Electrochemical loading enhances deuterium fusion rates in a metal target
Bibliographic record
Abstract
Nuclear fusion research for energy applications aims to create conditions that release more energy than required to initiate the fusion process1. To generate meaningful amounts of energy, fuels such as deuterium need to be spatially confined to increase the collision probability of particles2–4. We therefore set out to investigate whether electrochemically loading a metal lattice with deuterium fuel could increase the probability of nuclear fusion events. Here we report a benchtop fusion reactor that enabled us to bombard a palladium metal target with deuterium ions. These deuterium ions undergo deuterium–deuterium fusion reactions within the palladium metal. We showed that the in situ electrochemical loading of deuterium into the palladium target resulted in a 15(2)% increase in deuterium–deuterium fusion rates. This experiment shows how the electrochemical loading of a metal target at the electronvolt energy scale can affect nuclear reactions at the megaelectronvolt energy scale. A benchtop fusion reactor, called the Thunderbird Reactor, is described, showing that electrochemically loading a metal lattice with deuterium could enhance nuclear fusion rates when that metal is also bombarded by deuterium ions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".