Mitigating Hydrogen Ingress with Thin Cloaking Liquid Barrier Films
Bibliographic record
Abstract
As hydrogen energy systems advance globally, management of hydrogen ingress remains a significant challenge. When hydrogen gas contacts a metal, it dissociates into hydrogen atoms, which subsequently enter and embrittle the metal. This detrimental process not only affects hydrogen pipelines but also metal components in aqueous corrosive environments, such as heat exchanger tubes in geothermal systems. Disrupting hydrogen ingress at the metal interface is therefore crucial to prevent subsequent embrittlement within the metal. Here, we show that cloaking liquid films as thin as 1 nm minimize hydrogen ingress into steel. These films, made from Krytox lubricant, are immiscible with water and preferentially wet steel in aqueous environments. Using a Devanathan–Stachurski electrochemical permeation cell, we show that in mildly acidic electrolytes, cloaking liquid films reduce hydrogen diffusivity in steel by 80% and the subsurface hydrogen concentration, a measure of hydrogen uptake in the steel, is decreased by 86%. X-ray photoelectron spectroscopy confirms the stability of the cloaking barrier film after 25 h of accelerated electrochemical permeation testing. In alkaline environments, similar reductions in hydrogen diffusivity (82%) and subsurface hydrogen concentration (90%) were observed, demonstrating the versatility of these films as barriers to hydrogen ingress. Additionally, we apply these thin cloaking films to enhance the barrier properties of defective zirconia coatings on steel. Although zirconia impedes hydrogen ingress, pinhole defects in the zirconia coating can provide pathways for hydrogen entry. We show that thin cloaking liquid films combined with a 75 nm zirconia coating reduce hydrogen diffusivity in steel by 84%, even when defects are present. Thicker composite coatings of zirconia and lubricant-impregnated microtextured surfaces could further enhance long-term protection against hydrogen ingress in practical applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".