Design and fabrication of photoelectrochemical membranes for integrated, solar-driven hydrogen fuel generation
Bibliographic record
Abstract
Arguably the greatest confrontation for humanity and the Arguably the greatest confrontation for humanity and the natural world is addressing the shortfalls of our current energy sources and the threat that intensive consumption has on the environment. By harvesting the immense energy of the sun and converting it into clean fuels such as H2, these issues may be resolved. Inefficiencies of current technology have made the realization of an energy changeover extremely challenging. A viable solution would be an integrated system of the absorbing and conversion components embedded in a membrane. The membrane must house the electrode assembly, block product crossover, and manage ionic and electronic charges generated while remaining passive to the photoelectrochemical process. The first approach to developing such a membrane involves the formation of a composite of the electrically conducting polymer PEDOT and the inorganic acid PMA. It was found that the material possessed excellent electrical conductivity as a function of pH and oxidation state, and stability against overoxidation, while the ionic conductivity remained insufficient. This was combatted with the addition of the proton conductor Nafion®, which was combined in the desired ratio to optimize the material conductivities. Membranes capable of maintaining steady-state pH gradients, with the motivation to operate the oxygen- and hydrogen-generating sides in their optimal pH, were also investigated. It is herein confirmed that these membranes are able to maintain a pH gradient of 14 units indefinitely while adding no additional thermodynamic perturbance to the system. Membranes were constructed by combining ion exchange layers with interchangeable materials in an interfacial layer to develop a photoelectrochemically-adapted membrane. A transparent conducting oxide, conducting polymer, and graphene materials were selected, with the former two exhibiting inadequate activity. However, it was found that graphene oxide demonstrates activity that is comparable or better than commercially available membranes. Its presence also stabilized the membrane. The shortfall of graphene oxide is that it is an insulator. Chemical reduction was used to introduce electrical conductivity by removing the functional groups, which was controlled by the exposure conditions. It is shown that a reduced graphene oxide membrane can meet the figures of merit outlined for these integrated energy systems.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".