Enhancing the Stability and Performance of Mo-Doped Titanium Suboxide Fuel Cell Catalyst Supports
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cells (PEMFCs) are a near-commercial clean energy technology with potential application in automotive and station power systems. However, the high cost of Pt catalyst and long-term durability of electrode materials still represents barriers towards that hinder market gains for the technology. The catalyst support material greatly influences electrocatalytic activity and durability of the catalytic Pt nanoparticles. Carbon black has been the primary catalyst support in fuel cells over the last 30 years due to its high surface area and electrical conductivity. However, carbon corrosion occurs readily during start-up/shutdown conditions. Therefore, more advanced support materials are need in order to overcome these problems. Metal oxides supports have improved corrosion resistance over carbon black supports, but they typically lack the required electrical conductivity for high performance. Titanium suboxides, specifically those doped with Mo (TOM) and other metals, have been shown to have acceptable electronic conductivity and strong affinity for Pt nanoparticles, to enhance their activity and stability in fuel cells. However, doping with only Mo still creates materials with a sizable band bandgap of 2.6 eV. Furthermore, the long term stability of the Mo dopant is a potential concern. Our group has taken a two-pronged approach to improving TOM materials. The first approach has been to add a second dopant. Specifically, we have a new material doped with both Mo and Si, Ti3O5-Mo-Si (hereafter referred to as TOMS) support. Remarkably, this support has a band gap of only 0.31 eV, approaching the conductivity of a metal. Furthermore, the TOMS material are highly durable and corrosion resistant, showing no signs of dopant loss under extremely corrosive conditions. Our second approach to improving TOM has been to convert it into a nanotube structure (TNTS-Mo) and from there covalently attach a monolayer of a nitrogen-rich terpyridine (tpy) ligand to the surface of the support (hereafter referred to as TPY/TNTS-Mo). The tpy ligand serves as a protective barrier that stabilizes the TNTS-Mo support. Furthermore, the nitrogen groups also have an electronic interaction with Pt catalyst nanoparticles that enhances their stability under harsh operating conditions. In this presentation, we will give an overview of support modification methods as well as detailed discussion of catalyst durability. Figure 1
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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 | 0.001 | 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".