler tube steel. The coated steel substrates had a lower mass gain rate than the substrate after different oxidation times.

Limited literature is available where the CNT have been reinforced into the composite alloy powders and has been thermally spray-deposited for various surface engineering applications. This research showed that with the increase in the percentage of CNTs into the alloy powder mixture, there is a significant reduction in weight gain and hence higher resistance to oxidation.

The purpose of this paper is to provide a contemporary look at the current state‐of‐the‐art in wireless sensor networks (WSNs) for structure health monitoring (SHM) applications and discuss the still‐open research issues in this field and, hence, to make the decision‐making process more effective and direct.

This paper presents a comprehensive review of WSNs for SHM. It also introduces research challenges, opportunities, existing and potential applications. Network architecture and the state‐of‐the‐art wireless sensor communication technologies and standards are explained. Hardware and software of the existing systems are also clarified.

Existing applications and systems are presented along with their advantages and disadvantages. A comparison landscape and open research issues are also presented.

The paper presents a comprehensive and recent review of WSN systems for SHM applications along with open research issues.