Characterization of polyaniline-detonation nanodiamond nanocomposite fibers by atomic force microscopy based technique

Polyaniline (PANI) fibers were synthesized in presence of detonantion nanodiamond (DND) particles by precipitation polymerization technique. Morphological, electrical and mechanical characterizations of the obtained PANI/DND nanocomposited have been performed by different either standard or advanced atomic force microscopy (AFM) based techniques. Morphological characterization by tapping mode AFM supplied information about the structure of fibers and ribbons forming the PANI/DND network. An AFM based technique that takes advantage of an experimental configuration specifically devised for the purpose was used to assess the electrical properties of the fibers, in particular to verify their conductivity. Finally, mechanical characterization was carried out synergically using two different and recently proposed AFM based techniques, one based on AFM tapping mode and the other requiring AFM contact mode, which probed the nanocomposited nature of PANI/DND fiber sample down to different depths. © 2013 Elsevier Ltd. All rights reserved

Recursive Decomposition of Progress Graphs

Search of a state transition system is traditionally how deadlock detection for concurrent programs has been accomplished. This paper examines an approach to deadlock detection that uses geometric semantics involving the topo-logical notion of dihomotopy to partition the state space into components; after that the reduced state space is exhaustively searched. Prior work partitioned the state space inductively. in this paper we show that a recursive technique provides greater reduction of the size of the state transition system and therefore more efficient deadlock detection. If the preprocessing can be done efficiently, then for large problems we expect to see more efficient deadlock detection and eventually more efficient verification of some temporal properties. © 2009 IEEE

A cross-platform modular framework for building Life Cycle Assessment

In recent years the application of Life Cycle Assessment (LCA) for assessing and improving the environmental performance of buildings has increased. At the same time, the automated optimization of building designs is gaining attraction for both design and research purposes. In this regard, a number of issues persist when aiming to optimize building’s environmental impacts along the design process. Firstly, as LCA applies a life cycle perspective, many aspects have to be considered (e.g. energy demand in operation as well as consumption of resources and energy for production and end of life treatment) and a variety of specific calculations is needed (e.g. building energy performance simulation, material quantity take-off). Secondly, sophisticated software packages are available and being used for each of these calculations (e.g. software for building modelling, dynamic energy simulation, quantity surveying). Though many of these software packages are currently standalone applications that rely on human interaction, there is an increasing trend to provide an application programming interface (API) that enables customization and automation. Thirdly, the mentioned processes and calculations are influencing each other in various ways and several scenarios have to be assessed. Thus, a comprehensive and modular approach is required that promotes interconnectivity of the different software solutions and automation of the assessment. In this paper we propose a modular cross-platform framework for LCA of buildings aiming to support flexibility and scalability of building LCA. We present a conceptual framework, example data exchange requirements and highlight potential implementation strategies

Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation

Buildings are major sources of greenhouse gas (GHG) emissions and contributors to the climate crisis. To meet climate-change mitigation needs, one must go beyond operational energy consumption and related GHG emissions of buildings and address their full life cycle. This study investigates the global trends of GHG emissions arising across the life cycle of buildings by systematically compiling and analysing more than 650 life cycle assessment (LCA) case studies. The results, presented for different energy performance classes based on a final sample of 238 cases, show a clear reduction trend in life cycle GHG emissions due to improved operational energy performance. However, the analysis reveals an increase in relative and absolute contributions of so‐called ‘embodied’ GHG emissions, i.e., emissions arising from manufacturing and processing of building materials. While the average share of embodied GHG emissions from buildings following current energy performance regulations is approximately 20–25% of life cycle GHG emissions, this figure escalates to 45–50% for highly energy-efficient buildings and surpasses 90% in extreme cases. Furthermore, this study analyses GHG emissions at time of occurrence, highlighting the ‘carbon spike’ from building production. Relating the results to existing benchmarks for buildings’ GHG emissions in the Swiss SIA energy efficiency path shows that most cases exceed the target of 11.0 kgCO$^{2}$eq/m$^{2}$a. Considering global GHG reduction targets, these results emphasize the urgent need to reduce GHG emissions of buildings by optimizing both operational and embodied impacts. The analysis further confirmed a need for improving transparency and comparability of LCA studies

Embodied GHG emissions of buildings - Critical reflection of benchmark comparison and in-depth analysis of drivers

In the face of the unfolding climate crisis, the role and importance of reducing Greenhouse gas (GHG) emissions from the building sector is increasing. This study investigates the global trends of GHG emissions occurring across the life cycle of buildings by systematically compiling life cycle assessment (LCA) studies and analysing more than 650 building cases. Based on the data extracted from these LCA studies, the influence of features related to LCA methodology and building design is analysed. Results show that embodied GHG emissions, which mainly arise from manufacturing and processing of building materials, are dominating life cycle emissions of new, advanced buildings. Analysis of GHG emissions at the time of occurrence, shows the upfront \u27carbon spike\u27 and emphasises the need to address and reduce the GHG \u27investment\u27 for new buildings. Comparing the results with existing life cycle-related benchmarks, we find only a small number of cases meeting the benchmark. Critically reflecting on the benchmark comparison, an in-depth analysis reveals different reasons for cases achieving the benchmark. While one would expect that different building design strategies and material choices lead to high or low embodied GHG emissions, the results mainly correlate with decisions related to LCA methodology, i.e. the scope of the assessments. The results emphasize the strong need for transparency in the reporting of LCA studies as well as need for consistency when applying environmental benchmarks. Furthermore, the paper opens up the discussion on the potential of utilizing big data and machine learning for analysis and prediction of environmental performance of buildings

A hierarchical reference-based know-why model for design support of sustainable building envelopes

In current complex building designs, sustainability assessments are often performed after project completion, with limited impact on building performance which results in missed goals in terms of quality, cost, and time. We address this problem by proposing a hierarchical reference-based know-why model to answer the research question “what is a suitable decision support model to successfully integrate the sustainability requirements in the early design phase of buildings?”. The model presents a process that incorporates a life-cycle perspective and calculates design alternatives based on a defined reference and the DGNB building certification system. The results show that criteria synergies and trade-offs can be identified, leading to improved design by engineers and better building performance. Our findings pave the way for full integration of the model into building information modeling, combined with artificial intelligence. This can help manage the complexity of the sustainable design process on the path to carbon-neutral buildings