Anticipatory LCA seeks to overcome the paucity of data through scenario development and thermodynamic bounding analyses. Critical components of anticipatory LCA include:
1) Laboratory-scale inventory data collection for nano-manufacturing processes and
preliminary performance evaluation.
2) Thermodynamic modeling of manufacturing processes and developing scenarios of
efficiency gains informed by analogous material processing industries.
3) Use-phase bounding to report inventory data in a functional unit descriptive of
performance.
Together, these analyses may call attention to environmentally problematic processes or nanotechnologies before significant investments in R&D and infrastructure contribute to technology lock in. The following case study applies these components of anticipatory LCA to single wall carbon nanotube (SWCNT) manufacturing processes, compares the rapid improvements in SWCNT manufacturing to historic reductions in the embodied energy of aluminum, and discusses the use of SWCNTs as free-standing anodes in advanced lithium ion batteries.
The proposed hypotheses was based on the four different Kolbe A™ strengths, or Action Modes: Fact Finder, Follow Through, Quick Start, and Implementor. Hypotheses were made about class participation and official class twitter use, using #ASUsp, for each Kolbe type. The results proved these hypotheses incorrect, indicating a lack of correlation between Kolbe A™ types and playing. The report also includes qualitative results such as Twitter Keywords and a Sentiment calculation for each week of the course. The class had many positive outcomes, including growth in the ability to collaborate by students, further understanding of how to integrate Twitter use into the classroom, and more knowledge about the effectiveness of LSP.