Commercial Pt/C nanoparticle catalyst enhancement by ionic liquid (IL)/organic ionic plastic crystal (OIPC) modification for oxidation of biofuels in fuel cells
Bibliographic record
Abstract
Due to rising environmental concerns, carbon neutral or carbon negative strategies are being sought along with a wide range of clean energy conversion technologies, such as fuel cells. Fuel cells are being investigated owing to their low emissions, high theoretical efficiency, and simplicity of operation. A significant barrier to fuel cell commercialization is cost, mainly due to the use of expensive noble metals as catalysts, e.g., Pt. One strategy would still rely on Pt as a catalyst material but with electrocatalytic enhancement by OIPC (organic ionic plastic crystal) or IL (ionic liquid) modification which lowers the amount of Pt needed. IL are large organic salts with melting points below 100 °C, while OIPC are chemically similar; however, they have higher melting points with several solid-solid phase transitions below the melt which give rise to their plasticity. Solid catalyst with an ionic liquid layer (SCILL) is the concept of catalyst modification with a thin IL layer and has been demonstrated as a facile approach to improve catalyst activity. IL/OIPC modified 20% mass Pt/C nanoparticles were investigated as an anodic catalyst for proton exchange membrane fuel cells through oxidation of different biofuels such as methanol and formic acid (FA). Herein, three different tetraalkylphosphonium cations were paired with the bis(trifluoromethylsulfonyl)imide (NTf2) anion. Three n-butyl group were employed, while the fourth arm bound to the phosphonium core was varied in length from 4 to 8 carbons. Each gave excellent hydrophobicity as well as thermal and electrochemical stability. Tetrabutylphosphonium bis(trifluoromethylsulfonyl) imide (P4444NTf2) possesses a melting point of 85 °C and a single solid-solid phase transition was observed below this temperature, making it an OIPC, while the other salts were liquids at room temperature. As evidenced through voltammetric analysis, commercial 20% Pt/C catalysts demonstrated enhanced organic fuel oxidation when modified by either IL or OIPC of up to 2.5×. These results are highly significant and mean a reduction in the III amount of Pt needed – an enormous cost savings. IL/OIPC physicochemical characterization will also be discussed.
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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".