Large-Scale Water Electrolysis for Power-to-Gas
Bibliographic record
Abstract
Rob Harvey, Rami Abouatallah, and Joseph Cargnelli Over a century ago, Alexander T. Stuart began to take an interest in hydrogen energy while studying chemistry and mineralogy at the University of Toronto. At the time, Niagara Falls’ hydroelectric generating capacity was being utilized at only 30%–40%. The question was: How could such surplus capacity is converted to useable energy? The obvious answer was electrolysis of water. In 1948, father and son founded the Electrolyzer Company and it became a leading designer and manufacturer of electrolytic hydrogen and oxygen generation plants for markets around the world. By the 1990s, the company had built several hundred installations in over 80 countries and 5 continents. With its 2004 acquisition of the renamed company, Stuart Energy, Hydrogenics Corporation entered the electrolytic hydrogen generation market and today it has developed a megawatt-scale proton exchange membrane (PEM) electrolyzer stack technology that will be the building block platform for Power-to-Gas, a revolutionary approach to energy conversion and storage using hydrogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".