Eco-Audit of MOFs as H₂ Storage Materials for Vehicle Applications, Using Novel Refueling Model
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are a heavily researched candidate for fuel-cell electric vehicle (FCEV) hydrogen storage. However, little analysis has been done on the environmental impact of potential MOF vehicles compared to established alternative vehicles, such as compressed hydrogen or battery-electric vehicles. In this work, a preliminary eco-audit was conducted for a FCEV using an MOF hydrogen storage system based the best current MOF Ni₂(m-dobdc) (Ni-MOF-74). (1) Cost and environmental impact analyses were performed for both the production and use phases of an MOF-FCEV. The cost and environmental impact of MOF production was compared to that of Lithium Nickel Manganese Cobalt Oxide (LiNMCO) batteries, the current state-of-the-art for BEVs. (2) Environmental impact was assessed using embodied energy estimates based on reported values for LiNMCO BEVs. These highlighted MOF vehicles as a competitor to current renewable energy vehicle technologies. For the use phase, a hydrogen refueling station that produces hydrogen onsite by proton exchange membrane (PEM) electrolysis from grid electricity was compared to an equivalent population of battery electric vehicles (BEVs) charged at distributed recharging stations. FCEVs using the proposed refueling model were able to compete with BEVs both in terms of electricity CO₂ footprint and cost in the simulated solar-dominated Californian grid, but not in the hydro- and nuclear-heavy Ontario grid.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".