Integrating Sustainable Design into Advanced Electrochemical Manufacturing
Bibliographic record
Abstract
Considering the green engineering principles developed by Anastas and Zimmerman or detailed from the Sandestin conference of 2004 as presented by Abraham and Nguyen there is a duty of care as environmental stewards of our technology to closely examine where we can reduce impact regarding nascent electrochemical technologies. Comparison of state-of-the-art electrochemical manufacturing technologies highlight the design options competing technologies offer when we consider the sourcing, manufacturing, emissions, and other ways of embodying the environmental footprint of our designed processes. Impact on natural systems, circular life-cycle thinking and creating engineering solutions beyond our current or dominant manufacturing technologies to improve, innovate and invent solutions for a transition to a clean energy future are highlighted. Principles of conservation and improvement of natural ecosystems are considered to inform novice scientists and engineers and lead to insight in opportunities for advanced system developments for mature industry representatives and experienced experts. Challenges in greening of existing systems offers opportunities for continued growth in the industrial domain. Triple bottom line examination of how our actions can improve manufacturing processes, as well as benefit globally relevant sustainable development goals. Reframing the basis for how we design electrochemical processes from a cost-efficiency relationship to include additional considerations in our practice using simple guidelines to address cell architectures, integration and interconnection of energy and material flows, and careful material selection are shown to have a win-win benefit when we examine intergenerational scope of our practice.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".