A Proton Selective Carbon Nitride Layer for High Durability Fuel Cells
Bibliographic record
Abstract
Abstract To achieve a balance between performance and durability in electrochemical energy conversion systems, such as fuel cells (FC) and water electrolyzers (WE), proton exchange membranes (PEMs) must be optimized for minimal thickness and resistance while maximizing gas rejection and durability. 2D materials, with Angstrom‐scale pores, hold the potential to revolutionize these devices by enabling highly selective proton transport and mitigating degradation pathways. However, to date no material has been implemented that can prevent gas crossover and extend the device lifetime without compromising initial performance. In this study, it is demonstrated that polytriazine imide (PTI), a 2D graphitic carbon nitride with optimally sized and functionalized lattice pores, facilitates unimpeded proton transport. By engineering a centimeter‐scale monolayer film composed of tessellated PTI nanosheets and placing it at the cathode‐PEM interface, significant gains in performance, efficiency, and durability ‐are achieved. These results in PEMFCs show halved hydrogen crossover and over a threefold increase in lifetime. This approach promises to accelerate the adoption of economically viable FCs and WEs with enhanced output and extended service lifetimes, essential for achieving a decarbonized society.
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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) | 0.000 | 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 | 0.000 | 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".