Renewable Liquid Energy: Modular Off-Grid Carbon-Negative Hydrogen, Power, & Pure Water from Ethanol
Bibliographic record
Abstract
Golu-H2 is a member of the SBI Group, a clean energy innovator in cutting-edge technology development for over 25 years. SBI has invested over $40 million to reach the point we are at today, with a fully equipped state-of-the-art 25,000 sq. ft. Technology Development, Product Development, and Scale-up Facility equipped with modern analytical instruments and pilot fabrication capabilities. SBI's renewable diesel and sustainable aviation fuel (Jet Fuel) technologies were licensed by Royal Dutch Shell in 2017. We hold a vast portfolio of global patents for multiple green technologies, including our Gölu-H2, a green hydrogen technology that demonstrates our capabilities in catalyst development, process and processor design, and optimization. Additionally, we have in-house automation and control design capabilities, allowing us to execute projects with maximum flexibility and efficiency. Hydrogen is the clean fuel of the future, providing a de-fossilized alternative energy resource. Energy derived from hydrogen does not emit CO2 and can be extracted from renewable sources. When using a fuel cell for power generation, hydrogen is the most efficient renewable fuel, generating up to 21 kWh/kg out of the 33-kWh available in the hydrogen. The only byproduct of power generation through a fuel cell is pure H2O, or chemically pure potable water, which is another high-value resource with many potential benefits being explored.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".