Natural isotope fingerprinting of produced hydrogen and its potential applications to the hydrogen economy
Bibliographic record
Abstract
Stable isotopes of hydrogen (2H/1H) carry natural fingerprints of produced hydrogen by mode of origin which are difficult or costly to adulterate. A newly compiled database of 5677 measurements reveals that green hydrogen (electrolytic or biological hydrogen, e.g., nitrogenase, hydrogenase) is readily distinguished by its considerable depletion in heavy isotopic species, ranging from −831 to −555 ‰ in δ2H relative to Vienna Standard Mean Ocean Water (V-SMOW), as compared to −377 to +196 ‰ for fossil fuel sources (grey/turquoise hydrogen), and −379 to 0 ‰ for wood/biomass burning (brown hydrogen), compared to analytical uncertainty of close to ±1 ‰. White hydrogen, naturally produced in a variety of geologic settings, ranges from −996 to −49 ‰, reflecting diverse overlapping origins. Potential applications of fingerprinting include tracking of produced hydrogen by source, process and distribution control, grading and regulation of low carbon intensity (CI) products, and leakage detection for carbon storage operations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".