Surfactant-Assisted Electrodeposition of Stable Porous Platinum Structures for the Oxygen Reduction Reaction
Bibliographic record
Abstract
The demands on the alternative energy sector necessitates high quality research and development to produce economical, reliable and environment friendly energy sources. Proton exchange fuel cells (PEFCs) offer a versatile and dependable alternative to conventional energies both in the transportation and stationary power sectors. Their widespread use is, however, still restricted due to their high cost and the limited availability of platinum (Pt) resources. The high cost of PEFCs is attributed in part to the higher loading of Pt based catalyst at the cathode. The reduction of Pt loadings at the cathode is generally accompanied by significant performance losses due to sluggish oxygen reduction reaction (ORR) kinetics. This issue can be resolved by maximizing the Pt utilization without sacrificing the performance. Hence, on-going research and development work is seeking to better utilize Pt with lower loadings along with optimized performance. Compared to their solid or bulk counterparts, porous structures exhibit a high electrochemical active surface area (A ecsa) and, thus, can enhance the Pt utilization. These porous nanostructures can restructure during fuel cell cycling, and structural collapse can lead to an eventual decrease in Pt utilization.1 Stabilizing agents such as surfactants are usually employed during the Pt nanoparticle (NP) synthesis to prevent NP aggregation. These surfactants can also assist in the formation of the mesoporous structure.2 This study describes the electrochemical deposition of a stable porous Pt structure in the presence of surfactants. A site-directed electrodeposition offers advantages to the wet chemical synthesis of Pt nanocatalysts as it ensures that the Pt is deposited onto specific regions of a support that have a sufficient ionic and electrical conductivity. In this study, we demonstrated the use of anionic, cationic, and non-ionic surfactants to produce porous Pt using electrodeposition and the influence of these surfactants in stabilizing the porous structure during the ORR in acidic medium. The conditions for electrodeposition, such as concentration of surfactants and potential for the nucleation and growth stages were each optimized through a series of experiments. These surfactants included cetyl trimethylammonium bromide (CTAB), sodium dodecylsulfate (SDS), and polyethylene glycol octadecyl ether (Brij 78). We utilized scanning electron microscopy techniques to evaluate the porosity of the deposited NPs and their surface coverage. In these experiments, an initial applied pulse was used to induce nucleation of the Pt followed by the growth of these materials with further electrodeposition at a lower potential. These parameters were evaluated for their influence on the final product, such as the porosity, A ecsa, and the surface coverage these materials. The porosity, composition, and crystallinity of these mesoporous particles were confirmed using transmission electron microscopy (TEM) and selected area electron diffraction techniques. These surfactant systems each resulted in the formation of porous Pt. The porous Pt was also subjected to durability testing by cycling the applied potential over a range of oxidizing potentials. Further analysis of these materials by TEM indicated that the mesoporous structure was maintained after the durability tests with a negligible change in their half-wave potential towards the ORR. The durability testing conducted after the removal of surfactants using Soxhlet extractor confirmed the role of the surfactants in helping to stabilize the porous Pt structure.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".