Realistic adsorption geometries and binding affinities of metal nanoparticles onto the surface of carbon nanotubes

Adsorption geometries and binding affinities of metal nanoparticles onto carbon nanotubes (CNTs) are investigated through density-functional-theory calculations. Clusters of 13 metal atoms are used as models for metal nanoparticles. Palladium, platinum, and titanium particles strongly chemisorb to the CNT surface. Unlike the cases of atomic adsorptions the aluminum particle has the weakest binding affinity with the CNT. Aluminum or gold nanoparticles accumulated on the CNT develop the triangular bonding network of the metal surfaces in which the metal-carbon bond is not favored. This suggests that the CNT-Al interface is likely to have many voids and thus susceptible to oxidation damages.open10

Inaccuracy of Density Functional Theory Calculations for Dihydrogen Binding Energetics onto Ca Cation Centers

We investigate the mechanism of dihydrogen adsorption onto Ca cation centers, which has been the significant focus of recent research for hydrogen storage. We particularly concentrate on reliability of commonly used density-functional theories, in comparison with correlated wave function theories. It is shown that, irrespective of the chosen exchange-correlation potentials, density-functional theories result in unphysical binding of H2 molecules onto Ca1+ system. This suggests that several previous publications could contain a serious overestimation of storage capacity at least in part of their results.open262

Improvements in structural and optical properties of wafer-scale hexagonal boron nitride film by post-growth annealing

Remarkable improvements in both structural and optical properties of wafer-scale hexagonal boron nitride (h-BN) films grown by metal-organic chemical vapor deposition (MOCVD) enabled by high-temperature post-growth annealing is presented. The enhanced crystallinity and homogeneity of the MOCVD-grown h-BN films grown at 1050 degrees C is attributed to the solid-state atomic rearrangement during the thermal annealing at 1600 degrees C. In addition, the appearance of the photoluminescence by excitonic transitions as well as enlarged optical band gap were observed for the post-annealed h-BN films as direct consequences of the microstructural improvement. The post-growth annealing is a very promising strategy to overcome limited crystallinity of h-BN films grown by typical MOCVD systems while maintaining their advantage of multiple wafer scalability for practical applications towards two-dimensional electronics and optoelectronics.11Ysciescopu

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT)

© 2022Detecting contractual risk information from construction specifications is crucial to succeeding in construction projects. This paper describes clause classification using the Bidirectional Encoder Representations from Transformers (BERT) method in natural language processing. Seven risk categories are determined from a literature review, including payment, temporal, procedure, safety, role and responsibility, definition, and reference. Using 2807 clauses from 56 construction specifications, the BERT-based clause classification model returns noticeable performances with 0.889 accuracy for validation and a 0.934 F1 score on testing. The model is evaluated by comparing the clause classification performance with other machine learning methods, including the support vector machine and a simple deep neural network, and shows dominant performance on every risk category. Practitioners in the construction industry are the primary beneficiaries of the research as the model will contribute to improving the construction specification review process and risk management during construction projects.N

Development of a safety inspection framework on construction sites using mobile computing

Site safety inspection is an essential task to ensure that construction operations are carried out in a safe manner, in accordance with relevant health and safety policies and standards of a particular jurisdiction. It is also critical to the smooth execution, monitoring, and controlling of construction activities. The evidence gathered from construction experts as well as from previous studies suggests that the efficiency and effectiveness of current inspection processes are less than satisfactory. This paper reports an Australian research project that develops an innovative safety inspection approach to incorporate mobile computing technologies into safety inspection processes in order to facilitate more effective data collection, processing, and control practices. The paper also discusses the interview results of safety practitioners about the proposed inspection approach. The approach was implemented through the development and test of a prototype mobile inspection tool. The feasibility and usefulness of the tool was evaluated and recognized by industry practitioners. The results show that the developed approach and tool have the potential to improve safety inspection performance on construction sites as well as enhance the integration of safety management systems. The research efforts will also enrich the current knowledge on construction safety

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins

Wafer-Scale and Selective-Area Growth of High-Quality Hexagonal Boron Nitride on Ni(111) by Metal-Organic Chemical Vapor Deposition

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Remanufacturing Decision-Making for Gas Insulated Switchgear with Remaining Useful Life Prediction

Remanufacturing has emerged as a way to solve production problems, as raw material costs increase and environmental pollution caused by discarded equipment occurs. The process can extend product lifetime and prevent waste of resources. In particular, it has economical efficiency for large equipment such as GIS (Gas Insulated Switchgear). The crucial points in remanufacturing are determining replaceable parts and economic valuation. To address these issues, we propose a framework for remanufacturing GIS with remaining lifetime prediction. We construct a regression model for remaining useful life (RUL) in the proposed framework using GIS sensor data. The cost of the replacement parts is estimated with the selected sensors. To validate the effectiveness of the proposed framework, we conducted accelerated life testing on a GIS for data acquisition and applied our framework. The experimental results demonstrate that the tree-based RUL regression model outperforms the others in prediction accuracy. In the simulation of part replacement, the important sensor-based decision-making improves RUL significantly