Proposal of a framework for scale-up life cycle inventory: A case of nanofibers for lithium iron phosphate cathode applications
Bibliographic record
Abstract
Abstract Environmental assessments are crucial for the management of the environmental impacts of a product in a rapidly developing world. The design phase creates opportunities for acting on the environmental issues of products using life cycle assessment (LCA). However, the LCA is hampered by a lack of information originating from distinct scales along the product or technology value chain. Many studies have been undertaken to handle similar problems, but these studies are case-specific and do not analyze the development options in the initial design phase. Thus, systematic studies are needed to determine the possible scaling. Knowledge from such screening studies would open the door for developing new methods that can tackle a given scaling problem. The present article proposes a scale-up procedure that aims to generate a new life cycle inventory (LCI) on a theoretical industrial scale, based on information from laboratory experiments. Three techniques are described to obtain the new LCI. Investigation of a laboratory-scale procedure is discussed to find similar industrial processes as a benchmark for describing a theoretical large-scale production process. Furthermore, LCA was performed on a model system of nanofiber electrospinning for Li-ion battery cathode applications. The LCA results support material developers in identifying promising development pathways. For example, the present study pointed out the significant impacts of dimethylformamide on suspension preparation and the power requirements of distinct electrospinning subprocesses. Nanofiber-containing battery cells had greater environmental impacts than did the reference cell, although they had better electrochemical performance, such as better wettability of the electrode, improving the electrode's electrosorption capacity, and longer expected lifetime. Furthermore, material and energy recovery throughout the production chain could decrease the environmental impacts by 40% to 70%, making the nanofiber a promising battery cathode. Integr Environ Assess Manag 2016;12:465–477. © 2016 SETAC Key Points Scale-up procedure in life cycle assessment (LCA) is extremely important in the rapid developing technological environment in order to manage the growing environmental problems through environmentally conscious design of new materials. Recent publications and studies offer some promising techniques to handle the problem of different scales in LCA, even though they are not always feasible in low technology readiness levels (TRL 1–3). We propose a general process-scaling framework in order to gain information on theoretical large-scale production in the early development phase. We describe an example of the application of the proposed framework to scale-up the inventory of nanofiber production for a lithium iron phosphate battery cathode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".